JP2018523820A - Dynamic care area generation system and method for inspection tools - Google Patents

Abstract

The defect inspection system includes an inspection subsystem and a controller communicatively coupled to the detector. The inspection subsystem includes an illumination source configured to generate an illumination beam, a set of illumination optics for directing the illumination beam to the sample, and a detection configured to collect the radiation emitted from the sample Including a bowl. The controller includes a memory device and one or more processors configured to execute program instructions. The controller determines one or more target patterns corresponding to one or more features on the sample, and the one or more care areas on the sample are stored in the one or more target patterns and the sample stored in the controller memory device. Defined based on the design data and configured to identify one or more defects in one or more care areas of the sample based on the irradiation collected by the detector.

Description

  The present disclosure relates generally to defect inspection and, more particularly, to generating care areas for inspection tools.

PRIORITY This application is filed as “NOFFEL AND NOFELAND NO. 2681 / CHE / 2015 EFFRO TANDEN” filed on May 28, 2015 in the name of Vijayakumar Ramachandran, Vidyasagar Ananta, Philip Maesor and Rajesh Manepalli. Indian provisional patent application entitled "ON-TOOL DYNAMIC CARE AREA GENERATION USING DESIGN", and Vijayakura Ramandran, Vidasagar Anantha, Philip Maesor as the inventor in the 15th application, filed in the 15th month / 198 Claims priority entitled U.S. Provisional Patent Application as "NOVEL AND EFFICIENT APPROACH FOR ON-TOOL DYNAMIC CARE AREA GENERATION USING DESIGN" 911 No., incorporated herein by reference in its entirety with the both applications.

  The inspection system generates defects on the wafer by identifying and classifying defects on the semiconductor wafer. A given semiconductor wafer can contain hundreds of chips, each chip containing thousands of interesting components, and each noted component can be hundreds on a given layer of chips. Can have tens of thousands of instances. As a result, the inspection system can generate an enormous number of data points (eg, hundreds of billions of data points in some systems) on a given wafer. Furthermore, the demand for devices that are constantly shrinking leads to an increase in demand for inspection systems. This demand includes the need for increased resolution and capability necessary to infer the root cause of identified defects without sacrificing inspection speed or accuracy.

  However, the use of design data related to the wafer typically affects the overhead of the inspection process and thus the throughput. For example, a utility for the creation of a care area based on design data may provide a large data file that specifies various attributes of the care area that must be transferred to the inspection tool. Further, the inspection tool may need to align the design data with the sample and correlate the design coordinates with the coordinates of the inspection tool.

US Patent Application Publication No. 2014/0105482

  Accordingly, it would be desirable to provide a system and method that corrects the shortcomings identified above.

  In accordance with one or more exemplary embodiments of the present disclosure, a defect inspection system is disclosed. In one exemplary embodiment, the system includes an inspection subsystem. In another exemplary embodiment, the inspection subsystem includes an illumination source configured to generate an illumination beam. In another exemplary embodiment, the inspection subsystem includes a set of illumination optics that direct the illumination beam onto the sample. In another exemplary embodiment, the inspection subsystem includes a detector configured to collect the radiation emitted from the sample. In another exemplary embodiment, the system includes a controller communicatively coupled to the detector. In another exemplary embodiment, the controller includes a memory device and one or more processors configured to execute program instructions. In another embodiment, the controller is configured to determine one or more target patterns corresponding to one or more features on the sample. In another embodiment, the controller is configured to define one or more care areas on the sample based on the one or more target patterns and the design data of the sample. In another exemplary embodiment, the specimen design data is stored in a memory device of the controller. In another embodiment, the controller is configured to identify one or more defects in one or more care areas of the sample based on the irradiation collected by the detector.

  In accordance with one or more exemplary embodiments of the present disclosure, a defect inspection system is disclosed. In one exemplary embodiment, the system includes an inspection subsystem. In another exemplary embodiment, the inspection subsystem includes an illumination source configured to generate an illumination beam. In another exemplary embodiment, the inspection subsystem includes a set of illumination optics for directing the illumination beam to the sample. In another exemplary embodiment, the inspection subsystem includes a detector configured to collect the radiation emitted from the sample. In another exemplary embodiment, the system includes a controller communicatively coupled to the detector. In another exemplary embodiment, the controller includes a memory device and one or more processors configured to execute program instructions. In another embodiment, the controller is configured to determine one or more target patterns corresponding to one or more features on the sample. In another embodiment, the controller is configured to determine a source pattern. In another exemplary embodiment, the source pattern approximates a subset of one or more target pattern instances in the sample design data. In another exemplary embodiment, the specimen design data is stored in a memory device of the controller. In another exemplary embodiment, the controller is configured to define a spatial relationship between the source pattern and at least one target pattern of a subset of one or more target pattern instances in the sample design data. In another exemplary embodiment, the controller is configured to identify one or more instances of the source pattern in the sample design data. In another exemplary embodiment, the controller may include a subset of the one or more target pattern instances in the sample design data, the one or more identified instances of the source pattern, and the source pattern and one or more target pattern instances. Are determined based on a spatial relationship between at least one target pattern of the subset. In another exemplary embodiment, the controller is configured to define one or more care areas on the sample based on a subset of one or more target pattern instances. In another exemplary embodiment, the controller is configured to identify one or more defects in one or more care areas of the sample based on the irradiation collected by the detector.

  In accordance with one or more exemplary embodiments of the present disclosure, a defect inspection method is disclosed. In one exemplary embodiment, the method includes providing sample design data to an inspection system. In another exemplary embodiment, the method includes determining one or more target patterns. In another exemplary embodiment, the one or more target patterns include design data related to one or more sample features to be inspected. In another exemplary embodiment, the method includes defining, by the inspection system, one or more care areas on the sample based on the one or more target patterns and the sample design data. In another exemplary embodiment, the method includes identifying one or more defects in one or more care areas of the sample.

  It should be understood that both the above general description and the following detailed description are exemplary and explanatory only and do not necessarily limit the claimed invention. The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the general description serve to explain the principles of the invention.

  Numerous advantages of the present disclosure will be better understood by those of ordinary skill in the art by reference to the accompanying drawings.

1 is a conceptual diagram illustrating an inspection system according to one or more embodiments of the present disclosure. FIG. 2 is a block diagram of an inspection tool of an inspection system illustrating the use of design data to generate a care area to be inspected based on design data stored in the inspection tool, according to one or more embodiments of the present disclosure. FIG. 3 is a flow diagram illustrating steps performed in a defect detection method according to one or more embodiments of the present disclosure. FIG. 6 is a schematic diagram of design data illustrating the definition of care areas associated based on source patterns according to one or more embodiments of the present disclosure. 2 is a conceptual diagram illustrating an optical inspection subsystem according to one or more embodiments of the present disclosure. FIG. FIG. 2 is a schematic diagram of an inspection subsystem utilizing one or more particle beams according to one or more embodiments of the present disclosure.

  Reference will now be made in detail to the disclosed subject matter illustrated in the accompanying drawings.

  Embodiments of the present disclosure are directed to an inspection system with on-tool generation of a sample care area. In this connection, the care area or selection area of the sample of interest may be generated directly on the inspection tool. Additional embodiments of the present disclosure are directed to identifying care areas on-tool based on the identification of one or more instances of a target pattern of interest in sample design data stored in an inspection tool. To do. For example, the target pattern may include design data relating to one or more sample features to be inspected. Additional embodiments of the present disclosure are directed to storing and pre-processing sample design data in an inspection system for efficient determination of a care area on an inspection tool. Furthermore, embodiments of the present disclosure are directed to identifying a subset of target pattern instances in a sample design data based on proximity to a source pattern in the design data. Therefore, care area generation involves searching for sample target data combinations extracted to include instances of the target pattern that are close to the source pattern based on a defined spatial relationship in the sample design data. May be included.

