TWI684225B - Self directed metrology and pattern classification - Google Patents

Abstract

Methods and systems for determining parameter(s) of a process to be performed on a specimen are provided. One system includes one or more computer subsystems configured for determining an area of a defect detected on a specimen. The computer subsystem(s) are also configured for correlating the area of the defect with information for a design for the specimen and determining a spatial relationship between the area of the defect and the information for the design based on results of the correlating. In addition, the computer subsystem(s) are configured for automatically generating a region of interest to be measured during a process performed for the specimen with a measurement subsystem based on the spatial relationship.

Description

Self-directed measurement and pattern classification

The present invention generally relates to automated pattern measurement site placement and optimization for accurately characterizing pattern morphology including, but not limited to, local critical dimension (CD) changes, line or space width changes, and curvature. Certain embodiments relate to methods and systems for determining one or more parameters of a metrology procedure to be performed on a sample.

The following description and examples are not considered prior art by virtue of their inclusion in this section. Inspection procedures are used at various steps during a semiconductor manufacturing process to detect defects on the wafer to drive higher yields and therefore higher profits in the manufacturing process. Inspection is always an important part of manufacturing semiconductor devices. However, as the size of semiconductor devices decreases, inspection becomes more important for the successful manufacture of acceptable semiconductor devices, because smaller defects can cause device failure. Defect re-inspection usually involves re-inspecting the defects detected by an inspection procedure and using a high magnification optical system or a scanning electron microscope (SEM) to generate additional information about the defects at a higher resolution. Therefore, defect re-inspection is performed at discrete locations on the wafer where defects have been detected by inspection. The higher resolution data of defects generated by defect re-detection are more suitable for determining the attributes of defects (such as outline, roughness, more accurate size information, etc.). Since defect re-inspection is performed for defects detected on the wafer by inspection, the parameters for defect re-inspection at a position of a detected defect can be determined based on the defect attributes determined by the inspection procedure. However, the output acquisition parameters (eg, optical, electron beam, etc. parameters) used for defect re-detection at a location where a defect is detected are generally not determined based on information about the design part in or near the defect location. This information is usually not related to the output acquisition function performed on the detected defects during defect re-examination. Metrology procedures are also used in various steps during a semiconductor manufacturing procedure to monitor and control the procedure. The measurement procedure differs from the inspection procedure in that it is different from the inspection procedure in which defects are detected on a wafer. The measurement procedure is used to measure one or more characteristics of a wafer that cannot be determined using the currently used inspection tool. For example, a metrology program is used to measure one or more characteristics of a wafer (such as the size (eg, line width, thickness, etc.) of a feature formed on the wafer during a process), so that The one or more characteristics determine the effectiveness of the program. In addition, if one or more characteristics of the wafer are unacceptable (eg, outside the predetermined range of one of the (etc.) characteristics), the measurement of one or more characteristics of the wafer can be used to modify the program One or more parameters make the additional wafers manufactured by this procedure have (several) acceptable characteristics. The metrology procedure is also different from the defect re-inspection procedure in that, unlike the defect re-inspection procedure in which the defects detected by the inspection are re-accessed during the defect re-inspection, the metrology procedure can be performed at a location where no defect is detected. In other words, unlike defect re-inspection, the position of performing a metrology procedure on a wafer can be independent of the result of performing an inspection procedure on the wafer. In particular, the location where a measurement procedure is executed can be selected independently of the detection result. In addition, since the measurement position on the wafer can be selected independently of the inspection result, it is different from the position where the defect re-inspection on the wafer cannot be determined until the inspection result of the wafer is generated and can be used for defect re-inspection. The location where the metrology process is performed is determined before an inspection process has been performed on the wafer. The current methods used to implement metrology procedures have several disadvantages. For example, the conventional recipe setting using an SEM pattern measurement (including, for example, critical dimension (CD) and overlay measurement) requires a priori knowledge of the position to be measured. In addition, conventional recipe setting procedures usually include the use of design. In addition, if it is found that the user wishes to measure a new pattern of interest (POI) once or continuously, a measurement tool recipe needs to be updated. Therefore, it would be advantageous to develop a system and method that does not have one or more of the above-mentioned shortcomings and that is used to determine one or more parameters of a metrology procedure that will be performed on a sample.

The following description of various embodiments should not be construed in any way as limiting the subject matter of the patent application of the accompanying invention. An embodiment relates to a system configured to determine one or more parameters of a program to be executed on a sample. The system includes a measurement subsystem including at least one energy source and a detector. The energy source is configured to produce energy directed to a sample. The detector is configured to detect energy from the sample and generate an output in response to the detected energy. The system also includes one or more computer subsystems, which (etc.) are configured to: determine a region of a defect detected on the sample; design information for the region of the defect and one of the samples Correlation; determining a spatial relationship between the area of the defect and the information of the design based on the result of the correlation; and automatically generating the measurement subsystem based on the spatial relationship to be used during a procedure performed on the sample Measure one area of interest (ROI). The system can be further configured as described in this article. Another embodiment relates to a computer-implemented method for determining that one or more parameters of a program will be executed on a sample. The method includes the steps of determining a region, associating, determining a spatial relationship, and automatically generating an ROI described above. The steps of the method are performed by one or more computer systems. Each of the steps of the method described above can be further implemented as described further herein. Additionally, this embodiment of the method described above may include any other step(s) of any other method(s) described herein. Furthermore, the method described above can be performed by any of the systems described herein. Another embodiment relates to a non-transitory computer-readable medium whose storage can be executed on a computer system to execute a program for determining a computer-implemented method of one or more parameters of a program to be executed on a sample instruction. The computer-implemented method includes the steps of the method described above. The computer-readable medium can be further configured as described herein. The steps of the computer-implemented method can be performed as described further herein. In addition, the computer-implemented method (for which the program instructions may be executed) may include any other step(s) of any other method(s) described herein.