  It will be appreciated herein that the inspection tool typically only needs to inspect a subset of the sample surface for defects. The generation of the care area, or target area to be inspected of the sample, not only greatly improves the efficiency of defect detection by reducing the surface area to be inspected, but also the accuracy of defect inspection by reducing false signals and noise. Also greatly improve. Further, a care area may be defined to provide a targeted inspection analysis including, but not limited to, analysis of specific defect types or specific pattern elements placed across the sample.

  It is further recognized that the care area may be defined using sample design data (eg, physical layout of components on the sample, electrical connections between components on the sample, etc.). However, the use of design data typically affects overhead and therefore the throughput of the inspection process. For example, a utility for generating a care area based on design data specifies various attributes of the care area that must be transferred to the inspection tool (eg, the location of each care area on the sample, the shape of each care area, etc.) Large data files can be provided. In addition, the inspection tool must align design data (eg, alignment, scaling, etc.) with the sample in order to correlate design coordinates (eg, graphical design system (GDS) coordinates, etc.) with the coordinates of the inspection tool.

  Embodiments of the present disclosure are directed to storing a pre-processed version of sample design data in an inspection tool. In this regard, a design-based care area may be generated on the inspection tool using pre-processed design data (eg, a local version of the pre-processed design data). Further, in some embodiments, a design-based care area can be generated on an inspection tool without the need for further data transfer. Further, a care area based on the design generated on the inspection tool may be automatically aligned to the coordinates of the inspection tool.

  The term “sample” as used throughout this disclosure generally refers to a substrate (eg, a wafer, etc.) formed from a semiconductor or non-semiconductor material. For example, semiconductor or non-semiconductor materials may include, but are not limited to, single crystal silicon, gallium arsenide, and indium phosphide. The sample may include one or more layers. For example, such layers may include, but are not limited to, resists, dielectric materials, conductor materials, and semiconductor materials. Many different types of such layers are known in the art, and the term sample as used herein includes samples on which all types of such layers may be formed. Is intended. One or more layers formed on the sample may be patterned or unpatterned. For example, the sample may comprise a plurality of dies each having a repeatable patterned feature. The formation and processing of such a material layer may ultimately result in a completed device. Many different types of devices may be formed on a sample, and as used herein, the term sample refers to a wafer on which any type of device known in the art is fabricated. It is intended to be included. Further, for purposes of this disclosure, the terms sample and wafer should be interpreted as interchangeable. Further, for purposes of this disclosure, the terms patterning element, mask and reticle should be interpreted as interchangeable.

  FIG. 1 is a conceptual diagram illustrating an inspection system 100 according to one or more embodiments of the present disclosure. In one embodiment, inspection system 100 includes an inspection subsystem 102 for detecting defects on sample 110.

  It should be noted herein that the inspection subsystem 102 may be any type of inspection system known in the art that is suitable for detecting defects on the sample 110. For example, inspection subsystem 102 may include a particle beam inspection subsystem. Thus, the inspection subsystem 102 can detect one or more defects such that one or more defects can be detected based on detected radiation emitted from the sample 110 (eg, secondary electrons, backscattered electrons, luminescence, etc.). A particle beam (eg, electron beam, ion beam, etc.) may be directed to the sample 110. As another example, inspection subsystem 102 may include an optical inspection subsystem. Accordingly, the inspection subsystem 102 can detect light such that one or more defects can be detected based on detected radiation emitted from the sample 110 (eg, reflected radiation, scattered radiation, diffracted radiation, luminescent radiation, etc.). Radiation may be directed to the sample 110.

  The inspection subsystem 102 may operate in an imaging mode or a non-imaging mode. For example, in imaging mode, individual objects (eg, defects) can be resolved within the illuminated spot on the sample (eg, as part of a bright field image, dark field image, phase contrast image, etc.). In non-imaging mode operation, radiation collected by one or more detectors may be associated with a single illuminated spot on the sample to represent a single pixel of the sample 110 image. In this regard, an image of the sample 110 may be generated by acquiring data from an array of sample locations. In addition, the inspection subsystem 102 may analyze the radiation from the sample at the pupil plane to characterize the angular distribution of radiation from the sample 110 (eg, related to the scattering and / or diffraction of radiation by the sample 110). May operate as a scatterometry-based inspection system.

  In another embodiment, inspection system 100 includes a controller 104 coupled to inspection subsystem 102. For example, the controller 104 may be communicatively coupled to the detector 522. In this regard, the controller 118 may be configured to receive data including but not limited to inspection data from the inspection subsystem 102. In another embodiment, the controller 116 includes one or more processors 108. For example, one or more processors 108 may be configured to execute a set of program instructions maintained in memory device 108 or memory. The one or more processors 106 of the controller 104 may include any processing element known in the art. In this sense, the one or more processors 106 may include any microprocessor type element configured to execute algorithms and / or instructions. Further, the memory medium 108 may include any storage medium known in the art suitable for storing program instructions executable by one or more associated processors 108. For example, the memory medium 108 may include a non-transitory memory medium. As additional examples, the memory medium 108 may include, but is not limited to, read only memory, random access memory, magnetic or optical memory elements (eg, disks), magnetic tape, solid state drives, and the like. Further, the memory medium 108 may be housed in a controller housing common to one or more processors 108.

  Inspection system 100 may utilize any inspection technique known in the art for detecting defects associated with a sample. For example, defects on the sample 110 may be detected by comparing the measured sample properties (eg, generated by the inspection subsystem 102, etc.) with the measured properties of the reference sample (eg, die Comparison (D2D) inspection, standard reference die (SRD) inspection, etc.). As another example, a defect on sample 110 may be detected by comparing an inspection image of sample 110 with an image based on design characteristics (eg, die and database (D2DB) inspection). As a further example, inspection system 100 may include a virtual inspection system. In one embodiment, the controller 104 operates as a virtual inspection means. In this regard, the controller 104 may detect one or more defects on the sample 110 by comparing the inspection data of the sample with permanent reference data (eg, one or more reference images). For example, one or more reference images may be stored in the inspection system 100 (eg, in the memory 108) and used for defect detection. In another embodiment, the controller 104 generates and / or receives a simulated inspection image that serves as a reference image for defect detection based on design data related to the sample 110.

  An inspection system using design data is generally described in US Patent Application No. 2014/0153814 issued June 5, 2013, which is incorporated herein by reference in its entirety. Inspection systems that use persistent data (eg, stored data) are generally described in US Pat. No. 8,126,255 issued February 28, 2012, which is incorporated herein by reference in its entirety. Has been. Inspection systems that use sample design data to facilitate inspection are described in US Pat. Nos. 7,676,077 and November 2000, issued March 9, 2010, which are incorporated herein by reference in their entirety. This is generally described in US Pat. No. 6,154,714, issued on the 28th. Identification of the sources of defects and failures was issued on June 5, 2015, US Pat. No. 6,920,596, issued July 19, 2005, which is incorporated herein by reference in its entirety. U.S. Pat. No. 8,194,968 and U.S. Pat. No. 6,995,393 issued Feb. 7, 2006 are generally described. Device characterization and monitoring is generally described in US Pat. No. 8,611,639, issued December 17, 2013. The use of dual energy electron flooding for neutralization of charged substrates is generally described in US Pat. No. 6,930,309 issued Aug. 16, 2005, which is incorporated herein by reference in its entirety. Explained. The use of reticles in inspection systems is described in US Pat. No. 6,529,621 issued March 4, 2003, US patent issued June 8, 2004, which is incorporated herein by reference in its entirety. Generally described in US Pat. No. 6,748,103, and US Pat. No. 6,966,047 issued Nov. 15, 2005. Inspection process or inspection target generation is described in US Pat. No. 6,691,052 issued on Feb. 10, 2004, U.S. issued on July 26, 2005, which is incorporated herein by reference in its entirety. No. 6,921,672, generally described in US Pat. No. 8,112,241 issued February 7, 2012. The determination of critical areas for semiconductor design data is generally described in US Pat. No. 6,948,141 issued September 20, 2005, which is incorporated herein by reference in its entirety.

  FIG. 2 is a block diagram of the inspection tool 202 of the inspection system 100 showing the definition of a care area on the inspection tool 202 according to one or more embodiments of the present disclosure. In one embodiment, the inspection tool 202 includes one or more modules configured to perform one or more steps of the inspection tool 102. For example, one or more modules of the inspection tool 202 are not required, but may be implemented as one or more program instructions stored in the memory 108 and executed by the one or more processors 106.

  In another embodiment. The inspection tool 202 includes a design module 204. For example, the design module 204 may include design data relating to one or more samples 110 to be inspected by the inspection tool 202. In this regard, a care area may be generated on the inspection tool 202 using design data related to the sample 110. It should be noted herein that the direct generation of design-based care areas to the inspection tool 202 can facilitate efficient dynamic care area generation. For example, the creation of a care area based on the design on the inspection tool 202 may reduce data transfer (eg, defining a care area) between the inspection tool 202 and an external system. Furthermore, the creation of a care area based on the design on the inspection tool 202 facilitates the precise alignment of the coordinates related to the design data to the coordinates related to the sample and / or the inspection tool 202. For example, the design coordinates (eg, GDS coordinates, etc.) may need to be adjusted so that the size and orientation of the design pattern in the design data matches the printed pattern on the sample measured by the inspection tool 202 (eg, Scaling, rotation, etc.). Generation based on the design of the care area on the inspection tool can facilitate accurate and efficient alignment of the design coordinate system and the sample coordinate system.

  The term “design data” as used in this disclosure generally refers to the physical design of an integrated circuit and data derived from the physical design by complex simulations or simple geometric and Boolean operations. Further, the reticle image obtained by the reticle inspection system and / or its derivatives may be used as one or more proxies of design data. Such a reticle image or derivative thereof can serve as an alternative to the design layout in any of the embodiments described herein that use design data. For design data and design data proxy, see U.S. Patent No. 7,676,007 issued March 9, 2010 to Kulkarni, and U.S. Patent Application Serial No. 13 filed May 25, 2011 to Kulkarni. No. 115,957, U.S. Pat. No. 8,041,103 issued October 18, 2011 to Kulkarni and U.S. Pat. No. 7,570,796 issued to Zafar et al. All of the above patents are incorporated herein by reference. Further, the use of design data in advancing the inspection process is described in US Patent Application Serial No. 13 / 339,805 filed February 17, 2012, which is hereby incorporated by reference in its entirety. Is generally explained in the issue.

  The design data may include characteristics of individual components and / or layers (eg, insulators, conductors, semiconductors, wells, substrates) on the sample 110, connectivity relationships between layers on the sample 110, or connections to components on the sample 100. It may include a physical layout (eg, wire). In this regard, the design data may include a plurality of design pattern elements corresponding to the printed pattern elements on the sample 112.

  Note that the design data herein may include what is known as a “floor plan” that includes placement information about pattern elements on the sample 110. It is further noted that this information may be extracted from the physical design of the chip, usually stored in GDSII or OASIS file format. The structural behavior or process-design interaction may depend on the pattern element background (ambient environment). By using a floor plan, the proposed analysis can identify pattern elements in design data such as polygons that describe features that are to be built on the semiconductor layer. Furthermore, the proposed method can provide coordinate information of these repetitive blocks as well as background data (eg, the location of adjacent structures).

  In one embodiment, the design data includes one or more graphical representations (eg, visual representations, symbolic representations, graphical representations, etc.) of pattern elements. For example, the design data may include a graphical representation of the physical layout of the component (eg, a description of one or more polygons corresponding to the printed pattern elements produced on the sample 110). Further, the design data may include one or more layers of the sample design (eg, one or more layers of printed pattern elements fabricated on the sample 110) or a graphical representation of connectivity between one or more layers. As another example, the design data may include a graphical representation of the electrical connectivity of components on the sample 110. In this regard, the design data may include a graphical representation of one or more circuits or subcircuits related to the sample. In another embodiment, the design data includes one or more image files that include a graphical representation of one or more portions of the sample 110.

  In another embodiment, the design data includes one or more text descriptions (eg, one or more lists, one or more tables, one or more databases, etc.) of the pattern element connectivity of the sample 110. For example, the design data may include, but is not limited to, netlist data, circuit simulation data, or hardware description language data. The netlist may include any type of netlist known in the art that provides a description of electrical circuit connectivity, including but not limited to physical netlists, logical netlists, instance-based netlists. Contains a list or net-based netlist. In addition, the netlist may include one or more subnet lists (eg, in a hierarchical structure) that describe the circuit and / or subcircuits of the sample 110. For example, netlist data related to a netlist is not limited, but includes a list of nodes (eg, nets, wires between circuit components), a list of ports (eg, terminals, pins, connectors, etc.), and electrical power between nets. Description of components (eg resistors, capacitors, inductors, transistors, diodes, power supplies, etc.), values related to electrical components (eg resistance values in ohms of resistors, voltage values in volts of power supply, frequency of voltage source) Characteristics, initial state of components, etc.). In another embodiment, the design data may include one or more netlists related to specific steps in the semiconductor process flow. For example, the sample 110 may be inspected (eg, by the system 100) at one or more midpoints in the semiconductor process flow. Thus, the design data utilized to create the care area may be specific to the current sample 110 layout of the semiconductor process flow. In this context, the netlist associated with a particular intermediate point in the semiconductor process flow is either the physical design layout (layer connection, electrical characteristics of each layer, etc.) combined with the technical file, or the final layout of the sample 110. From any of the related netlists, it may be derived (eg, extracted, etc.) to include only the components present on the wafer at a particular midpoint in the semiconductor process flow.

  In another embodiment, the design module 204 of the inspection tool 202 performs step 206 of preprocessing design data. It should be noted herein that design data may include data that is unrelated to the determination of the care area of inspection system 100 (eg, production data, etc.). Further, the design data may not be in a format suitable for efficient identification (for example, search, matching, etc.) of a pattern element to be noted in the design data. Accordingly, the pre-processed design data may include a version of the pre-processed design data to facilitate efficient generation of care areas on the inspection tool 202. In this regard, the pre-processed design data can help identify one or more instances (eg, pattern elements to note, hot spots, etc.) of the target pattern in the design data. For example, the pre-processed design data includes, but is not limited to, a target pattern identifier, target pattern electrical characteristics, target pattern physical characteristics, or target pattern and one or more additional patterns (eg, anchor pattern). , Source pattern, etc.) or any combination of design data elements including a graphical representation of the target pattern.

  In another embodiment, design data (eg, raw design data, pre-processed design data, or a combination thereof) is stored by inspection tool 202. For example, the design data may be stored in the memory element 108 of the controller 104. In another embodiment, the design data may be preprocessed outside the inspection system 100 and stored in the inspection tool 202. In this regard, pre-processed design data relating to one or more samples may be transferred to the inspection tool 202.