As used herein, the terms "design" and "design data" generally refer to the physical design (layout) of an IC and data derived from the physical design through complex simulations or simple geometry and Boolean operations. The physical design can be stored in a data structure, Such as a graphical data stream (GDS) file, Any other standard machine-readable files, Any other suitable files and a design database known in the art. A GDSII file is one of the types of files used for design layout data representation. Other examples of these files include GL1 and OASIS files and proprietary file formats (such as RDF data), It’s exclusive to California, Milpitas, Milpitas, KLA-Tencor. In addition, An image of one reticle and/or its derivative captured by the doubling reticle inspection system can be used as an "agent" or "agents" for the design. This doubled reticle image or one of its derivatives can serve as a replacement for the design layout in any of the embodiments described herein using a design. The design may include the co-owned U.S. Patent No. 7 issued to Zafar et al. on August 4, 2009. 570, No. 796 and the joint US Patent No. 7, issued on March 9, 2010 to Kulkarni and others, 676, Any other design materials or design information agents described in No. 077, The two patents are incorporated herein as if they were cited in full. In addition, Design data can be standard cell library data, Integrated layout data, Design information for one or more layers, Derivatives of design data and complete or partial chip design data. In some examples, Simulated or captured images from a wafer or reticle can be used as an agent for design. Image analysis can also be used as an agent for design analysis. For example, Polygons in a design can be extracted from an image printed on a wafer and/or a reticle, It is assumed that the image of the wafer and/or the reticle is captured with sufficient resolution to fully image the designed polygon. In addition, "Design" and "design data" described herein refer to a design process generated by a semiconductor device designer and can therefore be used well in the embodiments described herein before the design is printed on any physical wafer Information and data. Preferably, The term "design" or "physical design" as used herein refers to a design that would ideally be formed on a wafer. In this way, One of the designs or physical designs described herein will preferably not include features of the design that will not be printed on the wafer (such as optical proximity correction (OPC) features), It is added to the design to enhance the printing of features on the wafer without actually printing its own. In this way, In some embodiments, The design of the sample used in the automatic generation and automatic determination steps described further herein does not include features of the design that will not be printed on the sample. One of the "design" and "design data" described in this article may include data and information related to the physical intent of the device formed on the wafer, These may include any of the various types of designs and design materials described above. A "design" and "design data" may or may alternatively contain data and information related to the electrical intent of the device formed on the wafer. This information and data may include, for example, a wiring diagram and SPICE nomenclature and/or a "comment layout" (for example, The design includes the parameter marking of the electrical wiring comparison table). This data and information can be used to determine which parts of a layout or wafer image are critical in one or more electronic aspects. Refer now to the diagram, It should be noted that The figure is not drawn to scale. In particular, To a large extent, the scale of some components in the drawing is enlarged to emphasize the characteristics of the components. It should also be noted that The figures are not drawn to the same scale. The same element symbols have been used to indicate elements that can be similarly configured and shown in more than one figure. Unless otherwise stated in this article, Otherwise, any elements described and shown may include any suitable commercially available elements. An embodiment relates to a system configured to determine one or more parameters of a metrology procedure to be performed on a sample. In one embodiment, The sample contains a wafer. In another embodiment, The sample contains a double reticle. The wafer and the reticle can include any wafer and reticle known in the art. An embodiment of this system is shown in FIG. 1. The system includes a measurement subsystem, The measurement subsystem includes at least one energy source and a detector. The energy source is configured to produce energy directed to a sample. The detector is configured to detect energy from the sample and produce an output in response to the detected energy. In one embodiment, The energy directed to the sample contains light, And the energy detected from the sample includes light. E.g, In the embodiment of the system shown in Figure 1, The measurement subsystem 10 includes an illumination subsystem configured to direct light to the sample 14. The lighting subsystem includes at least one light source. E.g, As shown in Figure 1, The lighting subsystem includes a light source 16. In one embodiment, The lighting subsystem is configured to direct light to the sample at one or more angles of incidence that may include one or more tilt angles and/or one or more normal angles. E.g, As shown in Figure 1, The light from the light source 16 is guided through the optical element 18 and then through the lens 20 to the beam splitter 21, The beam splitter 21 guides light to the sample 14 at a normal incidence angle. The angle of incidence may include any suitable angle of incidence, It may vary depending on, for example, the characteristics of the sample and the defects to be detected on the sample. The lighting subsystem can be configured to direct light to the sample at different angles of incidence at different times. E.g, The measurement subsystem can be configured to change one or more characteristics of one or more elements of the lighting subsystem, This allows light to be directed to the sample at an incident angle different from that shown in FIG. 1. In one such instance, The measurement subsystem can be configured to move the light source 16, Optical element 18 and lens 20, This allows light to be directed to the sample at a different angle of incidence. In some examples, The measurement subsystem can be configured to direct light to the sample at more than one angle of incidence at the same time. E.g, The lighting subsystem may contain more than one lighting channel, One of these lighting channels may include a light source 16, as shown in FIG. 1, Optical element 18 and lens 20, And the other of these lighting channels (not shown) may include similar elements that may be of different or the same configuration, Or it may include at least one light source and possibly one or more other components (such as components further described herein). If this light is directed to the sample at the same time as the other light, Then one or more characteristics of the light directed to the sample at different incident angles (for example, wavelength, Polarized light, etc.) can be different, This makes it possible to distinguish light from the illumination of the sample at different angles of incidence at the detector(s) from each other. In another example, The lighting subsystem may contain only one light source (eg, Source 16 shown in Figure 1) and the light from the light source can be divided into different optical paths (e.g., by one or more optical elements (not shown) of the illumination subsystem Based on wavelength, Polarized light, etc.). then, The light in each of the different optical paths can be directed to the sample. Multiple lighting channels can be configured to be at the same time or different times (for example, When different illumination channels are used to sequentially illuminate the sample) guide light to the sample. In another example, The same illumination channel can be configured to direct light with different characteristics to the sample at different times. For example, In some examples, The optical element 18 can be configured as a spectral filter and can be in many different ways (for example, By changing the spectral filter, changing the properties of the spectral filter allows different wavelengths of light to be directed to the sample at different times. The lighting subsystem may have any other suitable configuration known in the art for guiding light having different or the same characteristics to the sample sequentially or simultaneously at different or the same angle of incidence. In one embodiment, The light source 16 may include a broadband plasma (BBP) light source. In this way, The light generated by the light source and directed to the sample may include broadband light. however, The light source may include any other suitable light source (such as a laser). The laser can include any suitable laser known in the art and can be configured to produce light of any suitable wavelength or wavelength known in the art. In addition, The laser can be configured to produce monochromatic or near-monochromatic light. In this way, The laser can be a narrow-band laser. The light source may also include a multi-color light source that generates light at multiple discrete wavelengths or wave bands. The light from the optical element 18 can be focused to the beam splitter 21 by the lens 20. Although the lens 20 is shown as a single refractive optical element in FIG. 1, But understand, In practice, The lens 20 may include several refractive and/or reflective optical elements that collectively focus light from the optical elements to the sample. The lighting subsystem shown in FIG. 1 and described herein may include any other suitable optical elements (not shown). Examples of such optical elements include (but are not limited to) (several) polarizing components, (Several) spectral filters, (Several) space filters, (Several) reflective optical elements, (Several) apodizers, (Several) beam splitter, The aperture(s) and the like may include any such suitable optical elements known in the art. In addition, The system may be configured to change one or more elements of the lighting subsystem based on the type of lighting used for metering. The measurement subsystem may also include a scanning subsystem configured to cause light to scan throughout the sample. For example, The measurement subsystem may include a stage 22 on which the sample 14 is placed during measurement. The scanning subsystem may include any suitable mechanical and/or robotic assembly (including stage 22) that may be configured to move the sample so that light can be scanned throughout the sample. Additionally or alternatively, The measurement subsystem may be configured such that one or more optical elements of the measurement subsystem perform a certain scan of light throughout the sample. The light can be scanned throughout the sample in any suitable manner. The measurement subsystem further includes one or more detection channels. At least one of the one or more detection channels includes a detector, The detector is configured to detect light from the sample due to illuminating the sample by the measurement subsystem and generate an output in response to the detected light. For example, The measurement subsystem shown in Figure 1 includes two detection channels, A detection channel consists of a light collector 24, The element 26 and the detector 28 are formed and another detection channel is formed by the light collector 30, The element 32 and the detector 34 are formed. As shown in Figure 1, The two detection channels are configured to collect and detect light at different collection angles. In some examples, One detection channel is configured to detect specularly reflected light and the other detection channel is configured to detect specular reflection not from the sample (eg, scattering, Diffraction, etc.) light. however, Two or more detection channels can be configured to detect the same type of light from the sample (eg, Specular reflection light). Although FIG. 1 shows an embodiment of a measurement subsystem including two detection channels, However, the measurement subsystem may include a different number of detection channels (for example, Only one detection channel or two or more detection channels). Although each collector is shown as a single refractive optical element in FIG. 1, But understand, Each light collector may include one or more refractive optical elements and/or one or more reflective optical elements. The one or more detection channels may include any suitable detector known in the art. For example, The detector can include a photomultiplier tube (PMT), Charge-coupled device (CCD) and time-lapse integration (TDI) cameras. The detector may also include any other suitable detector known in the art. The detector may also include a non-imaging detector or an imaging detector. In this way, If the detector is a non-imaging detector, Each detector can be configured to detect certain characteristics (such as intensity) of scattered light but cannot be configured to detect such characteristics that vary according to the position in the imaging plane. thus, The output generated by each detector included in each detection channel of the measurement system may be a signal or data, It is not an image signal or image data. In these examples, A computer subsystem (such as the computer subsystem 36 of the system) can be configured to generate an image of the sample from the non-imaging output of the detector. however, In other examples, The detector may be configured as an imaging detector configured to generate imaging signals or image data. therefore, The system can be configured to generate the images described herein in several ways. It should be noted that Figure 1 is provided herein to generally depict a configuration of a measurement subsystem that may be included in the system embodiments described herein. Obviously, The measurement subsystem configuration described in this article can be modified to optimize the performance of the system as it is normally performed when designing a commercial measurement system. In addition, Existing metrology systems such as SpectraShape series tools and Archer series tools commercially available from KLA-Tencor (eg, The system described herein is implemented by adding the functionality described herein to an existing metrology system. For some of these systems, The method described in this article can provide optional functionality for the metrology system (eg, In addition to other functionalities of the metering system). Alternatively, The measurement system described in this article can be designed "from scratch" to provide a completely new measurement system. The computer subsystem 36 of the system can be in any suitable way (for example, Via one or more transmission media, The one or more transmission media may include "wired" and/or "wireless" transmission media) a detector coupled to the measurement subsystem so that the computer subsystem can receive the output generated by the detector during the scanning of the sample. The computer subsystem 36 may be configured to use the output of the detector as described herein to perform a number of functions and any other functions described further herein. This computer subsystem can be further configured as described in this article. This computer subsystem (as well as other computer subsystems described herein) may also be referred to herein as (some) computer system. Each of the (several) computer subsystems or systems described in this article can take many forms, Contains a personal computer system, Video computer, Host computer system, workstation, Network equipment, Internet equipment or other devices. Generally speaking, The term "computer system" can be broadly defined to encompass any device that has one or more processors that execute instructions from a memory medium. The computer subsystem or systems may also include any suitable processor known in the art (such as a parallel processor). In addition, (Several) computer subsystems or systems may include a computer platform with high-speed processing and software (as a standalone tool or a network tool). If the system contains more than one computer subsystem, Then different computer subsystems can be coupled to each other, So that images can be sent between these computer subsystems, as described further in this article, data, News, Instructions etc. E.g, Computer subsystem 36 may be coupled to computer subsystem(s) 102 by any suitable transmission medium that may include any suitable wired and/or wireless transmission media known in the art (as shown by the dashed lines in FIG. 1) . Two or more of these computer subsystems can also be effectively coupled by sharing a computer-readable storage medium (not shown). Although the measurement subsystem is described above as an optical or light-based measurement subsystem, However, the measurement subsystem may be an electron beam-based measurement subsystem. E.g, In one embodiment, The energy directed to the sample contains electrons, And the energy detected from the sample contains electrons. In this way, The energy source may be an electron beam source. In this embodiment shown in FIG. 2, The measurement subsystem includes an electronic column 122 coupled to the computer subsystem 124. Also shown in Figure 2, The electron column includes an electron beam source 126 configured to generate electrons focused by one or more elements 130 onto the sample 128. The electron beam source may include, for example, a cathode source or an emitter tip, And one or more elements 130 may include, for example, a shot lens, An anode, A beam limiting aperture, A gate valve, A beam of current selection aperture, An objective lens and a scanning subsystem, All of them may include any such suitable elements known in the art. Electrons returned from the sample (for example, Secondary electrons) can be focused to the detector 134 by one or more elements 132. One or more elements 132 may include, for example, a scanning subsystem, The scanning subsystem may be the same scanning subsystem contained in the element(s) 130. The electron column may contain any other suitable components known in the art. In addition, May be as No. 8 of US Patent issued to Jiang et al. on April 4, 2014 664, No. 594, No. 8 of U.S. Patent issued to Kojima et al. on April 8, 2014 692, No. 204, US Patent No. 8 issued on April 15, 2014 to Gubbens and others, 698, No. 093 and US Patent No. 8 issued to MacDonald et al. on May 6, 2014 716, Further configure the electronic column as described in No. 662, These patents are incorporated herein as if cited in full text. Although the electron column is shown in FIG. 2 as configured such that electrons are directed to the sample at an oblique incidence angle and scattered from the sample at another oblique angle, But understand, The electron beam can be directed to and scattered from the sample at any suitable angle. In addition, The electron beam-based measurement subsystem can be configured to use multiple modes to generate the image of the sample (eg, Use different lighting angles, Collect corners, etc.). The multiple modes of the measurement subsystem based on the electron beam may differ in any image generation parameters of the measurement subsystem. As described above, The computer subsystem 124 may be coupled to the detector 134. The detector can detect the electrons returned from the surface of the sample, This forms an electron beam image of the sample. The electron beam images may include any suitable electron beam images. The computer subsystem 124 may be configured to use the output of the detector and/or the electron beam image to perform any of the functions described herein. The computer subsystem 124 may be configured to perform any additional step(s) described herein. A system including the measurement subsystem shown in FIG. 2 can be further configured as described herein. note, 2 is provided herein to generally illustrate a configuration of an electron beam-based measurement subsystem that may be included in the embodiments described herein. Like the optical measurement subsystem described above, The configuration of the electron beam-based measurement subsystem described in this article can be modified to optimize the performance of the measurement subsystem as it is normally performed when designing a commercial measurement system. In addition, An existing metrology or high-resolution defect re-inspection system such as one of the tools commercially available from KLA-Tencor's eDR-xxxx series (eg, The system described herein is implemented by adding the functionality described herein to an existing metrology system. For some of these systems, The methods described in this article can provide optional functionality for the system (eg, In addition to other functionalities of the system). Alternatively, The system described in this article can be designed "from scratch" to provide a completely new system. Although the measurement subsystem is described above as a measurement subsystem based on light or electron beam, However, the measurement subsystem may be an ion beam-based measurement subsystem. This measurement subsystem can be configured as shown in Figure 2, Except that any suitable ion beam source known in the art can be used in place of the electron beam source. In addition, The measurement subsystem can be any other measurement subsystem suitable for ion beam-based measurement, Such as those contained in commercially available focused ion beam (FIB) systems, Measurement subsystem in helium ion microscope (HIM) system and secondary ion mass spectrometer (SIMS) system. One or more computer subsystems included in the system embodiments described herein are configured to automatically generate concerns to be measured using the measurement subsystem during a measurement procedure performed on the sample based on a sample design Region (ROI). Because the ROI is determined based on the design of the sample, Therefore, ROI can be called "design-based ROI". In addition, The metrology procedure (for which one or more parameters are determined as described herein) can be referred to as a "design-driven metrology procedure". Figure 3 provides some context for the various terms (including ROI) used in this article. For example, FIG. 3 shows a field of view (FOV) 300 of a measurement subsystem centered on the measurement site 302 (such as one of the measurement subsystems described herein). The measurement site may be a site of a detected defect (detected by inspection and/or re-detection) or a sample site. Each FOV position on the wafer during a metrology process can be associated with only one of the measurement sites for which the metrology process will be performed. For example, During a measurement procedure, A scanning electron microscope (SEM) or other measurement subsystem can be driven from the measurement site to the measurement site. As also shown in Figure 3, Within FOV 300, Can locate multiple ROI 304, 306 and 308. Although three ROIs are shown in Figure 3, But any number of ROIs can exist in any FOV (ie, One or more ROI). As further shown in Figure 3, ROI can be located in various positions within FOV, And although the three ROIs are shown as not overlapping in FOV, But in some cases, ROI may overlap to some extent in FOV. Within each of the ROI, You can choose to perform at least one measurement, This can be automatically selected or determined as described further herein. Although FIG. 3 does not show any patterned features that will be formed in the area of the wafer positioned in the FOV shown in FIG. 3, However, the measurement will generally target one or more characteristics of the patterned feature. To show the different measurements that can be performed in different ROIs, Figure 3 abstractly depicts these different measurements as a double-headed arrow showing the size range and direction, These measurements can be performed across this dimension. For example, As shown in Figure 3, Measurement 310 may be performed in ROI 304 in one direction across only a portion of the entire size of one of the ROIs in that direction. Measurement 312 may be performed in ROI 306 across the entire size of one of the ROIs in that direction in a different direction. In addition, Measurements 314 and 316 can be performed across the ROI 308 in the vertical direction. Measurement 314 may be performed across only a part of the entire size of one of the measurement directions in the ROI, The measurement 316 can be performed across an entire size of the measurement direction in the ROI. therefore, As described further in this article, Different measurements can be performed in different ROIs, And the measurements performed in either ROI can be selected or determined as described further herein. One or more computer subsystems are also configured to automatically determine the first and One or more measurements of one or more parameters are performed in the second subset. One or more parameters of one or more measurements performed in the first subset are determined individually and independently of one or more parameters of one or more measurements performed in the second subset. In other words, One or more parameters can be determined for the first subset of ROI based on the portion of the design located only in the first subset, One or more parameters may be determined for the second subset of ROI based on the portion of the design located only in the second subset, and so on. In addition, Although some embodiments are described herein with respect to the first and second subsets, But understand, More than two subsets of ROI (eg, Two or more subsets of ROI) perform the step(s) performed by the computer subsystem(s). In addition, Each of the subset of ROIs may contain one or more ROIs. For example, The first subset of ROI can contain only one ROI, The second subset of ROI may include more than one ROI. In this way, The embodiments described herein are configured for automated pattern fidelity measurement plan generation. The embodiments described herein may also be configured to execute the generated pattern fidelity measurement plan. In one embodiment, Automatic generation and automatic determination are performed during the setting of the measurement program. In this way, Methods can include automatic ROI generation using the physical design of the wafer during setup. In addition, It can make the formula setting of the fidelity measurement of drawings completely automatic, This is because the ROI of thousands of unique sites can be automatically generated during the setup. In another embodiment, Automatic generation and automatic determination are executed immediately during the running time of the measurement program. In this way, The embodiments described herein can be configured for automated real-time pattern fidelity measurement plan generation. In addition, The method may include automatic ROI generation using the physical design of the wafer during runtime. The embodiments described herein can also generate a metrology measurement plan without prior knowledge of the structure to be measured. For example, The embodiments described herein do not need to use another system or method to perform functions on the information generated by the structure to be measured. therefore, The embodiments described herein provide several advantages over currently used methods and systems for measurement plan generation. For example, In the new program node, The deviation of the pattern detected by the inspection tool will require quantitative analysis to determine whether it satisfies the criterion of "defect". We cannot predict in advance where these defect candidates may appear, therefore, Need immediate automated measurement plan generation. In some embodiments, Automatically generate rule-based searches that include designs executed during the setup of the measurement program. For example, It can make the formula setting of the fidelity measurement of drawings completely automatic, This is because one of the physical designs of the wafer can be used during setup to automatically generate ROI for thousands of unique sites based on rule search. In this way, The embodiments described herein may be configured for automatic rule-based ROI generation. Applying the rules generated by ROI to a design can be implemented in several different ways. For example, A rule-based method may be a non-image processing method, The rules are applied to the design data to generate ROI. CAD software can be used to execute these applications. In another example, A method based on image processing can be used, It may include presenting the design data as an image and then using rules as input to generate ROI using image processing algorithms. In this way, Design data can be generated by design analysis software and/or algorithms for various types of consumption in order to use rules as input to generate ROI. In an embodiment of a rule-based search for automatically generating ROI, A rule can be generated for each measurement type. In other words, Rule 1 can be used for measurement type 1, Rule 2 can be used for measurement type 2 and so on. In addition, Each rule cannot be used for more than one measurement type. In this way, Each rule can define the characteristics of the pattern in the design to be formed on the wafer, These characteristics will make a measurement of one of the other measurement types applicable to the drawing. For example, A rule for a line width measurement type can be designed to identify a pattern or a portion of a pattern that has a substantially uniform size across a relatively large section of the pattern as a candidate for the line width measurement type. In some of these examples, The rules can be enforced for any one and/or owner of the patterns included in any one FOV. therefore, All rules can be executed on the basis of each FOV. Since each rule can identify the possible location of the type of measurement that the rule writes to, Therefore, each rule can identify several possible ROIs of the FOV, Each potential position of one measurement type corresponds to one of the ROIs. therefore, The result of applying each rule to each FOV may include one or more ROI positions in the FOV. thus, Applying multiple rules to each FOV can generate one or more ROI positions in each FOV, Some such positions may correspond to different measurement types. In some of these examples, Each of the ROI positions within the FOV can correspond to only one measurement of only one type. however, Multiple ROI positions within a FOV may overlap (partially or completely) with each other within the FOV (eg, It is possible when it is suitable to perform two different measurements of two different types in the same part of the FOV. In these examples, Each individual ROI of the overlapping ROI may correspond to only one measurement of only one type. In other words, There can be only one measurement type per ROI. therefore, To perform multiple measurements for a given ROI position, Can generate multiple ROIs, Where each ROI has the same ROI limit (or position, Coordinates, etc.) but each has a different type of measurement. all in all, therefore, For any measurement location on a wafer, A FOV can be specified for this measurement