  In another embodiment, the design module 204 performs the step of analyzing design data stored in the inspection tool 202. In this regard, the design module 204 may identify one or more instances of the target pattern in the sample design data (eg, search for preprocessed design data for one or more target pattern instances). Etc.) In addition, the design module 204 may provide parameters for the identified instance of the target pattern necessary to generate the care area, such as, but not limited to, the coordinates and / or shape of the identified target pattern.

  In one embodiment, the inspection tool 202 includes a recipe module 210. For example, the recipe module 210 may generate a recipe for one or more inspection steps by the inspection tool 202. In this regard, the recipe includes, but is not limited to, a description of one or more care areas to inspect for defects, one or more alignment operations (eg, coordinates related to design data, sample and / or inspection sub It may include one or more defect identification steps or one or more defect classification steps), such as aligning and / or scaling to coordinates related to the system 102. Additionally, the generation of care areas based on the design on the inspection tool 202 may facilitate an efficient multi-step inspection process (eg, systematic defect discovery, etc.). In this regard, design data can be retrieved with iterative inspection analysis for different target patterns or combinations of target patterns on inspection tool 202 without the need for data transfer to external systems.

  In another embodiment, the recipe module 210 may include one or more target patterns (eg, one or more notable pattern elements) that are related to fabricated pattern elements on the sample 110 to be inspected by the inspection tool 202 in the inspection step. Step 212 of determining one or more hot spots, etc.) is performed. For example, the recipe module 210 may provide one or more target patterns based on one or more purposes of one inspection process of the inspection tool 202. For example, the recipe module 210 may provide a target pattern related to a known defect type to be noted.

  In another embodiment, the recipe module 204 determines one or more target patterns in an automated process. For example, the recipe module 204 may determine one or more target patterns that may exhibit defects by analyzing the design data 208 of the sample 110 (e.g., to physical layout, pattern size, other patterns). Based on characteristics related to the degree of approximation, circuit complexity, etc.).

  In another embodiment, the determination of the target pattern is facilitated by the user. For example, the user may include, but is not limited to, an inspection tool that includes one or more defect identifiers, one or more GDS coordinates, one or more design-based classification (DBC) clips, or one or more design-based grouping (DBG) bins. 202 may be provided with input (eg, input to recipe module 210). In this regard, the recipe module 210 may determine one or more target patterns based on the user input. In another embodiment, the inspection tool 202 may provide a visual display related to design data (eg, within the design view of the inspection tool tool 202). In this regard, the user may select one or more target patterns from a visual display of design data. For example, the visual display may be a graphical display (eg, pattern elements related to the physical layout of components, pattern elements related to electrical connections between components, etc.) of design data (eg, pattern elements related to the physical layout of the components). Display of images and the like). As another example, the visual display may include a text-based display on which design data may be displayed. In another embodiment, the user may visualize (eg, on a graphical display) design data according to a coordinate system (eg, GDS coordinates) to determine and / or confirm one or more target patterns. For example, a user may enter coordinates (eg, on an input device of the inspection system 100) to visualize and / or confirm design data at a particular location that generates a target pattern for inspection.

  In another embodiment, the recipe module 210 performs step 214 of defining one or more care areas for subjects to be examined on the sample 110. For example, the recipe module 210 may define one or more care areas based on one or more target patterns and design data stored in the inspection tool 202. In one embodiment, recipe module 210 may interact with design module 204 to analyze design data based on one or more determined target patterns. In this regard, the recipe module 210 may provide one or more target patterns to the design module 204 for pattern matching. In addition, the design module 204 identifies one or more instances of the target pattern in the design data and provides the recipe module 110 with any parameters needed to generate a care area based on the identified target pattern instance. To do. For example, the design module 204 may provide the location of the identified instance of the target pattern (eg, in design coordinates), the shape of the identified instance of the target pattern, the contour of the identified instance of the target pattern, etc. .

  In another embodiment, recipe module 210 performs step 216 to identify defects on sample 110. In this regard, the recipe module 210 may interact with the inspection subsystem 102 to perform a defect inspection. Further, the recipe module 210 may determine the presence of one or more defects by analyzing data received by the inspection subsystem 102. Additionally, the recipe module 210 may characterize one or more defects. For example, the recipe module is not essential, but the defect may be characterized based on a DBC system, a DBG system, or the like. Further, the recipe module 210 may assign one or more defect identifiers to one or more characterized defects.

  As used herein, steps described throughout this disclosure (eg, steps related to modules of inspection tool 202, etc.) may be performed by a single controller 104, or alternatively by multiple controllers 104. Is recognized. Further, it should be noted herein that one or more controllers 104 may be located proximate to the inspection subsystem 102. Additionally, one or more controllers 104 may be housed in a common housing with the inspection subsystem 102. Further, any controller or combination of controllers may be separately implemented as a module suitable for incorporation into the complete inspection system 100. For example, the first controller may be configured to perform steps related to the design module 204. The one or more additional controllers may then be configured to perform the steps associated with the recipe module 210. In this regard, one or more controllers 104 may be incorporated into the inspection system 100.

  FIG. 3 is a flow diagram illustrating steps performed in a method 300 for defect detection according to one or more embodiments of the present disclosure. Applicants note that the embodiments previously described herein in the context of the system 100 and the techniques enabling it should be construed to extend to the method 300. . However, it should also be noted that the method 300 is not limited to the architecture of the system 100.

  In one embodiment, the method 300 includes providing 302 sample design data to an inspection system. For example, the design data is not essential, but may be provided to the inspection system in the form of one or more data files (eg, GDSII file, OASIS file, etc.). In this regard, design data provided to the inspection system may be utilized to generate an inspection care area based on one or more designs.

  In another embodiment, the method 300 includes determining 304 one or more target patterns. In this regard, one or more notable target patterns related to features fabricated on the sample may be provided for inspection. For example, the target pattern may be one or more polygons (eg, cross, plus, L, T, square, rectangular or of specific dimensions that represent features to be built on the semiconductor layer. One or more instances of other polygons, and the spacing between instances).

  In one embodiment, the one or more target patterns are determined based on the defect identifier. In this regard, the defect type associated with one or more known defects or defect identifiers (eg, one or more identifiers used to classify one or more defects) may be one or more specific target patterns ( For example, based on one or more previous inspection steps, based on one or more design characteristics, etc. Thus, the occurrence of a known defect or defect type may be characterized by providing a corresponding target pattern for inspection.

  In another embodiment, one or more target patterns are determined (eg, by inspection system 100 or an additional inspection system) based on previous inspection steps. For example, in the discovery of systematic defects, a first inspection performed on sample 110 or a portion of sample 110 may identify that one or more fabricated components of sample 110 are prone to defects. . In this regard, the first inspection process may determine one or more target patterns related to the identified fabricated component on the sample 110. Further, the second inspection process may include a recipe (eg, generated by the recipe module 210) for performing a dedicated inspection of one or more target patterns identified from the first inspection process. In another embodiment, the one or more target patterns are determined according to a DBC or DBG process related to previous inspection steps.

  In another embodiment, the one or more target patterns may be determined based on one or more coordinates (eg, GDS coordinates, etc.) of the target pattern on the sample. For example, one or more target patterns may be determined based on known coordinates of one exemplary target pattern of interest related to design data. As another example, the one or more target patterns may be determined based on known coordinates of exemplary fabricated components on the sample 110. In this way, one or more target patterns related to exemplary fabricated components can be provided for inspection.