location. All rules can be run for each FOV. Since all rules are run, One or more ROIs of each rule of each FOV can be generated by one measurement of each ROI. The same steps can be repeated for each FOV/measurement site, Until all FOV/measurement sites have been processed. In one embodiment, One or more computer subsystems include a computer subsystem of an electronic design automation (EDA) tool. For example, For ROI generation at runtime, The method can use EDA solid design analysis tools or apply custom algorithm to solid design. In some of these examples, A design clip or another representation of the design can be automatically analyzed by physical design analysis software to determine the effective measurement within the design thumbnail or another representation of the design. In one such instance, For ROI generation at runtime, An algorithm can be based on whether a given segment of a pattern is straight/parallel (ie, (Two edges of a structure/pattern are parallel to each other), Bending (for example, The design is automatically segmented at a corner) or at the end of a line. The EDA tool may include any suitable commercially available EDA tool. In some such embodiments, One or more computer subsystems described in this article (for example, (Several) computer subsystem 102) can be configured as an EDA tool. In another embodiment, The automatic determination of one or more parameters for the first subset of ROI results in the execution of a first type(s) of measurements in the first subset of ROI, Automatically determining one or more parameters for the second subset of ROI results in performing a second type(s) of measurements in the second subset of ROI, And the measurements of the first and second types are different from each other. In this way, The method may include automatically determining the type of measurement during the ROI generation process. There can be one type of measurement for each ROI and can be automatically determined during the ROI generation process. thus, The embodiments described herein can be configured to automatically generate metrology plans with appropriate measurement types for each ROI. For example, Measurement plan generation may include automatic definition of ROI and measurement types for each FOV from physical design. The automatic definition of ROI and measurement types can be performed using design analysis algorithms and software. One or more parameters may also be included in the ROI where the type of measurement is to be performed. The location of the type of measurement to be performed in the ROI can be determined as described further herein. The metrology procedure described in this article can be performed to determine the difference between the pattern on a wafer and the pattern in the design. In particular, When the design is designed to be printed on a wafer, They are almost never printed exactly on the wafer as they were designed. Such differences between the designed pattern and the printed pattern can be attributed to the process used to print the pattern on the wafer, The inherent limitations of tools and materials and these procedures, Any errors in tools and materials. An example of how the pattern printed on a wafer may differ from the pattern as designed is shown in FIGS. 4 and 5. In particular, As shown in Figure 4, A design part 400 of a wafer (not shown in FIG. 4) may include three different patterns 402, 404 and 406. The pattern 402 may be an example of a line structure included in a design of a wafer. The pattern 404 can be included in an example of a contact structure in a design of a wafer, And the pattern 406 may be included in an example of a polygonal structure in a design of a wafer. Although some examples of structures included in a design of a wafer are shown in FIG. 4 (and other figures described herein), However, these examples are not intended to represent any particular design of any particular wafer. Instead, As the general technician will understand, The design of the wafer may include many different types of structures in many different configurations and in many different numbers. The structure shown in FIG. 4 (and other figures described herein) is only intended to illustrate some hypothetical wafer structures to further understand the various embodiments described herein. Attributable to the tools used to print the structure shown in the part 400, Inherent limitations of materials and procedures, These structures are not necessarily printed on the wafer when they are included in the design. For example, As shown in Figure 5, Replace the pattern 402 in the part 400 with a sharp 90-degree corner as shown in the design, 404 and 406, The pattern will have at least some rounded corners. In addition, Any of the structures may have a variation in size (such as the width at various points across the structure). For example, As shown in Figure 5, Compared to the design characteristics of the structure at multiple points across this structure, The pattern 406 has some line width variations. Therefore, the ROI and the measurement type of each ROI can be automatically selected as described herein based on the characteristics of the designed pattern (which may be combined with some prior knowledge about potential problems with the pattern). Several possible ROIs for the pattern shown in FIG. 5 are shown in FIG. 6. Although these possible ROIs are shown regarding the pattern shown in Figure 5, However, the ROI may actually be based on the design corresponding to the pattern shown in FIG. 5 (ie, Based on the pattern shown in Figure 4). In the embodiment shown in FIG. 6, The ROI 600 can be determined for the portion of the feature that is designed to have a substantially uniform size across the feature 602 and 604. For example, ROI 600 can be generated for a portion of the portion that is designed to have a substantially uniform size across feature 402, And ROIs 602 and 604 can be generated for those portions that are designed to have a substantially uniform size across features 406. The measurement type automatically selected for these ROIs can be a line width measurement, It can be used to detect necking or swelling in patterned features. Another ROI (ROI 606) can be automatically generated for a space between the two of these features (features 402 and 406), The space is designed to have substantially the same size across the ROI. The measurement type automatically selected for this ROI by the embodiments described herein may include a gap measurement (or a distance between two features or some statistical measure of distance). Gap measurement can be performed to detect bridging problems between two patterned features. The embodiments described herein may also be configured to automatically generate several ROIs at and/or near the end of one or more features. For example, As shown in Figure 6, ROI 608 and 610 can be automatically generated for the end of feature 402, Instead, ROIs 612 and 614 can be automatically generated for the end of feature 406. The measurement type selected for these ROIs can be the end of the line, The wire end is pulled back, Line end distance (for example, The distance between the two ends of a straight line) may be used to describe some other type of measurement of the relative position of the feature as designed to the end of the feature as printed. One or more ROIs can also be automatically generated for the corners of one or more patterned features in the design. For example, As shown in Figure 6, ROIs 616 and 618 may be generated for the corner of feature 406. The type of measurement selected for these ROIs can be curvature, radius, distance, The arc area may be some other type of measurement that can be used to describe the shape of the corner. Another ROI can be automatically generated for the contact patterned features in the design by the embodiments described herein. For example, As shown in Figure 6, ROI 620 may be generated for junction feature 404. The measurement type selected for this ROI can be diameter, width, height, radius, The area may be used to describe how the printed contact is different from another measurement type as the designed contact. Other measurement types that can be determined for a measurement procedure include tip-to-tip (one of the gaps between two wire ends), Tip-line (one of the gaps between the end of the line and the line is measured), Line length (measured as one of the length of a straight line) and corner to corner measurement. therefore, As described above, The embodiments described herein may be configured to perform design-based segmentation of at least a portion of a design of a wafer into an ROI for a metrology process. In addition, Some segments can include straight segments, Straight gap section, Line end, Corner segment and contact segment. The different segments and corresponding ROIs can be determined in the design in several different ways as described herein. For example, The segment or ROI can be determined by applying one or more rules to the design. In another example, It can be identified as passing through the hypothetical centerline of the patterned features in the design as described further herein (which is hypothetical in the sense that it is not part of the design or is not printed on the wafer), And then, These centerlines can be used to segment the patterned features into segments and/or ROIs. For example, The straight centerline passing through one of the patterned features can be used to identify the portion of the patterned feature through which the straight centerline extends as a straight line segment. In another example, The straight centerline passing through one of the spaces between the two patterned features can be used to identify the portion of the space through which the straight centerline extends as a straight gap segment. In an additional instance, A part of the patterned features where two straight lines meet at a 90-degree angle can be identified as a corner segment. An imaginary centerline can be used to identify other segments described herein in a similar manner. Once the various positions used in the metrology procedure have been determined (for example, Measuring the position of the site, Align the location of the site, Autofocus position, etc.), The metering recipe setting can include various additional steps, A physical wafer may be used to perform some of these additional steps on the metrology tool. For example, One or more positions can be located in one of the FOV of the measurement subsystem. Once one or more positions are located in the FOV of the measurement subsystem, You can use the measurement subsystem parameters (for example, Optics, The different values of electron beam or imaging parameters) produce the output of the measurement subsystem. then, The different outputs generated using different values of the parameters can be compared to determine which parameters are most suitable for the measurement procedure for one or more locations. In addition, Different measurement subsystem parameters can be selected for different locations to be measured in the same measurement program. For example, A set of measurement subsystem parameters that can be determined to be the best (and therefore selected) for a measurement type in a type of ROI, At the same time, it is possible to determine the best (and therefore selected) another different set of measurement subsystem parameters for another different measurement type in another different type of ROI. In a similar way, It can be determined on a position-by-position type basis that the computer subsystem(s) is applied to one or more methods and/or parameters of the output generated by the measurement subsystem /Or algorithms and/or different parameters of the same method(s) and/or algorithm(s) can be applied to the output generated at different types of locations on the wafer). In some embodiments, The computer subsystem(s) can be configured to determine the position on the sample during the metrology process by aligning the output of the detector with the design of the samples of the first and second subsets of the ROI. For example, The computer subsystem can be configured for automatic SEM to design fine alignment (eg, Use the geometry in FOV of SEM). Since global alignment does not ensure the alignment of the center line of the structure in the image generated by a measurement subsystem with the design structure, Therefore SEM to design fine alignment can be performed. In some embodiments where the output of the measurement subsystem is aligned with the design, The imaginary center line drawn through the output and patterned features in the design can be used for fine alignment (while the alignment marks described further herein can be used for global alignment of a wafer or one or more FOVs). 7 and 8 illustrate some problems that can occur when using the edges of the features in the output and design for alignment. For example, As shown in Figure 7, A part of a design may include two features (line 700 and polygon 702). In addition, A portion of the output generated by the measurement subsystem corresponding to the portion of the design may include output for two features (line 704 and polygon 706). The features in the design and the output of the measurement subsystem appear to be different due to printing the design on the wafer as described further above. The output of the measurement subsystem can be aligned using the edge-to-edge method at the upper or lower edge of a pattern of interest (eg, One SEM image) and one design. For example, As shown in Figure 7, If the lower edge 708 of the horizontal portion of polygons 702 and 706 is used for alignment, Then the line end measurement performed on the polygon 706 in the polygonal areas 710 and 712 will generate a measurement. however, As shown in Figure 8, If the upper edge 800 of the horizontal portion of polygons 702 and 706 is used for alignment, The line end measurement performed on the polygon 706 in the polygonal regions 710 and 712 will produce a different measurement. In this way, Depends on which edge of the polygon is used for design-to-output alignment, Line end measurement will produce different results, This is due to several obvious reasons (for example, The wire end pullback measurement system is inconsistent) and is unfavorable. therefore, Instead of using edge-to-edge alignment, The embodiments described herein may use the center of the features in the output and in the design to perform the alignment of the measurement subsystem output to the design. For example, As shown in Figure 9, If the centers of polygons 702 and 706 are used for alignment, Then one of the measurements different from the case of using any of the edge alignment methods described above will be generated for the line end measurement performed on the polygon 706 in the polygonal regions 710 and 712. however, Using the center of the feature to align the output of the measurement subsystem with the design will produce a more consistent alignment from ROI to one of the ROI, This provides substantially consistent measurements of ROI (eg, Corner measurement, Line end pull back measurement and width measurement). Alignment using the center of the feature instead of its edges can also improve the alignment robustness for severely distorted patterns and when the FOV does not have many features for aligning the pattern of interest. 10 to 12 illustrate how the center of the patterned features in a part of a design and in the output of the measurement subsystem can be used to align the design and the output. For example, As shown in Figure 10, A part of the design of a sample can contain four different features (line 1000, 1002 and 1004 parts and polygon 1006). As further shown in Figure 10, An imaginary centerline can be determined by the entirety of each feature part included in the design part. For example, Available for line 1000, 1002 and 1004 determine the imaginary centerline 1008, 1010 and 1012. In addition, The virtual center line 1014 can be determined for the polygon 1006. The hypothetical centerline can be determined in any suitable way. The hypothetical centerline can also be determined for patterned features as they appear in the output of the measurement subsystem. For example, As shown in Figure 11, A part of a design in the output of the measurement subsystem may include four different features corresponding to the parts shown in FIG. 10 (eg, Line 1100, 1102 and 1104 parts and polygons 1106). As further shown in Figure 11, An imaginary centerline can be determined by the entirety of the features included in this part of the design. For example, Available for line 1100, 1102 and 1104 determine the imaginary centerline 1108, 1110 and 1112. In addition, The virtual center line 1114 can be determined for the polygon 1106. The hypothetical centerline can be determined as described further herein. Since the centerline of the patterned features in the design can be reproducibly determined and since the centerline of the patterned features in the output should be substantially reproducible, Therefore, the imaginary centerline can be used to relatively reproducibly align the patterned features in the design and the patterned features in the output. For example, As shown in Figure 12, The alignment 1200 of the center lines 1008 and 1108 can be used to reproducibly align the line 1000 in the design and the line 1100 in the output. In another example, The alignment 1202 of the center lines 1010 and 1110 can be used to reproducibly align the line 1002 in the design and the line 1102 in the output. In addition, The alignment 1204 of the center lines 1012 and 1112 can be used to reproducibly align the line 1004 in the design and the line 1104 in the output. In addition, The alignment 1206 of the centerlines 1014 and 1114 can be used to reproducibly align the polygon 1006 in the design and the polygon 1106 in the output. of course, To align the features in one part of the design with the features in the same part of the design in the output of the measurement subsystem, It is not necessary to align all the centerlines of all the features in this part to each other in order to produce the alignment of all the features. For example, In the example shown in Figure 12, The alignment of the centerline of the polygon in the design and the centerline of the polygon in the output can be used to produce a fine-to-output alignment for the polygon and the remaining features in this part of the design. Reproducibly being able to align features in the design with features in the output of the measurement subsystem will improve the consistency of measurements performed using the results of the alignment. In a further embodiment, The parameter(s) of the measurement(s) include the boundary of one or more dimensions across which the measurement(s) are performed. For example, (Several) computer subsystems can be configured for automatic generation of measurement limits. The measurement limits can be automatically determined for each unique location during runtime (no parameters required during setup). In some embodiments, The centerline described further herein can be used to determine the boundary across which dimensions of the measurement are performed equally. For example, As shown in Figure 13, A part of a design formed on a wafer may include four patterned features 1300, 1302, 1304 and 1306, They are shown in FIG. 13 as they can be formed on the wafer and then imaged by the measurement subsystem. An imaginary centerline 1308 can be generated for each of the features described further herein, 1310, 1312 and 1314. An imaginary centerline can also be generated for the space between the patterned features. The centerline for these spaces can be defined by the midpoint between two adjacent features in the design. For example, Based on feature 1300 and any other neighboring features (eg, The midpoint between the centerlines of features 1302) defines the centerline 1316. The centerline 1318 may be defined based on the midpoint between the centerline of the feature 1302 and any other adjacent features (not shown in FIG. 13) on the left side of this feature and extending beyond the feature 1300. Based on feature 1304 and any other neighboring features (eg, The center line 1320 is defined by the midpoint between the center lines of features 1302 and 1306). The centerline 1322 may be defined based on the midpoint between the centerlines of the features 1302 and 1306. In addition, The centerline 1324 may be defined based on the midpoint between the centerline of the feature 1306 and any neighboring features on the right side of this feature (not shown in FIG. 13). Although the centerline depicted in FIG. 13 is described with respect to the patterned features as appearing in the output of the measurement subsystem, But the centerline can also or alternatively be defined based on the patterned features as appearing in the design itself. In addition, Although the centerline in the space between the patterned features was described above as being defined based on the centerline in the patterned features, However, the centerline in these spaces may be based on some other characteristics of the patterned features (for example, Defined by the edge of the patterned feature). The centerline in the space between the patterned features can then be used as a boundary for any measurement of the patterned features for execution. For example, As shown in Figure 13, If the critical dimension (CD) of one of the patterned features will be measured for the patterned feature 1304, Then one of the lines 1326 can be moved from the position of the centerline 1320 on one side of the patterned feature to the position of the centerline 1320 on the other side of the patterned feature and is substantially perpendicular to the patterned feature The measurement is performed in one direction of the center line 1312 within 1304. In this way, Measurements can be performed in a direction orthogonal to the centerline through the patterned feature. Although the three lines 1326 are shown in FIG. 13 as representing the dimensions across which they can perform different measurements on the patterned features 1304, However, any suitable number of such measurements can be performed at any suitable location along the centerline within the patterned feature. In addition, Measurements can be performed in a direction that is substantially parallel to the centerline of the features. For example, As shown in Figure 13, Measurements can be performed along one of the lines 1328, And although not shown in Figure 13, But the boundary of these measurements can also be determined by the centerline in the space between the patterned features, As described further herein. In addition, Although not shown in Figure 13, However, the size across which measurements are performed may intersect the patterned features and/or the centerline of the space between the patterned features at an angle other than orthogonal (e.g., For measuring radius, For wire end pullback measurement, For line end distance measurement, etc.). Using the center line in the space between the patterned features as the boundary of any measurement performed on the patterned features can advantageously ensure that the measurement starts and ends outside the patterned features, This ensures that the measurement is performed across the entire size of one of the patterned features and that the boundary of the measurement is sufficiently outside the patterned feature so that it can determine the warp patterns in the output generated during the measurement with sufficient accuracy and/or confidence The edge of the feature. E.g, If the boundary where a measurement starts is too close to the edge of a patterned feature, The location of the edges of the patterned features in the output can be easily confused with the measurement boundary and/or can be lost in the measurement boundary noise. however, Using the center line in the space between the patterned features to determine the measured boundary as described herein will substantially eliminate any such errors in edge detection of patterned features. In a similar way, If the measurement described in this article will be performed for a space between two patterned features (eg, To measure the gap between two features), The measured boundary can then be determined based on the centerline within the patterned features surrounding the space. In this way, The measurement can start and end at a position that sufficiently exceeds the edge of the space, This ensures that the measurement is performed across the entire size of the space and that the edge of the space can be determined with relatively high accuracy and/or confidence. In one embodiment, The (equal) measurement includes automatically determining the position in the output generated by the detector during the (equal) measurement of one or more edges of one or more structures formed on the sample. In this way, The embodiments described herein can be configured for automatic determination of SEM edge positions. In some examples, The edge position can be determined using the ID gradient curve further described herein. For example, The edge position can be automatically determined by finding the strongest positive or negative gradient peak within a 1D gradient quantitative curve. In other words, The peak point in the 1D gradient curve can be selected as the edge position. One of the features CD or other attributes can then be determined based on the edge position. For example, By using the positive/negative gradient peaks of the 1D gradient curve that is orthogonal to a line drawn through the center of the structure, Zero crossing or negative/positive gradient peak positioning top, Determine the top, bottom or middle edge position Middle or bottom CD. however, In addition to using the gradient curve, Other measurement algorithms can also be used to locate edges. In another embodiment, The computer subsystem(s) is configured for automatically generating one or more attributes for one of the first and second subsets of ROI based on the results of the measurement(s). In this way, The embodiments described herein may be configured for automatically generating measurement statistics and attributes for each ROI. The measurement statistics of each ROI can be determined independently of the measurement results of each other ROI. Multiple measurements of a ROI can be used to generate various measurement statistics (eg, Maximum (Max), Min (Min), Mean (Mean), Average, Median (Median), Standard Deviation, Range (Range) and the sum (Sum)). In another example, The computer subsystem(s) can be configured to automatically generate other attributes, Such as a one-dimensional (1D) gray scale curve of a patterned structure formed on a wafer. The 1D gray-scale quantification curve can be automatically generated by output along a line orthogonal to a line passing through a center line of the patterned structure or parallel to a line passing through the center line of the patterned structure. The computer subsystem(s) can also be configured to automatically generate a 1D gradient curve, The 1D gradient quantity curve can be automatically generated by taking a gradient of a 1D grayscale quantity curve determined as described above. In some examples, Multiple measurements within an ROI can include one measurement for each 1D grayscale or gradient curve. Measurement statistics can be about actual CD, Positive △CD and negative △CD, △CD provides CD measurement relative to design. In addition, Various types of gray-scale or gradient-based attributes (such as peak local gray-scale difference, Peak positive or negative gradient, etc.). The measurement statistics and/or attributes that can be determined using the embodiments described herein are also not limited to those described herein. In an additional embodiment, One or more computer subsystems are configured for automatically generating one or more instances of ROI for one of the first subset and the second subset based on the results of one or more measurements Multiple attributes, And compare at least one of one or more attributes for two or more of multiple instances to identify outliers in two or more of multiple instances. In this way, The embodiments described herein may be configured for relative comparison of measurement statistics and attributes across various locations on a wafer to determine outliers. The measurement statistics and attributes of each of the ROIs can be compared across various locations on a wafer to determine outliers for defect detection. In a further embodiment, One or more computer subsystems are configured for automatic selection of one or more alignment sites in the design, And the measurement procedure includes determining one or more positions of at least one of one or more alignment sites on the sample during the measurement procedure, And one or more positions of one or more ROIs in the first subset and the second subset on the sample are determined based on one or more positions of at least one alignment site on the sample. For example, The embodiments described herein may be configured for automatically generating alignment sites (for coarse alignment) using physical design analysis. In this example, During the generation of the measurement plan, For each FOV, The computer subsystem(s) can be configured to use physical design to automatically determine the unique alignment site(s) and automatic focus site(s) for each measurement site. Design analysis algorithms and software can be used to perform automatic determination of (several) unique alignment sites and (several) autofocus sites. In some embodiments, The system described herein can be configured to execute a measurement plan for each FOV on a measurement tool that includes at least one of a measurement subsystem and a computer subsystem. In one such embodiment, The system can perform auto-focusing of each FOV and then align the anchor point of each FOV. In some of these examples, The system can extract abbreviated forms of design for anchor points and measurement points from a design database for automatic focusing and/or anchor point alignment. The system can be further configured for the alignment of the measurement site of each FOV and execute the measurement plan for the measurement site, The selected type of measurement is performed, such as in the ROI(s) within the FOV. The computer subsystem(s) can then generate measurement data for each ROI. In some embodiments, The metrology procedure includes determining whether there is a defect in the one ROI based only on one or more measurements performed in one of the ROIs in the first subset and the second subset. In other words, Defect detection in an ROI may not be based on the output generated in any other ROI (in the same die as the ROI or in a die different from the die where the ROI is located) or any amount produced using this output Measurement. For example, Comparable using only the output generated in a ROI to produce a measurement result and a threshold for the ROI, And any measurement result higher than the threshold can be judged as a defect, However, any measurement result lower than the threshold value may not be determined as a defect (or vice versa). In addition, More than one threshold can be used (for example, Upper threshold and lower threshold) and/or any other suitable defect detection methods and/or algorithms to perform such defect detection. In this way, The metrology procedure determined to be used for one or more of its parameters may include ROI-based single die defect detection. This defect detection can be performed to generate various types of attributes at the ROI position (for example, CD measurement, Gradient magnitude, Local gray-scale contrast, etc.) to detect various types of defects (for example, Pattern defects, Missing and/or underfilled epitaxial layer, Silicon germanium (SiGe) defects, etc.). Compared with the embodiments described herein, The currently used method for ROI-based single die defect detection uses a reference image or reference contour (acquired or generated) for defect detection. Compared with ROI-based single die defect detection, The acquired image method has half the output. The generated image or outline method suffers from the complexity and inaccuracy of reference generation. In one embodiment, Performing one or more measurements in one of the first and second subsets of ROI includes a CD measurement of the CD measurement of one of the ROI relative to the other of the ROI. In this way, Measurements that determine one or more parameters for them can be relative CD measurements, Among them, CDs of multiple instances of a given pattern of interest (POI) on a given wafer can be compared. In other words, CD measurement can be a relative measurement rather than an absolute measurement. Compared with the embodiments described herein, The currently used method for relative CD measurement uses a CD-SEM tool, The recipe setting that defines multiple ROIs for each site is a very laborious and time-consuming procedure, Therefore, for a CD measurement, a substantially limited number of ROIs per site and a limited number of unique sites per die can be measured. In an additional embodiment, Performing one or more measurements in one of the first subset and the second subset of ROI includes an overlay measurement of one of the ROI relative to the other of the ROI. In this way, It is determined that the measurement used for one or more of its parameters can be a relative stack measurement. In other words, Overlap measurement can be a relative measurement, Not an absolute measurement. Available in multiple patterned manufacturing processes (for example, Double patterning, Triple patterning or quad patterning), The spacer pitch is divided into manufacturing procedures and the like to measure the stacking error. In addition, The overlay error between a current layer formed on the wafer and a previous layer formed on the wafer can be measured. Compared with the embodiments described herein, The currently used method for relative stack measurement uses a CD-SEM tool, The recipe setting for defining multiple ROIs at each site is a very laborious and time-consuming procedure and therefore a substantially limited number of ROIs and each die for each site can be measured for overlapping measurements A limited number of unique sites. In some embodiments, The sample contains a program window verification (PWQ) wafer, And the automatically generating includes automatically generating the ROI to be measured during the metrology process based on the design and the result of performing one of the inspection procedures on the sample. In this way, Determination that the measurement used for one or more of its parameters may include automated re-inspection of pattern defects on the PWQ wafer (eg, Use CD measurement), These pattern defects can be detected by PWQ inspection of one of the wafers performed by an inspection tool, such as one of the inspection tools commercially available from KLA-Tencor. In some examples, Defects detected by PWQ inspection can be used as hot spots for measurement, And the measurement and detection performed at the measurement hot spot can be used to improve the PWQ window (for example, The window of program parameters for which PWQ is executed). The currently used method for automated PWQ re-inspection of pattern defects performs manual or automated design-based re-inspection of pattern defects discovered by a PWQ inspection. Manual methods are inaccurate and unreliable (for example, A user may miss a complete pattern failure or may not be able to discern the subtleties (for example, 3nm to 7nm) CD change), And the design-based method needs the recipe setting between the discovery and metering steps. PWQ detection can be performed as described in the following US patents: US Patent No. 6, issued on June 7, 2005 to Peterson et al. 902, No. 855, US Patent No. 7, issued on August 26, 2008 to Peterson et al. 418, No. 124, US Patent No. 7, issued on August 3, 2010 to Kekare et al. 769, No. 225, US Patent No. 8 issued to Pak et al. on October 18, 2011 041, No. 106 and U.S. Patent No. 8 issued to Peterson et al. on July 3, 2012 213, No. 704, These U.S. patents are incorporated herein as if cited in full. The embodiments described herein may include any step(s) of any method(s) described in these patents and may be further configured as described in these patents. A PWQ wafer can be printed as described in these patents. In a further embodiment, During the on-line monitoring of one of the manufacturing procedures performed on the sample, the measurement procedure is performed on the sample. In this way, The measurement procedure determined for one or more of its parameters may include a measurement procedure performed during online monitoring (i.e., (Measurement performed on a wafer generated by a manufacturing process). Measurements such as critical dimension uniformity (CDU) of gates, Line edge roughness (LER)/line width roughness (LWR) measurement, These measurement procedures are performed for CD/stack pair measurement and other measurements. In another embodiment, Automatically generating includes automatically generating the ROI to be measured during the metrology process based on the results of the design and execution of one of the inspection procedures on the sample. For example, In-line monitoring can also be performed on the location of defects detected by inspection, The position of the detected defect is basically used as a "hot spot" for the measurement of the detection guide. In some such embodiments, The measurement result can be related to the detection result. For example, In some examples, A pattern fidelity feature generated by detection may be related to the measurement performed during measurement. Compared with the embodiments described herein, The currently used method for metrology during in-line monitoring uses a CD-SEM tool at specific metrology targets (eg, CD/stack measurement is performed at the scribe line printed on the wafer, And because the recipe setting is quite laborious in defining the ROI, Therefore, it is not possible to automatically measure thousands of unique points on a wafer. Some other currently used methods for in-line monitoring include using a SEM re-inspection tool to randomly sample several locations from millions of hot spot locations to perform critical point inspection (CPI) using a die-to-die mode. however, Due to random sampling of hot spots, The currently used method can miss a substantial number of hot spot defects. In an additional embodiment, One or more computer subsystems are configured for comparing one or more measurements performed in one of the first and second subsets of ROI with the first and second subsets of ROI The design intention of the one is to modify an optical proximity correction (OPC) model based on the result of the comparison. In this way, The measurement procedure for determining one or more parameters can be performed for the OPC model verification of design intent. Compared with the embodiments described herein, The currently used method for verifying the OPC model of design intent uses a CD-SEM tool, The recipe setting for defining multiple ROIs at each site is a very laborious and time-consuming procedure and therefore can measure substantially a limited number of ROIs at each site and each die for CD measurement A limited number of unique sites. For OPC, We need to automatically discover weak structures and set and measure thousands of unique sites per die immediately and/or automatically. In another embodiment, One or more computer subsystems are configured to detect defects in one of the first and second subsets of ROI based on one or more measurements and report one or more measurements as Detect defect attributes of defects. In this way, The metrology procedure may include reporting pattern fidelity measurements as defect attributes at the defect locations reported by the repeated detection algorithm. Compared with the embodiments described herein, The currently used method does not report measurement statistics as part of the defect attribute and therefore cannot quantify whether a pattern distortion is an obstacle, Partly broken, Completely broken, Partially bridged or fully bridged. The embodiments described herein have several advantages over currently used methods for determining one or more parameters of a metrology procedure. For example, The embodiments described herein provide a substantially rapid automated real-time mechanism to generate ROI for thousands of unique sites and then automatically generate various measurement statistics and attributes for each ROI across various sites (using (Abbreviated form of SEM image and physical design of the positioning point), It can then be used to serve the various use cases described herein. Another embodiment relates to a computer-implemented method for determining one or more parameters of a measurement procedure to be performed on a sample. The method includes the steps of automatic generation and automatic determination described above. Each of the steps of the method can be performed as described further herein. The method may also include any other step(s) that may be performed by the measurement subsystem and/or computer subsystem(s) or system described herein. Automatically generated and automatically determined steps are executed by one or more computer systems, The one or more computer systems can be configured according to any of the embodiments described herein. In addition, The method described above can be performed by any of the system embodiments described herein. An additional embodiment relates to a non-transitory computer readable medium, Its storage can be executed in a computer system for executing program instructions for determining a computer-implemented method of one or more parameters of a metrology program to be executed on the sample. One such embodiment is shown in FIG. In particular, As shown in Figure 14, The non-transitory computer readable medium 1400 includes program instructions 1402 executable on the computer system 1404. Computer-implemented methods may include any step(s) of any method(s) described herein. Program instructions 1402 implementing methods such as those described herein may be stored on computer readable medium 1400. The computer-readable medium may be a storage medium, Such as a disk or disc, A magnetic tape or any other suitable non-transitory computer-readable medium known in the art. Program instructions can be implemented in any of various ways, Contains program-based technology, Component-based technology and/or object-oriented technology, etc. For example, ActiveX controls can be used as needed, C++ objects, JavaBeans, Microsoft Foundation Category ("MFC"), SSE (SIMD Streaming Extension) or other technologies or methods to implement program instructions. The computer system 1404 can be configured according to any of the embodiments described herein. Additional embodiments described herein include a system configured to determine one or more parameters of a program or a parameter that will be executed on the sample. The system contains a measurement subsystem, The measurement subsystem can be configured according to any of the embodiments described herein. In one embodiment, The sample contains a wafer. In another embodiment, The sample contains a double reticle. The wafer and reduction mask can include any suitable wafer and reduction mask known in the art. The embodiments described further herein are configured for area (or ROI) generation for automated pattern fidelity assessment and monitoring. The embodiments described herein can be used to automatically generate metrology and/or detection sites for hot spot monitoring in semiconductor operations. Pattern fidelity and overlay issues become increasingly critical to device yield. in tradition, Generate measurement points manually. For example, The current and previously used methods for hotspot monitoring have been driven by manual efforts. In one such instance, For a given hot spot location, For measurement purposes, A user will have to manually draw a subset of the area. Although this method can be used for a limited number of sites (for example, Dozens of sites) effective, But it is not feasible to monitor hundreds of hotspot locations with dozens of locations within the hotspot. For example, The number of sites to be monitored and the type of pattern are increasing, And the manual method of setting measurement and detection sites is not enough. In particular, Setting up these hundreds of areas is substantially time-consuming and inefficient. In one such instance, It can take an hour to draw measurement points (or bounding boxes) of dozens of points. however, Mapping these measurement sites to hundreds of sites creates user fatigue and requires too much time to generate the measurement formula. therefore, Existing manual methods have limitations. In addition, Visual re-inspection of SEM images is usually not sufficient to determine good patterns from damaged patterns and also lacks the ability to quantify to make a target decision. The combination of the need to monitor a substantial number of sites and the SEM images that require pattern fidelity quantification advances the new method described in this article. In addition, Product development and monitoring need to be substantially accurate, Automated measurement site placement and measurement measurement. The system contains one or more computer subsystems, They may include any of the computer subsystems and computer system embodiments described herein. One or more computer subsystems are configured to determine an area of a defect detected in the sample. Defects can be detected on samples by inspection, Detection may include optical detection (eg, Broadband optical detection or optical detection based on light scattering) or electron beam based detection (eg, Performed using a SEM). Defects can also be detected by other procedures (such as metrology) performed on a physical version of the sample. however, Defects can be detected by performing one or more procedures on the design, Such as how the analog design will be formed on the wafer (for example, Lithography simulation, Etching simulation, etc.). In addition, The defect can be a user-defined location on the sample, A user suspects that a defect may exist at the location or a defect is known to exist at the location. It is determined that the area of the defect detected on the sample may include the automatic identification of the defect center, The defect can be any of the defects described in this article, Such as detecting a defect by physical sample detection or identifying a hot spot by simulation. The defect area can then be determined around the identified center of the defect and centered on it. In this way, The computer subsystem(s) can be configured for automatic zone definition of a fault location and its affected area. Defect areas can be further determined as described herein. In one embodiment, The area of the defect is defined by a bounding box drawn around the defect in one of the images of the defect. For example, The bounding box can be drawn based on a defect location in the design space. In one such instance, The bounding box may be based on the defect area (eg, Defective pixels) determination. In addition, A technique such as design rule checking (DRC) can be used to calculate the bounding box. In another embodiment, The area of the defect is defined by a free-form area drawn around the defect in an electron beam image based on the defect. For example, The defective area can be a "spot" or affected area of an SEM image. Similar "spots" can be determined based on other types of images (such as optical images). The free-form area can be automatically drawn by the computer subsystem(s) described herein and in any suitable manner known in the art. One or more computer subsystems are also configured to correlate the defect area with the design information of one of the samples. The design used for the samples in the embodiments described herein may include any of the designs or design data described herein. In addition, The design used in the embodiments described herein may include any design layout data format suitable for automatic measurement area generation. These formats include open formats (such as GDS, OSASIS, Text) or proprietary format (such as RDF (available from KLA-Tencor)). Appropriate data formats or conversion formats can be fed into the automatic metering/detection area generator embodiments described herein. In one embodiment, Design information includes information on more than one level of design. For example, The design information may include information on a layer above and/or below the design layer on which the defect is detected. In this way, The steps or functions described herein may be performed with respect to more than one layer designed for one of the samples. The information on more than one layer of the design may include any of the design information described in this article. In another embodiment, The design information does not include information about the characteristics of the design that will not be printed on the sample. For example, The design information preferably does not include the features included in the design data of the sample but not actually printed on the sample, Features such as optical proximity correction (OPC). In this way, The design information can more accurately reflect how the design is intended to be printed on the sample than if the design information contains information on OPC features and other features that will not be printed on the sample. In some embodiments, Design information includes information on patterned features in the design, And the interrelationship includes overlapping the defect area with the patterned features in the design. For example, The computer subsystem(s) may be configured to superimpose an optical or SEM-based spot and design information to automatically determine the ROI as described further herein and the ROI metrics as described further herein. Correlating the information of the defective area and the design of the sample can be performed by aligning the design in some way with the output generated by the detector of the measurement subsystem. No. 7 of the U.S. Patent Case issued to Kulkarni et al. on March 9, 2010 676, No. 077 describes examples of methods and systems that can be used to align the output of a measurement subsystem to a design, The case is incorporated into this article as if it were cited in full text. Correlating the defect area with the design information can also involve determining the design data space coordinates of the defect area in the design. In one embodiment, Design information includes information about additional ROI in the design based on design rather than defect determination by one or more computer subsystems. In this way, The extra ROI can be called "unsupervised ROI", The additional ROI is based only on design decisions and is not "supervised" or modified by any other information (such as defect information). For example, The computer subsystem(s) can be configured to use a design layout file to automatically generate ROI based on a given location. In unsupervised mode, The critical section can be determined based on the design node. In addition, Additional ROI can be automatically identified for the weakest point in one of the polygon groups in unsupervised mode. ROI types can include (but not limited to) and space, The point where the line and the adjacent pattern are associated. In addition, It is determined that the additional ROI performed by the computer subsystem(s) may include automated site selection for overlay metrology. The additional ROI may be automatically determined using one or more of several design analysis tools (such as those further described herein) (or a combination of one or the other). Although the design analysis tools described further in this article are not currently used in this way, However, the tools and data flow can be modified to perform automated metrology site generation as described further herein. therefore, The embodiments described herein provide a substantially new method that has not previously attempted to be automated. For example, As an alternative to the embodiments described herein, Users can be equipped with tools to allow manual drawing of areas of interest for a given hot spot. however, This is a time-consuming process that is substantially error-prone. In another embodiment, Design information includes information about additional ROI in the design determined by one or more computer subsystems by pattern matching based on design rather than defect execution. For example, Physical pattern matching may include using some information of polygons in the design to match information of other polygons in the design. This matching can be performed as described further herein. The information of the additional ROI can then be determined in any suitable way based on the pattern matching result. In an additional embodiment, Design information includes information about additional ROI in the design determined by one or more computer subsystems by virtue of geometric matching performed based on the design rather than defects. For example, Geometric matching may include using one or more rules to find instances of specific geometric-based characteristics of the design. The one or more rules can have any suitable format known in the art and can be generated or obtained in any manner known in the art. Geometric-based characteristics may include any geometric-based characteristics of the design of the sample, Such as a geometric shape with a specific shape or a specific size, spacing, cycle, Orientation and other specific types of geometric shapes. The information of the additional ROI can then be determined in any suitable way based on geometric matching. In some embodiments, The design information includes information about the additional ROI in the design determined by the unit information of one or more computer subsystems based on the design (not defects) of the unit. The additional ROI may be identified based on the unit information by using some information about polygons or structures within the unit to determine the additional ROI. Unit information may be included in or obtained from any of the design data described herein. The information of the additional ROI can then be determined in any suitable way based on the unit information. In another embodiment, Design information includes information about additional ROI in the design determined by image processing of one or more computer subsystems based on a graphical representation of the design (rather than defects). The design data can be converted into a graphical representation of the design in any suitable way, And the graphical representation may have any suitable format. Image processing can then be performed on the graphical representation to identify specific portions of the graphical representation with specific image characteristics. The information of the additional ROI can then be determined in any suitable way based on the graphical representation. The computer subsystem(s) can also be configured to use the techniques described in this article (such as pattern matching, Geometric matching, Unit information and image processing) or a combination of two or more to define the ROI. In one embodiment, The design information includes information about additional ROI in the design based on hot spot information of the design (not defects). Hotspot information can be generated or obtained in any suitable manner known in the art. No. 7 of the U.S. Patent Case issued to Kulkarni et al. on March 9, 2010 676, No. 077 describes examples of methods and systems that can be used to generate or obtain hotspot information, The case is incorporated into this article as if it were cited in full text. The information of the additional ROI can then be determined in any suitable way based on the hotspot information. In a further embodiment, Design information includes information about additional ROI in the design based on information about hot spots in the design (not defects), And only one or more additional ROI is determined for one of the hot spots. FIG. 18 illustrates an embodiment of a hot spot including more than one additional ROI. For example, As shown in Figure 18, The hot spot 1800 may include a relatively narrow space 1802 between two wider features 1804. thus, The space 1802 may be a critical space in the CD. The hot spot 1800 also includes a relatively narrow line 1806 spaced from other features in the hot spot. thus, Line 1806 may be a critical line in CD. therefore, This hot spot may contain at least three different additional ROIs. One additional ROI 1808 can be generated for space 1802 only. Another additional ROI 1810 may be generated only for line 1806. The additional ROI may be determined according to any of the embodiments described herein. As described further in this article, In some examples, The computer subsystem(s) can be configured to automatically determine additional ROI. therefore, One advantage of the embodiments described herein is that a sub-region of a hot spot (or sometimes referred to as a weak spot in a design) can be automatically identified for metrological monitoring purposes. In other words, The embodiments described herein may be configured for unsupervised monitoring of a potentially weak or weakest site in a design. For a given hot spot that may need to be monitored, there can be dozens of areas, And the computer subsystem(s) described herein can be configured to perform an automated algorithm-based method to identify these areas. One or more computer subsystems are further configured to determine a spatial relationship between the defective area and the design information based on the results of the correlation. In some embodiments, Design information includes information about additional ROI in designs based on design rather than defect determination, And determining the spatial relationship includes determining which additional ROI overlaps the defective area spatially. For example, Once the defective area has been associated with design information, Then a spatial relationship can be determined, What elements such as design information or which additional ROI(s) overlap (at least partially) the defective area, Which elements of the design information or which additional ROI(s) are closest to the defect area, One or more distances between one or more elements of the design and the area of the defect, One or more distances between one or more of the additional ROIs and the defect area, etc. The spatial relationship can be expressed in any suitable format, E.g, Expressed as one or more identifiers indicating which design elements or which additional ROI(s) overlap (at least partially) the area of the defect (or at least partially), or the area closest to the defect, One or more dimensions between the defect area and one or more features of the design information or one or more additional ROI, etc. One or more computer subsystems are also configured to automatically generate a ROI to be measured using the measurement subsystem during a procedure performed on the sample based on the spatial relationship. In this way, The input data of the steps performed by the computer subsystem(s) can be the design layout, Defect location (for example, Fault pattern location), Hot spots, Known and unknown locations and procedures performed on design information (eg, An image-based algorithm), And then the measurement area or the area of interest can be automatically identified and generated. thus, The computer subsystem(s) can be configured to perform a supervisory mode, Which is based on defects, The ROI is automatically determined at the location or position of the fault pattern. therefore, The ROI described herein may be referred to as a "supervised" ROI. In this way, The embodiments described herein may be configured to determine the ROI of the metrology used to detect guidance (or another procedure described herein), And perform the measurement of the measurement guidance (or another procedure described herein) as appropriate. Supervised ROI types include (but are not limited to) and space, The point where the line and the adjacent pattern are associated. therefore, The output of the embodiments described herein may include ROIs or monitoring areas that require measurement and/or detection. then, These outputs can be transformed to perform one of the operations described herein (e.g., Measurement, Detection or re-detection operation). The result of the ROI generation step may include any information that can be used to identify the ROI on the sample or another sample (eg, A ROI name, Numbers or other identifiers, ROI design or sample coordinates, etc.). In one embodiment, Automatically generating the ROI includes selecting one of the additional ROIs that spatially overlap with the defect area as the ROI to be measured for the defect. FIG. 15 illustrates an embodiment of this automatic ROI generation. For example, As shown in Figure 15, Information of a design (such as design abbreviated form 1500) can be input to the computer subsystem(s). As shown in Figure 15, The abbreviated form of the design may contain several different polygons representing the features in the design. The shaded polygons shown in the design abbreviated form 1500 represent the features formed on the sample, The non-shaded area represents the space between features. The computer subsystem(s) may use the information in the design abbreviation form (and possibly the design abbreviation form itself) to pre-define an additional ROI based on the fidelity measure of the known pattern. In other words, The types of measurements that can be performed in any given procedure can be used to search for design information for potential candidates for such measurements. For example, Based on design information in abbreviated form, The computer subsystem can be pulled back based on the line end, width, space, Pre-defined ROIs such as corners. In one such instance, If the type of measurement includes width measurement, You can search for design information designed to have a feature with a width less than a specific value, This makes its features with wider widths more prone to defects (for example, Opening). In the embodiment shown in FIG. 15, Several different ROIs can be determined based on these metrics. These ROIs can include, for example, wire-end pullback ROI 1502, Width ROI 1504, Spatial ROI 1506 and corner ROI 1508. Since ROI is determined based on design without any other information to monitor ROI determination, Therefore, ROI can be unsupervised. In one embodiment, The area of the defect is defined by a free-form area drawn around the defect in one of the defects based on the electron beam image (eg, As one of the SEM spots described