  In another embodiment, the method 300 includes a step 306 of defining, by the inspection system, one or more care areas on the sample based on the target pattern and the sample design data. In this regard, step 306 may include defining one or more regions on the sample to be examined. For example, the care area may include coordinates on a sample to be inspected (eg, in the coordinate system of the inspection system).

  In another embodiment, the care area includes one or more target areas on the sample to be examined. For example, the first target region may include one or more instances of the first target pattern identified in step 304, and the second target region may be another of the second target pattern identified in step 304. One instance or the like may be included. In addition, the definition of one or more target areas can facilitate a highly sensitive inspection of the sample 110. For example, the target area may be defined to include samples having equivalent sensitivity levels. Thus, each target area may be inspected with a different sensitivity threshold so that the contrast of the inspection data related to each target area can be increased.

  In another embodiment, step 306 includes one or more instances of the target pattern determined in step 304 in the design data (eg, preprocessed design data stored in memory element 108 of inspection system 100). Including identifying. In this regard, each identified instance of the target pattern of interest may be included in a care area. In addition, noteworthy target pattern deformations (eg, horizontal and / or vertical flips of the target pattern, scaled versions of the target pattern, rotated versions of the target pattern, etc.) are identified in step 306 to provide care areas May be included.

  Instances of target patterns in device data may be identified using any method known in the art. For example, step 306 may include searching design data for one or more instances of the target pattern to generate one or more identified instances of the target pattern. In one embodiment, step 306 includes a text-based search of design data. For example, text-based design data (eg, one or more lists, one or more tables, one or more databases, one or more data files, etc.) may be searched according to one or more characteristics of the target pattern. In another embodiment, step 306 includes an image-based search for design data. For example, one or more instances of a target pattern (or target pattern variation) may include, but are not limited to, images such as feature extraction techniques, convolution techniques, pattern matching techniques, spatial frequency analysis, transformation techniques (eg, Hough transform techniques, etc.) It may be found by a processing algorithm. In addition, multiple design layers of design data (eg, corresponding to multiple layers of manufactured components on the sample 110) may be individually searched for one or more instances of the target pattern of interest.

  In one embodiment, the target pattern may be specified using design data included in a design layout file such as OASIS or GDS. As used herein, the target pattern may vary in size and may be placed at various levels of design data (eg, related to various layers, dies, blocks, cells, etc. of the sample 110). Please note that. In this regard, the target pattern in the design data may be specified in a known or observed design cell hierarchy. For example, the design cell hierarchy may be analyzed to identify a repeating group of target patterns within a given set of inspection data.

  In another embodiment, the target pattern may be identified using a design rule verification (DRC) process, an optical rule verification (ORC), or a failure analysis (FA) process to identify target patterns that are important to device performance. Good. In another embodiment, the target pattern may be identified using a process window qualification (PWQ) method. Retrieving design data for one or more target patterns may be performed as described in the above references by Kulkarni et al. And Zafar et al., Incorporated above by reference.

  In some embodiments, target patterns may be identified on a semiconductor wafer using data from electronic design automation (EDA) tools and other knowledge. Any such information regarding the design generated by the EDA tool may be used to identify the iterative block. Furthermore, the design data may be retrieved in any suitable manner for one or more target patterns. For example, retrieving design data for one or more target patterns is described in the above-referenced patent applications by Kulkarni et al. And Zafar et al., Incorporated by reference above. Furthermore, the target pattern may be selected or specified using any other method or system described in this patent application.

  Further, the design data may be analyzed to identify an appropriate target pattern to be inspected based on a given technique (eg, optical inspection, e-beam inspection, etc.).

  It will be appreciated that the target pattern may be repeated through the sample 110 die to form a repeating block (or field). Further, the sample 110 cell may sometimes be repeated through a given die with a different name, or repeated at multiple locations under a single name. In some embodiments, the repeating cells may be aligned on the same horizontal and / or vertical axis. In another embodiment, the repeating cells are not aligned on the same horizontal and / or vertical axis.

  In another embodiment, step 306 includes providing a confidence metric related to the identification of each instance of the target pattern of interest at a location in the design data. In this regard, an instance of the target pattern in the device data may include an exact match (eg, 100% confidence metric, etc.) or a substantial match (eg, less than 100% confidence metric). It should be understood that any confidence metric in the art is within the spirit and scope of the present disclosure. For example, the confidence metric may range from 0 (no match) to 1 (perfect match).

  It should be noted that a care area may be defined herein to include a subset of identified target pattern instances. For example, the possibility that a particular defect on a particular component of a device fabricated on sample 110 may induce performance degradation includes, but is not limited to, the presence of adjacent structures or operating conditions of a particular component. May depend on a number of factors such as

  In one embodiment, step 306 includes defining one or more care areas to include instances of target patterns (eg, source patterns, anchor patterns, etc.) proximate to additional patterns in the design data. In this regard, the presence of the source pattern may serve as a filter to provide a subset of the target pattern instances as a care area to be examined.

  FIG. 4 is a schematic diagram of design data showing the definition of care areas associated based on source patterns according to one or more embodiments of the present disclosure.

  In one embodiment, design data 402 includes a plurality of target pattern instances 404. Further, the design data 402 includes a source pattern 408 that approximates a subset of instances of the target pattern 404 (eg, a particular instance 412 of the target pattern 404). For example, the source pattern may include, but is not limited to, one or more instances of intersection, cross, plus, L, T, square, rectangle, or other polygons of a particular dimension, and the spacing between instances. May be included.

  Further, step 306 includes defining a care area 406 around a particular instance 412 of the target pattern 404 based on the spatial relationship between the particular instance 412 of the target pattern 404 and the source pattern 408. It's okay. For example, the spatial relationship between a particular instance 412 of the target pattern 404 and the source pattern 408 includes, but is not limited to, a vector 414 between the particular instance 412 of the target pattern 404 and the source pattern 408. It's okay. In another embodiment, step 306 searches for one or more instances of the source pattern in the design data and further sub-sets of target pattern instances to include in the care area (eg, a particular instance 412 of the target pattern 404). Identifying based on a spatial relationship between a particular instance 412 of the target pattern 404 and the source pattern 408. In another embodiment, step 306 may include a subset of target pattern 404 instances related to the identified composite target pattern 410 with respect to the composite target pattern 410 instance that includes the source pattern 408 and an instance of the target pattern in the device data 402. Including searching while defining a care area 406 around (e.g., a particular instance 412 of the target pattern 404). Accordingly, the source pattern 408 may be used as part of the search step without being included in the related care area 406.

  In another embodiment, the method 300 includes identifying 308 one or more defects in one or more care areas of the sample. In this regard, the inspection system (eg, inspection system 100) inspects the sample care area defined in step 308 for defects (eg, using the illumination subsystem 101). For example, data from the inspection subsystem 102 may be analyzed to determine the presence of one or more defects on the sample 112 related to the care area defined at step 306. Furthermore, the identified defects may be classified (eg, according to defect identifiers, DBC clips, DBG bins, etc.). In another embodiment, data related to one or more identified defects may be provided to inspection system 100 and / or external systems (eg, as feedforward data, feedback data, etc.).

  It is recognized herein that the steps described throughout this disclosure may be performed by a single controller 104 or alternatively may be performed by multiple controllers 104. It is further noted herein that one or more controllers 104 may be housed in a common housing or multiple housings. Thus, any controller or combination of controllers may be individually implemented as a module suitable for integration into the complete inspection system 100. As a non-limiting example, the first controller may be configured to perform a step of identifying a set of illumination detection events based on the illumination signal received from the illumination sensor. The one or more additional controllers then identify a set of radiation detection events based on the one or more radiation signals received from the one or more radiation sensors, and set the radiation detection events to the set of radiation detection events. Generating a set of simultaneous events as compared to and generating a set of identified features on the sample by excluding the set of simultaneous events from the set of irradiation detection events May be.