further herein), Design information includes information on additional ROI in the design based on design rather than defect determination (which may include any such information described in this article), Determining the spatial relationship includes determining which additional ROI overlaps the area of the defect spatially or the area closest to the defect (this can be performed as described further herein), And automatically generating the ROI includes selecting or prioritizing one of the additional ROIs that spatially overlap with the defect area or the area closest to the defect as the ROI to be measured for the defect. In this way, Can reselect or prioritize potential measurement sites based on SEM positions (spots), This makes it possible to measure the most likely defect sites. For example, The information about the ROI can be combined with one of the defect regions described herein, such as a defect region or an SEM spot from sample inspection or simulation. In one such instance, As shown in Figure 15, The SEM spot 1510 may be the same as the ROI described above (eg, ROI 1502, 1504, 1506 and 1508). As further shown in Figure 15, The superimposed information on the design of the SEM spots may include information on the ROI instead of information on the patterned features in abbreviated design. however, The superimposed information on the design of the SEM spots may also include information on the patterned features (such as the patterned features shown in the design abbreviated form 1500). The ROI that spatially overlaps the SEM spot or is closest to the SEM spot can then be selected or prioritized as a procedure for performing against the ROI (eg, Measurement, ROI of defects corresponding to SEM spots in inspection, etc.). For example, As shown in Figure 15, Since the SEM spot 1510 overlaps with one of the width ROI 1504, Therefore, the ROI can be selected for or prioritized as a measurement to be performed for defects corresponding to SEM spots. therefore, Only the information of the ROI 1504a may be exported as the ROI information 1512 for use in one of the procedures such as those described further herein. however, In the case where the procedure will be executed for unsupervised ROI, All ROI information can also be exported for use in this procedure. In another embodiment, Automatic ROI generation includes changing the defect area based on the spatial relationship between the defect area and the design information and designating the modified area of the defect as the ROI. For example, The computer subsystem(s) can be configured to self-adjust the size of the ROI (correct ROI) by using the interaction between a specified location and the design layout. In one such instance, Measurement sites can be self-calibrated by overlapping the design layout. For example, The computer subsystem(s) can be configured to self-adjust the size of an ROI by overlapping a potential defect point with a line or space in the design layout. Based on a defect or specified location, A measurement area can be automatically corrected based on the design. In one embodiment, Design information includes information on patterned features in the design, And automatically generating the ROI includes determining an area of the ROI based on the patterned features of the defect area and the area adjacent to the defect. The patterned features adjacent to the defect area may include overlapping (partially or completely) with the defect area, Spaced apart from the defect area but adjacent to the area, Is the patterned feature of the area closest to the defect, Patterned features around areas of defects, etc. For example, The embodiments described in this article can not only automatically determine the placement of the measurement site, Moreover, the size of the measurement area can be readjusted based on the position of the defect and the adjacent polygon around it. basically, Use inspection to identify pattern changes or other defects, Use SEM or other measurement subsystem output to improve the exact location of the defect, And improve the measurement area by taking the design into consideration, Which is based on the polygon's own line, The exact size of the space etc. optimizes the measurement location. This region of ROI determination can be further performed as described herein. In another embodiment, Design information includes information on patterned features in the design (which can include any such information described in this article), And automatically generating the ROI includes determining a region of the ROI based on the defect region or the smallest one of one or more of the patterned features near the defect region. For example, The embodiments described herein may be configured to resize the measurement site to the smallest of the defect area or the polygon or space near the defect. This readjustment of the size of the measurement site can be performed as described further herein. FIG. 16 illustrates an embodiment of self-correcting ROI size. For example, Detection of a sample such as the one described herein can detect a bridging defect at location 1600. then, The area 1602 can be determined for this defect. In this example, The area can be defined by a certain bounding box based on the location of a defect in the design space. The bounding box can be determined as described herein. then, It can make the bounding frame overlap with the design information. For example, As shown in Figure 16, The bounding box can be overlapped with the design abbreviated form 1604. In this abbreviated form of design, Non-shaded areas correspond to the patterned features included in the abbreviated form of the design, The shaded area corresponds to the space between the patterned features. then, A Boolean operation can be performed to self-correct the size of the defect area to match the width of the space between the patterned features (which overlaps the area). For example, As shown in Figure 16, The size of the region 1602 can be self-corrected to the size of the ROI 1606 by reducing the size of the region 1602 so that it does not extend beyond the space above and below the space between the patterned features. In this way, By using the design information as a "cutter", Defect areas can be self-calibrated to accurately define the measured area, It can then be used as the ROI for defects in the procedures described herein. FIG. 17 illustrates an embodiment of self-correcting ROI size. For example, Detection of a sample such as the one described herein can detect an opening defect at location 1700 and a wire end short defect at location 1702. then, The area 1704 can be determined for the defect at the position 1700, And the region 1706 can be determined for the defect at the location 1702. In this example, The area of each defect location can be separately defined by a certain bounding box based on the defect location determination in the design space. The bounding box can be determined as described herein. then, Can make the bounding box and design information overlap. For example, As shown in Figure 17, The bounding frame can be overlapped with the design abbreviated form 1710. In this abbreviated form of design, The shaded area corresponds to the patterned features included in the reduced form of the design, The non-shaded area corresponds to the space between the patterned features. Then a Boolean operation can be performed to self-correct the size of the defect area to match the width of the patterned features that spatially overlap with it. For example, As shown in Figure 17, The size of the region 1704 can be self-corrected to the size of the ROI 1712 by reducing the size of the region 1704 so that it does not extend beyond the patterned features that spatially overlap it. In addition, As shown in Figure 17, The size of the region 1706 can be self-corrected to the size of the ROI 1714 by reducing the size of the region 1706 so that it does not extend beyond the patterned features that spatially overlap it. In this way, By using the design information as a "cutter", Defect areas can be self-calibrated to accurately define the measured area, It can then be used as the ROI for defects in the procedures described herein. In some embodiments, Design information includes information about additional ROI in a design based on design rather than defect determination by one or more computer subsystems, And measure one or more of the additional areas during the procedure. For example, As described further in this article, The computer subsystem(s) can be configured for unsupervised ROI generation, It only determines the additional ROI based on design information. In addition, The computer subsystem (etc.) is configured for supervised ROI generation, The ROI is determined based on the design information combined with the defect information. In this way, It can automatically identify the two locations of (several) known defect areas (supervised) and all potential weak points (unsupervised). Both types of ROI can be measured in the same procedure performed on the sample. In some of these examples, The information of the ROI(s) and additional ROI may include some markers that can be used to determine whether any given ROI is a supervised ROI or an unsupervised ROI (eg, label, ID, etc.). The ROI(s) and additional ROI may be measured in any suitable manner depending on the ROI itself. For example, The computer subsystem(s) may be configured to determine one or more parameters of measurements to be performed independently and separately in the ROI(s) and additional ROI. In one such instance, The parameter(s) of the measurement(s) of the ROI(s) can be determined based on the information (possibly combined with the information of the defects) of the design(s) close to the design(s) of the ROI(s), The parameter(s) of the measurement of the additional ROI can be determined based only on the information close to the design of the additional ROI. In this way, The parameter(s) used for the measurement of the ROI(s) may be different from the parameter(s) used for the measurement of the additional ROI. In another embodiment, One or more computer subsystems are configured to automatically determine one or more parameters of one or more measurements performed in the ROI during a procedure using the measurement subsystem. The computer subsystem(s) can use the design layout and layer-aware area to identify the measurement type (or measurement method). In addition, The computer subsystem(s) can be configured to automatically set metrics or how to measure each ROI. The computer subsystem(s) can also be configured to assign measurements to respective ROIs to automatically perform all selected ROI measurements. One or more parameters can include any parameter(s) measured, Such as measuring direction, Measurement type, etc. If (several) parameters are determined for more than one ROI, Then the ROI can be automatically grouped based on the measurement type (or measurement method) of the ROI and an index can be generated for each type (eg, ID). For example, If two ROIs are measured using the same measurement type, Then the two ROIs can be divided into a group independently of the other ROIs, Other ROIs can be grouped into a different group of ROIs. In this way, The embodiments described herein may be configured to be used for searching (eg, design, Optical imaging, SEM image, Pattern matching, Geometric search, Unit information, etc.) to identify the ROI and assign the ROI to a type of ROI for a combination of techniques for automatically generating metrics. In one embodiment, In-line monitoring is performed on the sample during one of the manufacturing procedures performed on the sample. In addition, The system can be configured to execute procedures based on the ROI described herein. In this way, The embodiments described herein may be configured to perform an automated procedure to identify the ROI and perform a procedure based on the ROI (such as one of the procedures described further herein). thus, The embodiments described herein can be configured for fully automated setup (eg, Identify areas to monitor, etc.) and quantify patterned fidelity and changes. therefore, The embodiments described herein implement a key part of the overall process of monitoring pattern fidelity in a semiconductor processing environment. You can also use one or more algorithms and tool platform tuning data at each step. In another embodiment, The procedure includes a measurement procedure, And the system is configured as a measurement tool. In this way, The ROI(s) identified as described herein may include automatically identified metrology sites. The metrology program and metrology tools can be further configured as described herein. In an additional embodiment, The program includes a test program, And the system is configured as a detection tool. In this way, The ROI(s) identified as described herein may include automatically identified detection sites. The inspection procedures and inspection tools can be further configured as described in this article. For example, The system shown in FIGS. 1 and 2 can be configured to be used for detection rather than metering by changing one or more parameters of the system. In particular, The system shown in FIGS. 1 and 2 can be configured such that energy is scanned throughout the sample and in response to detecting energy at a resolution lower than one that will be used for metering and/or higher than that that will be used for metering One of the speeds produces output. Change the parameters of the system shown in Figure 1 and Figure 2 (such as resolution, Speed, etc.) can be performed in any suitable manner known in the art. In this way, The system shown in Figures 1 and 2 can be configured to produce a substantial portion of the sample's output in a relatively short period of time, It can then be used by one or more computer subsystems in any suitable manner (eg, By applying one or more defect detection algorithms to the output, They may include any suitable defect detection algorithm known in the art) to detect defects on the sample. Testing procedures and testing tools can be configured for light-based testing of samples, Sample-based detection of the electron beam or other charged particle-based detection of the system. In particular, As described further in this article, The systems shown in Figures 1 and 2 can be configured to produce light, The output of a sample of electrons or other charged particles. In a further embodiment, The procedure includes a defect re-inspection procedure, And the system is configured as a defect detection tool. The program may also include any other sample analysis program known in the art. Defect detection or other analysis programs and tools can be further configured as described in this article. For example, The system shown in Figures 1 and 2 can be configured to be used for defect inspection or other analysis rather than measurement by changing one or more parameters of the system, This can be performed as described further above, Except that changes can be made to make the system suitable for defect re-inspection or other analysis instead of modifying the system shown in Figures 1 and 2 for inspection. Defect re-examination or other analysis procedures and tools can be configured for light-based defect re-examination or other analysis of samples, Re-detection or other analysis of samples based on electron beam defects or other system-based defects. In particular, As described further in this article, The systems shown in Figures 1 and 2 can be configured to produce light, The output of a sample of electrons or other charged particles. Another embodiment relates to a computer-implemented method for determining that one or more parameters of a program will be executed on a sample. The method includes determining the determination area described above, Interrelated areas, Steps to determine the spatial relationship and automatically generate ROI steps. Each of the steps of the method can be performed as described further herein. The method may also include any other step(s) that may be performed by the measurement subsystem and/or computer subsystem(s) or system described herein. The determination area is executed by one or more computer systems, Interrelated areas, Determine the spatial relationship and automatically generate ROI steps, The one or more computer computer systems can be configured according to any of the embodiments described herein. Although the one or more computer subsystems are described herein as part of a system that includes a measurement subsystem, However, the one or more computer subsystems may also be configured to not include a physical version of the processing sample and to measure one of the ability to perform measurements on the physical version of the sample, Detection, (Several) independent computer subsystems for re-testing or other system parts. In addition, The method described above can be performed by any of the system embodiments described herein. An additional embodiment relates to a non-transitory computer readable medium, Its storage can be executed in a computer system to execute program instructions for determining a computer-implemented method for one or more parameters of a program to be executed on the sample. This embodiment may be further described herein and configured as shown in FIG. 14. The computer-implemented method may include any step(s) of any method(s) described herein. In some embodiments, The output generated by the detector contains the image of the sample, The detector generates different images for different areas on the sample, And the multiple patterned features on the sample are imaged in different images. As described further in this article, The detector can generate images of different areas of the sample. The plurality of patterned features may include any patterned features designed to be formed on a sample (such as a doubling reticle or a wafer). For example, As described further in this article, Multiple patterned features may include contacts or line/space pairs. In one such embodiment, One or more computer subsystems are configured to: Automatically locate multiple patterned features in different images; Determine one or more characteristics of multiple patterned features located in different images; And determining one or more statistical data of one or more characteristics determined for the positioned plurality of patterned features in different images. For example, The embodiments described herein can be performed on an image processing tool using an algorithm, The image processing tool reads in the SEM re-detection images and outputs the measured CD or other characteristic measurement and statistical data of the patterned features in these images. Depending on the type of pattern to be quantified and/or the type of defect (such as contact array or line-space pattern), Any number of methods can be used to process the image. Although some embodiments are described in this text regarding CD, But understand, The CD may be replaced with any other suitable characteristics of patterned features and the embodiments may function in the same manner as described herein. The automatic positioning of multiple patterned features in different images can be performed as described further herein (by aligning the patterned features in the image to the patterned features in a reference) or in any other way. One or more characteristics of the positioned plurality of patterned features in different images may be determined as described further herein or in any other suitable manner known in the art. The determination of one or more statistics of one or more characteristics may be performed as described further herein or in any other suitable manner known in the art. One or more statistics may include any of the statistics described herein (such as average) or any other appropriate statistics (such as median, Standard deviation, Average and the like). In one such instance, Images can be read in sequence. An image processing algorithm can automatically locate any feature of interest. For example, The position of each contact in the image or the position of each line space pair can be automatically located by the computer subsystem. then, The algorithm can measure the CD of each feature and display the results of each image. At the end of quantifying all images, The detailed statistical data of CD measurement can be saved for further analysis by the user. In the example of the contact array, The quantity to be measured can be the diameter of each contact in the image X and Y and the distance between all contacts (ie, Unit size). The computer subsystem(s) can then generate an image intensity curve through one of the contacts in the image, Since then, the diameter of the contact hole can be measured. For example, The gray scale in the image of a contact hole can be drawn according to the position of a diameter across the contact hole. The full width at half maximum (FWHM) of the quantitative curve can then be measured and determined as the diameter of the contact hole. When a new pattern type or control quantity is required, The embodiments described herein are flexible in nature and new algorithms can be easily added. Display the image and any related measurement pattern attributes to the user. These measurements and attributes can then be saved for further use. Images can be classified by new defect attributes and the resulting statistical distribution can be incorporated into the initial optical inspection result file. In some embodiments, Different areas include areas on the sample (defects detected on the sample and selected for defect re-detection are located in these areas), And during the execution of a defect re-inspection procedure for defects, different images are generated by the measurement subsystem. For example, During wafer inspection, Defects can be detected on samples. then, Select one sample of defects for defect re-examination. Defect re-inspection involves obtaining an image of the sample only at the area where the previous defect was located. therefore, In the defect re-inspection, For each defect contained in a sample, A series of images can be acquired at discrete locations on the sample (one defect location at a time). The embodiments described herein may use the images as described further herein to determine one or more characteristics of patterned features in different images. thus, The embodiments described herein may only use images generated during defect re-detection at the location of the defect previously detected on the sample (without generating or acquiring any additional images) to determine the patterned features in the image ( Several) characteristics. In one embodiment, Multiple patterned features include contacts. The contacts may include any type of contact of any type design used to manufacture any type of device on the sample. In another embodiment, The measurement subsystem is configured as an electron beam microscope. In this way, The embodiments described herein can be configured to measure the contacts using images generated by an SEM or other suitable electron beam microscope (which can be configured as described further herein). In some embodiments, One or more characteristics include a critical dimension (CD) of the contact. The CD of the contact may include one of the diameter of the contact or any other suitable CD of the contact. In one such instance, A substantially high-resolution SEM re-inspection tool is used to re-inspect the defects sought by an optical defect-finding device. In this re-detection image, It can substantially accurately measure the size of defects and other image features and CDs. The embodiments described herein allow the user to effectively scan through substantially a large number of these SEM re-detect images and automatically measure many CDs within these images, Thereby giving feedback to the optical inspection tool. For example, In the SEM image with hundreds of contacts, The diameter of all contacts can be measured to quantify the consistency of the program steps, And measure the distance between all contacts, This can quantify the accuracy of the overlay in the case of double or multiple patterning process steps. Double or multiple patterning steps generally involve printing different parts of a single layer on the same copy in different process steps. therefore, The embodiments described herein can be used to efficiently and accurately determine the positioning of the first feature printed on one layer of the sample in one program step relative to the feature printed on the same layer of the sample in another program step . The embodiments described herein significantly reduce the time to produce results on the initial optical defect finding device. In a specific example, The embodiments described herein apply to a contact layer with programming defects. Measure and plot all contact diameters in a scatter diagram. In particular, Generate a scatter diagram of the X and Y dimensions of all contacts in an image. One of the stylized defects consisting of a contracted contact is easy to highlight. For example, The size of the "cloud" of the contact diameter gives one-time visual feedback of the accuracy and repeatability of the contact size. Any statistical outliers (for example, A shrink joint) can be marked in a certain way (for example) using a color (such as red), And it can be detected as a defect in the optical inspection. The degree of expansion (standard deviation) of normal diameter contacts sets a limit on which defect sizes can be detected externally during naturally occurring process changes. In addition, Generate a histogram distribution of all contact sizes in 1000 images. Can execute 100 in a substantially short amount of time, 000 measurements. SEM re-inspection images have usually been used to quantify a defect previously detected by an inspection tool. The embodiments described herein provide features and CDs (such as the substantially large number of contact diameters and cell sizes in SEM re-detection images) as an integral part of the defect detection process. This ability is valuable in process control, Therefore, the CD measurement information is fed back to the defect detection process of the optical inspection tool. This capability can be extended to many different feature measurements in SEM images. This capability significantly reduces the time it takes for the defect detection process to produce results. Compared with the embodiments described herein, A relatively large number of SEM images of potential defect areas can be collected from many locations on a wafer. The application engineer can then display each of these images on a computer screen and determine whether there is a defect in the image. It is simple to measure the size of a defect and draw pixels in two dimensions by drawing a frame around the defect, But it is impractical to measure the CD of many (hundreds) features in the image in this way. usually, The measurement is only performed to an accuracy of one integer number of pixels. therefore, Statistics on CDs remain sparse and not relatively accurate. therefore, Compared to the embodiments described herein, The currently used method described above has several disadvantages. For example, The manual classification of SEM audio is essentially labor-intensive and time-consuming. As design rules shrink, Changes in the program at a given level become key variables. Within the allotted time, An engineer can only measure a substantially limited number of CDs in an image or a group of images. The accuracy of the measurement can be dependent on the user, And in the presence of image noise, These measurements can be subjective and change repeatedly. In a further embodiment, Multiple patterned features include line and space pairs. For example, Images with vertical line-space patterns can be checked by measuring the line-spacing at each pixel along the trench. A scatter diagram of a size determined for the line-space pattern can be generated. Use this scatter plot, Any outlier line-space can be identified as described above. The width or depth of the trench (such as average width, Minimum depth, Maximum depth, Various data of minimum width and maximum width) to generate another graph of groove depth/contrast according to the groove width, They can be based on targeting a substantial number of images (for example, 1000 images) to determine the size of the line-space measurement. In an additional embodiment, One or more computer subsystems are further configured to automatically classify multiple patterned features based on the determined one or more statistical data. For example, The embodiments described herein can be configured for automatic contact classification using SEM re-detection images. Contacts can be automatically classified as defects (for example, Shrink contacts, Zoom in contacts, Misplaced contacts, etc.) may be classified in some other way based on one or more statistical data determined. The embodiments described herein have several advantages over other methods and systems for measuring the characteristics of patterned features. For example, As design rules shrink, Substantially small aberrations in printed features (such as line edge roughness (LER) or joint size) become critical measurements, Its level (not just the defect itself) is important in program control. The embodiments described herein for measuring CD do not require a dedicated CD tool and are orders of magnitude faster and more accurate than the SEM manual measurement of the CD in the image for re-detection. therefore, The embodiments described herein can save a lot of time relative to the manual measurement and classification of CDs in patterns imaged using SEM re-inspection tools. The results are more repeatable and not user dependent. therefore, The measurement system is more accurate and repeatable. Since defects and features can be measured in a substantially short amount of time, Therefore, the embodiment may be an integrated part of the defect detection procedure and the procedure limitation. In addition, Give users better statistics on the consistency of a given procedure step, Can handle larger samples. In addition, The results generated by the embodiments described herein can be fed back to the optical inspection tool for further tuning of the inspection recipe. This configuration can have specific advantages in characterizing PWQ or FEM wafers. Given this description, Those skilled in the art will understand further modifications and alternative embodiments of the various aspects of the invention. For example, Provides a method and system for determining one or more parameters of a program to be executed on a sample. therefore, This description is to be understood as illustrative only and for the purpose of teaching a person skilled in the art to perform the present invention in a general manner. Should understand, The form of the invention shown and described herein should be considered as the presently preferred embodiment. After benefiting from this description of the invention, As those familiar with this technology will understand, Elements and materials may replace those shown and described herein. Parts and procedures can be reversed and certain features of the invention can be utilized independently. Changes can be made to the elements described herein without departing from the spirit and scope of the invention as described in the patent application scope of the invention below.

10‧‧‧Measurement subsystem 14‧‧‧Sample 16‧‧‧Light source 18‧‧‧Optical element 20‧‧‧Lens 21‧‧‧ Beam splitter 22‧‧‧Object stage 24‧‧‧Concentrator 26‧‧‧component 28‧‧‧detector 30‧‧‧concentrator 32‧‧‧component 34‧‧‧detector 36‧‧‧ computer subsystem 102‧‧‧computer subsystem 122‧‧‧Electronics Column 124‧‧‧ Computer subsystem 126‧‧‧E-beam source 128‧‧‧Sample 130‧‧‧Element 132‧‧‧Element 134‧‧‧ Detector 300‧‧‧Field of view (FOV) 302‧‧‧ Measurement location 304‧‧‧ region of interest (ROI) 306‧‧‧ region of interest (ROI) 308‧‧‧ region of interest (ROI) 310‧‧‧ measurement 312‧‧‧ measurement 314‧‧‧ Measurement 316‧‧‧Measurement 400‧‧‧Part 402‧‧‧Pattern/feature 404‧‧‧Pattern/feature 406‧‧‧Pattern/feature 600‧‧‧‧ area of interest (ROI) 602‧‧‧ follow Area (ROI) 604‧‧‧ area of interest (ROI) 606‧‧‧ area of interest (ROI) 608‧‧‧ area of interest (ROI) 610‧‧‧ area of interest (ROI) 612‧‧‧ follow Region (ROI) 614‧‧‧ Region of concern (ROI) 616‧‧‧ Region of concern (ROI) 618‧‧‧ Region of concern (ROI) 620‧‧‧ Region of concern (ROI) 700‧‧‧ Line 702 ‧‧‧Polygon 704‧‧‧line 706‧‧‧polygon 708‧‧‧lower edge 710‧‧‧region 712‧‧‧region 800‧‧‧upper edge 1000‧‧‧line 1002‧‧‧‧line 1004‧‧‧ Line 1006‧‧‧Polygon 1008‧‧‧Imaginary center line 1010‧‧‧Imaginary center line 1012‧‧‧Imaginary center line 1014‧‧‧Imaginary center line 1100‧‧‧ Line 1102‧‧‧ Line 1104‧‧‧ Line 1106 ‧‧‧Polygon 1108‧‧‧Imaginary center line 1110‧‧‧Imaginary center line 1112‧‧‧Imaginary center line 1114‧‧‧Imaginary center line 1200‧‧‧Align 1202‧‧‧Align 1204‧‧‧Align 1206‧‧‧Aligned 1300‧‧‧Patterned feature 1302‧‧‧Patterned feature 1304‧‧‧Patterned feature 1306‧‧‧Patterned feature 1308‧‧‧Imaginary centerline 1310‧‧‧Imaginary centerline 1312‧ ‧‧Imaginary centerline 1314‧‧‧Imaginary centerline 1316‧‧‧Centerline 1318‧‧‧Centerline 1320‧‧‧Centerline 1322‧‧‧Centerline 1324‧‧‧Centerline 1326‧‧‧Line 1328‧‧ ‧Line 1400‧‧‧non-transitory computer readable media 1402‧‧‧program command 1404‧‧‧computer system 1500‧‧‧designed shortened form 1502‧‧‧line end pull back to the area of interest (ROI) 1504‧‧ ‧Width of interest (ROI) 1504a‧‧‧Region of interest (ROI) 1506‧‧‧Space of interest (ROI) 1508‧‧‧corner of interest (ROI) 1510‧‧‧SEM spot 1512‧‧ ‧Region of interest (ROI) information 1600‧‧‧ location 1602‧‧‧region 1604‧‧‧Design abbreviated form 1606‧‧‧region of interest (ROI) 1700‧‧‧ location 1702‧‧‧ location 1704‧‧‧ Region 1706‧‧‧Region 1710‧‧‧Design abbreviated form 1712‧‧‧ area of interest (ROI) 1714‧‧‧ area of interest (ROI)1800‧‧‧hot spot 1802‧‧‧space 1804‧‧‧Feature 1806 ‧‧‧ line 1808‧‧‧ area of interest (ROI) 1810‧‧‧ area of interest (ROI)