  FIG. 5A is a conceptual diagram of an inspection subsystem 102 configured as an optical inspection subsystem according to one or more embodiments of the present disclosure. In one embodiment, the inspection subsystem 102 includes an illumination source 502. The illumination source 502 may include any illumination source known in the art suitable for generating an illumination beam 504 (eg, a beam of photons). For example, the illumination source 502 may include, but is not limited to, a monochromatic light source (eg, a laser), a multicolor light source with a spectrum that includes two or more discrete wavelengths, a broadband light source, or a wavelength swept light source. Further, the irradiation source 502 includes, but is not limited to, a white light source (for example, a broadband light source having a spectrum including a visible wavelength), a laser source, a free-form irradiation source, a monopolar irradiation source, a multipolar irradiation source, an arc lamp, an It may be formed from an electrode lamp or a laser sustained plasma (LSP) source. Further, the illumination beam 504 may be supplied via free space propagation or guide light (eg, optical fiber, light pipe, etc.).

  In another embodiment, the illumination source 502 directs one or more illumination beams 504 to the sample 110 via the illumination path 506. The irradiation path 506 may include one or more lenses 510. Further, the illumination path 506 may include one or more additional optical components 508 suitable for modifying and / or adjusting one or more illumination beams 504. For example, the one or more optical components 508 include, but are not limited to, one or more polarizers, one or more filters, one or more beam splitters, one or more diffusers, one or more homogenizers, one or more apodizers, or one or more. Other beam shapers. In one embodiment, the illumination path 506 includes a beam splitter 514. In another embodiment, the inspection subsystem 102 includes an objective lens 516 for focusing one or more illumination beams 504 onto the sample 110.

  The irradiation source 502 may direct one or more irradiation beams 504 to the sample at an arbitrary angle via the irradiation path 506. In one embodiment, as shown in FIG. 5A, the illumination source 502 directs one or more illumination beams 504 to the sample 110 at a normal incidence angle. In another embodiment, the illumination source 502 directs one or more illumination beams 504 to the sample 110 at a non-normal incidence angle (eg, viewing angle, 45 degree angle, etc.).

  In another embodiment, the sample 110 is placed on a sample stage 512 suitable for securing the sample 110 during scanning. In another embodiment, the sample stage 512 is a drivable stage. For example, sample stage 512 includes, but is not limited to, one or more translational movements suitable for selectively translating sample 110 along one or more linear directions (eg, x, y, and / or z directions). A stage may be included. As another example, the sample stage 512 may include, but is not limited to, one or more rotation stages suitable for selectively rotating the sample 110 along one rotational direction. As another example, the sample stage 512 includes, but is not limited to, a translation stage and translation suitable for selectively translating the sample along a linear direction and / or rotating the sample 110 along one rotational direction. A moving stage may be included.

  In another embodiment, the illumination path 506 includes one or more beam scanning optics (not shown) suitable for scanning the illumination beam 504 across the sample 110. For example, the one or more illumination paths 506 include, but are not limited to, one or more electro-optic beam deflectors, one or more acousto-optic beam deflectors, one or more galvanometer scanners, one or more resonant scanners, or one or more. Any type of beam scanner known in the art, such as a polygon scanner, may be included. Thus, the surface of the sample 110 may be scanned with an r-θ pattern. It should further be noted that the illumination beam 504 may be scanned according to any pattern on the sample. In one embodiment, the illumination beam 504 is split into one or more beams so that the one or more beams can be scanned simultaneously.

  In another embodiment, the inspection subsystem 102 can include one or more detectors 522 (eg, one or more optical detectors, one or more optical detectors) configured to capture radiation emitted from the sample 110 via the collection path 518. Photon detector etc.). Collection path 518 directs the radiation collected by objective lens 516, including but not limited to one or more lenses 520, one or more filters, one or more polarizers, one or more beam blocks, or one or more beam splitters. Multiple optical elements may be included for attaching and / or modifying. It should be noted herein that the components of collection channel 518 may be oriented at any location relative to sample 110. In one embodiment, the collection path includes an objective lens 516 oriented normal to the sample 110. In another embodiment, the collection path 518 includes a plurality of collection lenses oriented to collect radiation from the sample at a solid angle.

  In one embodiment, inspection system 100 includes a bright field inspection system. For example, a bright field image of sample 110 or a portion of sample 110 may be projected onto detector 522 (eg, by objective lens 516, one or more lenses 520, etc.). In another embodiment, inspection system 100 includes a dark field inspection system. For example, the inspection system 100 may include one or more components (for directing the illumination beam 504 at a large angle of incidence on the sample 110 such that an image of the sample on the detector 112 is associated with scattered and / or diffracted light. For example, an annular beam block, dark field objective lens 516, etc.) may be included. In another embodiment, the inspection system 100 includes a bevel inspection system. For example, the inspection system 100 may direct the illumination beam 504 to the sample at an off-axis angle to provide contrast for defect inspection. In another embodiment, inspection system 100 includes a phase contrast inspection system. For example, inspection system 100 may include one or more phase plates and / or beam blocks (eg, to provide a phase contrast between diffracted and non-diffracted light from a sample to provide contrast for defect inspection. An annular beam block or the like may be included. In another embodiment, inspection system 100 may include a luminescence inspection system (eg, a fluorescence inspection system, a phosphorescent inspection system, etc.). For example, inspection system 100 may direct illumination beam 504 having a first wavelength spectrum to sample 110 and one or more additional wavelength spectra emanating from sample 110 (eg, one or more of sample 110). And / or one or more filters that detect (e.g., emitted from one or more defects on the sample 110). In another embodiment, the inspection system includes one or more pinholes positioned at a confocal location such that system 100 operates as a confocal inspection system.

  FIG. 5B is a schematic diagram of an inspection subsystem configured as a particle beam inspection subsystem according to one or more embodiments of the present disclosure. In one embodiment, the illumination source 502 includes a particle source configured to generate a particle beam 504. The particle source 502 may include any particle source known in the art suitable for generating the particle beam 504. As a non-limiting example, the particle source 502 may include, but is not limited to, an electron gun or an ion gun. In another embodiment, the particle source 502 is configured to provide adjustable energy to the particle beam 504. For example, a particle source 502 including an electron source may provide an acceleration voltage between 0.1 kV and 30 kV. As another example, a particle source including an ion source may provide an ion beam with an energy value in the range of 1 to 50 KeV, although not required.

  In another embodiment, the inspection subsystem 102 includes two or more particle beam sources 502 (eg, an electron beam source or an ion beam source) for the generation of two or more particle beams 504.

  In another embodiment, the irradiation path 506 includes one or more particle focusing elements 524. For example, the one or more particle focusing elements 524 may include one or more particle focusing elements that form a single particle focusing element or compound system. In another embodiment, the objective lens 516 of the system 100 is configured to direct the particle beam 504 to the sample 110. Further, the one or more particle focusing elements 524 and / or the objective lens 516 may be any type of particle lens known in the art, including but not limited to electrostatic, magnetic, single potential or dual potential lenses. May be included. Further, the inspection subsystem 102 may include, but is not limited to, one or more electronic deflectors, one or more apertures, one or more filters, or one or more astigmatism correction devices.

  In another embodiment, the inspection subsystem 102 includes one or more particle beam scanning elements 526. For example, the one or more particle beam scanning elements may include one or more scanning coils or deflectors suitable for controlling the position of the beam relative to the surface of the sample 110. In this regard, one or more scanning elements may be used to scan the particle beam 504 across the sample 110 in a selected pattern.