Those skilled in the art will benefit from the following detailed description of the preferred embodiment and after referring to the accompanying drawings, it will become clear that further advantages of the present invention, wherein: FIG. 1 and FIG. 2 are illustrated as A schematic diagram of a side view of an embodiment of a system with the general configuration described in FIG. 3; FIG. 3 is an embodiment showing the relationship between various terms (including measurement site, field of view, and area of interest) used herein A schematic view of a plan view; FIG. 4 is a schematic view of a plan view of an example of a part of a design of a wafer when it appears in the design space; FIG. 5 is a schematic view of a plan view shown in FIG. 4 A schematic view of a plan view of an example of a part of a design when it can be printed on a wafer; FIG. 6 shows a part of the design with potential areas of interest within the part of the design shown in FIG. 5 A schematic view of a plan view of an embodiment; FIGS. 7 to 8 are diagrams for aligning a part of a design of a wafer in a design space with a design part of a wafer in a wafer space A schematic view of a plan view of different examples of the results of the current method of use; FIG. 9 shows one of the parts of the design of a wafer in the design space and one of the parts of the design of the wafer in the wafer space An example of a plan view of an example of the result of the embodiment; FIGS. 10 to 12 are plan views showing a part of a design of a wafer in a design and wafer space and how it can be used in this article A schematic diagram of the alignment of the described embodiment; FIG. 13 is a plan view showing a part of a design of a wafer in the wafer space and how a measurement can be performed across it determined by the embodiments described herein 14 is a block diagram of an embodiment of a non-transitory computer-readable medium storing program instructions for causing a computer system to execute a computer-implemented method described herein; 15 to 17 show the results of various steps performed by the embodiment described in this article for the information of one design of the sample, one area of a defect detected on the sample, and the information of the design and the area of the defect Schematic views of plan views of various embodiments; and FIG. 18 is a schematic view of a plan view of one embodiment of one or more additional areas of interest determined for only one hot spot in a design of the same sample. Although the present invention is susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of examples in the drawings and will be described in detail herein. The drawing may not be drawn to scale. However, it should be understood that the drawings and their embodiments are not intended to limit the present invention to the particular forms disclosed, but on the contrary, are intended to cover the invention as defined by the scope of the accompanying invention patent application All modifications, equivalents and alternatives within the spirit and scope.

10‧‧‧ Measurement subsystem

14‧‧‧ sample

16‧‧‧Light source

18‧‧‧Optical components

20‧‧‧Lens

21‧‧‧beam splitter

22‧‧‧stage

24‧‧‧Concentrator

26‧‧‧ Components

28‧‧‧detector

30‧‧‧Concentrator

32‧‧‧component

34‧‧‧detector

36‧‧‧ Computer subsystem

102‧‧‧ Computer subsystem

Claims (37)

A system configured to determine one or more parameters of a program to be executed on a sample, which includes: a measurement subsystem including at least one energy source and a detector, wherein the energy source is grouped To generate energy directed to the sample, and wherein the detector is configured to detect the energy from the sample and generate an output in response to the detected energy; and one or more computer subsystems, etc. The configuration is used to: determine an area of a defect detected on the sample; correlate the area of the defect with the design information of the sample; determine the area of the defect based on the result of the correlation A spatial relationship between the information of the design; and automatically generating an area of interest in which one or more measurements are performed on the sample based on the spatial relationship, wherein the measurement subsystem During a procedure executed on the sample, the one or more measurements are performed in the area of interest by the measurement subsystem. The system of claim 1, wherein the area of the defect is defined by a bounding box drawn around the defect in an image of the defect. The system of claim 1, wherein the area of the defect is defined by a free-form area drawn around the defect in an electron beam image based on the defect. As in the system of claim 1, the information of the design includes more than one layer of the design News. As in the system of claim 1, the information of the design does not contain information about the characteristics of the design that will not be printed on the sample. The system of claim 1, wherein the information of the design includes information on patterned features in the design, and wherein the correlation includes overlapping the area of the defect with the patterned features in the design. The system of claim 1, wherein the information of the design includes: information of additional areas of interest in the design determined solely by the one or more computer subsystems based on the design. The system of claim 1, wherein the information of the design includes: information of additional areas of interest in the design determined by the one or more computer subsystems by pattern matching based only on the design. The system of claim 1, wherein the information of the design includes: information of additional areas of interest in the design determined by the one or more computer subsystems by virtue of geometric matching performed based only on the design. The system of claim 1, wherein the information of the design includes: information of additional areas of interest in the design determined by the one or more computer subsystems based only on unit information of the design. The system of claim 1, wherein the information of the design includes: information of additional areas of interest in the design determined by image processing of the one or more computer subsystems based only on a graphical representation of the design. As in the system of claim 1, the information of the design includes: information of additional areas of interest in the design determined based only on hotspot information of the design. As in the system of claim 1, the information of the design includes: information of additional areas of interest in the design determined based only on information of hotspots in the design, and of which only one of the hotspots is determined These additional areas of concern. As in the system of claim 1, wherein the information of the design includes information of additional areas of interest in the design determined based only on the design, wherein the determination of the spatial relationship includes: determining which of the additional areas of interest are related to the defect The area overlaps spatially, and wherein the automatic generation includes: selecting one of the additional areas of interest spatially overlapping the area of the defect as the area of interest to be measured for the defect. The system of claim 1, wherein the automatic generation includes changing the area of the defect based on the spatial relationship between the area of the defect and the information of the design and designating the changed area of the defect as the focus Area. The system of claim 1, wherein the information of the design includes patterned features in the design Information, and wherein the automatic generation includes determining an area of the area of interest based on the area of the defect and the patterned features adjacent to the area of the defect. The system of claim 1, wherein the information of the design includes information of patterned features in the design, and wherein the automatic generation includes one or more of the patterned features based on the area of the defect or near the defect The smallest one determines the area of the area of interest. The system of claim 1, wherein the area of the defect is defined by a free-form area drawn around the defect in an electron beam image based on the defect, wherein the information of the design includes: only based on the design decision Information of the additional area of interest in the design, wherein the determination of the spatial relationship includes: determining which of the additional areas of interest spatially overlaps the area of the defect or is closest to the area of the defect, And wherein the automatic generation includes: selecting or prioritizing one of the additional areas of interest that spatially overlaps with the area of the defect or closest to the area of the defect to the place to be measured for the defect Concern area. The system of claim 1, wherein the information of the design includes: information about additional areas of interest in the design that are determined by the one or more computer subsystems based only on the design, and wherein the measurement is performed during the process One or more of these additional areas. The system of claim 1, wherein the one or more computer subsystems are further configured to automatically determine the one or more measurements performed by the measurement subsystem in the area of interest during the procedure One or more parameters. The system of claim 1, wherein the output generated by the detector includes an image of the sample, wherein the detector generates different images for different regions on the sample, wherein the patterns on the sample Features are imaged in the different images, and the one or more computer subsystems are further configured to automatically locate the patterned features in the different images to determine the experience in the different images Locating one or more characteristics of the plurality of patterned features, and determining one or more statistics of the one or more characteristics determined for the located plurality of patterned features in the different images. The system of claim 21, wherein the different regions include regions on the sample, defects detected on the sample and selected for defect re-detection are located in the regions, and wherein one of the defects is performed for the defects The measurement subsystem generates these different images during the defect re-inspection process. The system of claim 21, wherein the plurality of patterned features includes contacts. The system of claim 23, wherein the measurement subsystem is further configured as an electron beam microscope. The system of claim 23, wherein the one or more characteristics include a critical dimension of the contacts. The system of claim 21, wherein the plurality of patterned features include line and space pairs. The system of claim 21, wherein the one or more computer subsystems are further configured to automatically classify the plurality of patterned features based on the determined one or more statistical data. The system of claim 1, wherein the process is performed on the sample during online monitoring of one of the manufacturing processes performed on the sample. The system of claim 1, wherein the procedure includes a metrology procedure, and wherein the system is further configured as a metrology tool. The system of claim 1, wherein the procedure includes a testing procedure, and wherein the system is further configured as a testing tool. The system of claim 1, wherein the procedure includes a defect re-inspection procedure, and wherein the system is further configured as a defect re-inspection tool. The system of claim 1, wherein the sample includes a wafer. The system of claim 1, wherein the sample includes a double reticle. The system of claim 1, wherein the energy directed to the sample includes light, and wherein the energy detected from the sample includes light. The system of claim 1, wherein the energy directed to the sample includes electrons, and wherein The energy detected from the sample includes electrons. A non-transitory computer-readable medium whose storage is executable on a computer system to execute program instructions for determining a computer-implemented method for one or more parameters of a program to be executed on a sample, wherein the computer The implementation method includes: determining a region of a defect detected on the sample; correlating the region of the defect with the design information of one of the samples; determining the region of the defect and the defect based on the result of the correlation Designing a spatial relationship between the information; and automatically generating an area of interest in which one or more measurements are performed on the sample based on the spatial relationship, where is performed by a measurement subsystem During a procedure on the sample, the one or more measurements are performed in the area of interest by the measurement subsystem, wherein the measurement subsystem includes at least one energy source and a detector, wherein the The energy source is configured to generate energy directed to the sample, and wherein the detector is configured to detect energy from the sample and generate an output in response to the detected energy. A computer-implemented method for determining that one or more parameters of a program to be executed on a sample includes: determining an area of a defect detected on the sample; and comparing the area of the defect with the sample The information of one design is related to each other; a spatial relationship between the area of the defect and the information of the design is determined based on the result of the correlation; and an area of interest is automatically generated based on the space in the area of interest Relationship One or more measurements are performed, wherein during one procedure performed on the sample by a measurement subsystem, the one or more measurements are performed in the area of interest by the measurement subsystem, wherein The measurement subsystem includes at least one energy source and a detector, wherein the energy source is configured to generate energy directed to the sample, wherein the detector is configured to detect energy from the sample and respond An output is generated from the detected energy, and wherein the determination of the area, the correlation, the determination of the spatial relationship, and the automatic generation are performed by one or more computer systems.

Images