  In another embodiment, the inspection subsystem includes a detector 522 for imaging or detecting particles 528 emitted from the sample 110. In one embodiment, detector 522 includes an electron collector (eg, a secondary electron collector, a backscattered electron detector, etc.). In another embodiment, detector 522 is coupled to a photon detector (eg, a photo detector, x-ray detector, photomultiplier tube (PMT) detector) for detecting electrons and / or photons from the sample surface. Flash device etc.). In a general sense, it is recognized herein that detector 522 can include any device or combination of devices known in the art for characterizing a sample surface or bulk with particle beam 504. The For example, detector 522 was configured to collect backscattered electrons, Auger electrons, transmitted electrons or photons (eg, x-rays emitted by the surface in response to incident electrons, cathodoluminescence of sample 108, etc.). Any particle detector known in the art may be included.

  In another embodiment, inspection system 100 includes a voltage contrast imaging (VCI) system. As used herein, an inspection system that uses a particle beam (eg, electron beam, ion beam, etc.) to detect and / or identify a defect mechanism in a semiconductor sample (eg, random logic chip) with high achievable spatial resolution. It is understood that it is particularly useful. For example, a particle beam may be used in an inspection system to image a sample (eg, by capturing secondary electrons, backscattered electrons, etc. emitted from the sample). Furthermore, structures on the sample (eg, patterned semiconductor wafer) may exhibit a charging effect in response to excitation with a particle beam. The charging effect may include changing the number of electrons (eg, secondary electrons) captured by the system, and thus changing the VCI signal strength. In this regard, a voltage contrast imaging (VCI) system may generate a high resolution image of the sample where the intensity of each pixel of the image provides data on the electrical properties of the sample at the pixel location. . For example, an insulating structure or a structure that is not connected to a grounded source (eg, not grounded) is charged (eg, positively charged) in response to depletion of particles (eg, secondary electrons, ions, etc.) induced by the particle beam. Or negative charge). Thus, the induced charge can deflect the trajectory of the secondary electrons and reduce the signal intensity captured by the detector. Conversely, a grounded structure does not generate charge and can therefore exhibit a strong signal (eg, it appears bright in the associated VCI image). Furthermore, the signal strength of the capacitive structure can be a function of the scanning speed and / or the energy of the particle beam. In this regard, the VCI image may include a grayscale image, and the grayscale value for each pixel provides data regarding the relative electrical characteristics of that location on the wafer. In another embodiment, the inspection system 100 includes one or more components (eg, one or more electrodes) configured to apply one or more voltages to one or more locations of the sample 108. In this regard, the system 100 may generate active voltage contrast imaging data.

  In another embodiment, the inspection system 100 may include a display (not shown). In another embodiment, the display is communicatively coupled to controller 104. For example, the display may be communicatively coupled to one or more processors 104 of controller 101. In this regard, one or more processors 106 may display one or more of the various results of the present invention on a display.

  The display device may include any display device known in the art. In one embodiment, the display device may include, but is not limited to, a liquid crystal display (LCD). In another embodiment, the display device may include, but is not limited to, an organic light emitting diode (OLED) based display. In another embodiment, the display device may include, but is not limited to, a CRT display. Those skilled in the art will recognize that a wide variety of display devices may be suitable for implementation in the present invention, and the particular choice of display device may depend on various factors including, but not limited to, form factor, cost, etc. You should understand that. In general terms, any display device that can be integrated into a user interface device (eg, touch screen, bezel-mounted interface, keyboard, mouse, trackpad, etc.) is suitable for implementation in the present invention. .

  In another embodiment, the inspection system 100 may include a user interface device (not shown). In one embodiment, the user interface device is communicatively coupled to one or more processors 106 of controller 104. In another embodiment, the user interface device may be utilized by the controller 104 to accept selections and / or instructions from the user. In some embodiments further described herein, the display may be used to display data to the user. The user may then enter selections and / or instructions (eg, user selection of the inspection area) in response to inspection data displayed to the user via the display device.

  The user interface device may include any user interface known in the art. For example, the user interface includes, but is not limited to, keyboard, keypad, touch screen, lever, knob, scroll wheel, trackball, switch, dial, slide bar, scroll bar, slide, handle, touch pad, paddle, steering wheel Joysticks, bezel input devices, and the like. In the case of touch screen interface devices, those skilled in the art will recognize that many touch screen interface devices may be suitable for implementation in the present invention. For example, the display device may be integrated into a touch screen interface such as, but not limited to, capacitive touch screens, resistive touch screens, surface elastic based touch screens, infrared based touch screens and the like. In a general sense, any touch screen interface that can be integrated with the display portion of the display device 105 is suitable for implementation in the present invention. In another embodiment, the user interface may include, but is not limited to, a bezel-mounted interface.

  It should be noted herein that FIGS. 5A and 5B and the corresponding description above are provided for illustration only and should not be construed as limiting. It is envisioned that several equivalent or additional configurations can be utilized within the scope of the present invention.

  The subject matter described herein illustrates separate components that are sometimes contained within or connected to other components. It should be understood that such a depicted architecture is merely exemplary and in fact many other architectures that achieve the same functionality can be implemented. In a conceptual sense, any arrangement of components that achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Thus, any two components combined herein to achieve a particular functionality are considered “associated” with each other to achieve the desired functionality, regardless of their architecture or intermediate components. Can be made. Similarly, any two components so associated may also be considered “connected” or “coupled” to each other to achieve the desired functionality, and Any two components that can be associated with can also be considered “combinable” with each other to achieve the desired functionality. Specific examples of combinable include, but are not limited to, physically interactable and / or physically interacting components and / or wireless interactable and / or wireless interacting components and / or logically Includes components that can interact and / or interact logically.

  Many of the advantages of the present invention and related matters will be understood by the foregoing description, without departing from the disclosed subject matter or without sacrificing all of its substantial advantages, It will be apparent that various changes can be made in configuration and arrangement. The described forms are merely illustrative, and it is the intention of the following claims to encompass and include such changes. Furthermore, it is to be understood that the invention is defined by the appended claims.

Claims (47)

A defect inspection system,
An inspection subsystem, the inspection subsystem
An illumination source configured to generate an illumination beam;
A set of illumination optics for directing the illumination beam to the sample;
A detector configured to collect the radiation emitted from the sample;
A controller communicatively coupled to the detector, the controller including a memory device and one or more processors configured to execute the program instructions, the program instructions to the one or more processors;
Determining one or more target patterns corresponding to one or more features on the sample;
One or more care areas on the sample are defined based on the one or more target patterns and the sample design data, the sample design data being stored in the memory device of the controller,
Identifying one or more defects in one or more care areas of the sample based on the irradiation collected by the detector;
Defect inspection system configured as follows. Defining one or more care areas on the sample includes
The defect inspection system according to claim 1, comprising identifying one or more instances of the one or more target patterns in sample design data. Identifying one or more instances of the one or more target patterns in the design data of the sample,
Generating a synthetic search pattern, the synthetic search pattern including a source pattern and at least one target pattern of the one or more target patterns;
Identifying one or more instances of one or more target patterns in the sample design data;
The defect inspection system of claim 2, comprising identifying one or more instances of the one or more target patterns in the design data of the sample based on the one or more identified instances of the composite pattern. The one or more processors further includes the one or more processors,
Pre-processing the design data of the sample by the inspection system to facilitate identification of the sample of one or more instances of the one or more target patterns in the design data;
The defect inspection system of claim 2, wherein the defect inspection system is configured to execute program instructions. Identifying one or more instances of one or more target patterns in the design data of the sample
The defect inspection system of claim 2, comprising retrieving design data for one or more target patterns to generate one or more identified instances of the one or more target patterns.   The one or more identified instances of the one or more target patterns are one or more confidence scores related to the similarity between the one or more target patterns and the one or more identified instances of the one or more target patterns. The defect inspection system according to claim 5, comprising: Defining one or more care areas on the sample includes
Defining one or more target areas, wherein the one or more target areas include at least one instance of at least one of the one or more target patterns, and the care area includes the one or more target areas. Including,
The defect inspection system according to claim 1. Inspecting the one or more care areas of the sample includes
The defect inspection system according to claim 7, comprising inspecting the one or more target areas, wherein the inspection sensitivity of the one or more target areas is individually adjustable. The one or more target patterns are:
Including one or more target patterns identified by at least one of design-based classification or design-based binning processes;
The defect inspection system according to claim 1. The one or more target patterns are:
The defect inspection system of claim 1, comprising one or more target patterns related to one or more known defect types. The one or more target patterns are:
The defect inspection system of claim 1, comprising one or more target patterns identified by a previous defect inspection process. Determining the one or more target patterns includes
The defect inspection system of claim 1, comprising determining the one or more target patterns by a user. The determination of the one or more target patterns by the user is
The defect inspection system of claim 12, comprising selecting one or more instances of the one or more target patterns from sample design data. Selecting one or more instances of the one or more target patterns from sample design data;
The defect inspection system of claim 13, comprising selecting one or more instances of the one or more target patterns from a visual display of sample design data. Determining the one or more target patterns includes
The defect inspection system of claim 1, comprising providing one or more coordinates related to one or more instances of the one or more target patterns in sample design data. Providing one or more coordinates related to one or more instances of the one or more target patterns in the design data of the sample;
The defect inspection system of claim 15, comprising providing graphic data system coordinates of the one or more instances of the one or more target patterns.   The defect inspection system according to claim 1, wherein the inspection system is a virtual inspection system. The design data is
The defect inspection system according to claim 1, comprising at least one of a physical layout of the sample or an electrical layout of the sample.   The defect inspection system of claim 1, further comprising providing one or more care areas for use in a subsequent inspection process.   The defect inspection system of claim 1, further comprising classifying the one or more identified defects based on sample design data. The irradiation beam is
The defect inspection system of claim 1, comprising at least one of a photon beam or a particle beam.   The defect inspection system of claim 21, wherein the particle beam comprises at least one of an electron or ion beam. The set of irradiation optical elements
The defect inspection system according to claim 1, comprising at least one of a photon optical element or a particle optical element. The detector is
The defect inspection system of claim 1, comprising at least one of a photon detector or a particle detector.   The defect inspection system of claim 1, wherein the controller is disposed proximate to an optical subsystem.   The defect inspection system of claim 1, wherein the controller and at least a portion of the optical subsystem are disposed within a common housing. A defect inspection system,
An inspection subsystem, the inspection subsystem
An illumination source configured to generate an illumination beam;
A set of illumination optics for directing the illumination beam to the sample;
A detector configured to collect the radiation emitted from the sample;
A controller communicatively coupled to the detector, the controller including a memory device and one or more processors configured to execute the program instructions, the program instructions to the one or more processors;
Determining one or more target patterns corresponding to one or more features on the sample;
Determining a source pattern, wherein the source pattern approximates a subset of one or more target pattern instances in the sample design data, the sample design data being stored in a memory device of the controller;
Defining a spatial relationship between the source pattern and at least one target pattern of a subset of one or more target pattern instances in the sample design data;
Identify one or more instances of the source pattern in the sample design data,
A subset of one or more target pattern instances in the sample design data between one or more identified instances of the source pattern and at least one target pattern of the source pattern and a subset of one or more target pattern instances; Identifying one or more care areas on the sample based on a subset of instances of the one or more target patterns;
A defect inspection system configured to identify one or more defects in one or more care areas of a sample based on irradiation collected by a detector. A defect inspection method,
Provide sample design data to the inspection system,
Determining one or more target patterns, the one or more target patterns including design data relating to one or more sample features to be inspected;
One or more care areas on the sample are defined by the inspection system based on one or more target patterns and sample design data;
Identify one or more defects in one or more care areas of the sample;
A defect inspection method. Defining one or more care areas on the sample includes
29. The defect inspection method according to claim 28, comprising identifying one or more instances of the one or more target patterns in sample design data. Identifying one or more instances of the target pattern in the design data of the sample,
Determining a source pattern, the source pattern approximating a subset of one or more target pattern instances in the sample design data;
Defining a spatial relationship between the source pattern and at least one target pattern of a subset of one or more target pattern instances in the sample design data;
Identify one or more instances of the source pattern in the sample design data;
A subset of one or more target pattern instances in the sample design data between one or more identified instances of the source pattern and at least one target pattern of the source pattern and a subset of one or more target pattern instances; Based on the spatial relationship of
30. The defect inspection method according to claim 29. further,
30. The defect inspection method of claim 29, comprising pre-processing sample design data to facilitate identification of one or more instances of the one or more target patterns in the sample design data. Identifying one or more instances of the one or more target patterns in the design data of the sample,
30. The defect inspection method of claim 29, comprising retrieving the design data for one or more instances of one or more target patterns to generate one or more identified instances of the target patterns.   35. The method of claim 32, further comprising calculating one or more confidence scores related to similarity between the one or more target patterns and one or more identified instances of the one or more target patterns. Defect inspection method. Defining one or more care areas on the sample includes
Defining one or more target regions, wherein the one or more target regions include at least one instance of at least one target pattern of the one or more target patterns, and the care area includes the one or more target regions. Including the target area,
The defect inspection method according to claim 28. Inspecting the one or more care areas of the sample for defects includes:
The defect inspection method according to claim 34, comprising inspecting the one or more target regions, wherein inspection sensitivity of the one or more target regions is individually adjustable. The one or more target patterns are:
30. The defect inspection method of claim 28, comprising one or more target patterns identified by at least one of a design based classification or a design based binning process. The one or more target patterns are:
30. The defect inspection method of claim 28, comprising one or more target patterns related to one or more known defect types. The one or more target patterns are:
29. The defect inspection method of claim 28, comprising one or more target patterns identified by a previous defect inspection process. Determining the one or more target patterns includes
Determining by the user the one or more target patterns;
The defect inspection method according to claim 28. The determination of the one or more target patterns by the user is
40. The defect inspection method of claim 39, comprising selecting one or more instances of the one or more target patterns from sample design data. Selecting one or more instances of the one or more target patterns from sample design data;
41. The defect inspection method of claim 40, comprising selecting one or more instances of the one or more target patterns from a visual display of sample design data. Determining the one or more target patterns includes
30. The defect inspection method of claim 28, comprising providing one or more coordinates related to one or more instances of the one or more target patterns in sample design data. Providing one or more coordinates related to one or more instances of the one or more target patterns in the design data of the sample;
43. The defect inspection method of claim 42, comprising providing graphic data system coordinates of the one or more instances of the one or more target patterns.   The defect inspection method according to claim 28, wherein the inspection system is a virtual inspection system.   The defect inspection method according to claim 28, wherein the design data includes at least one of a physical layout of a sample or an electrical layout of the sample.   30. The defect inspection method of claim 28, further comprising providing one or more care areas for use in a subsequent inspection process.   30. The defect inspection method of claim 28, further comprising classifying the one or more identified defects based on sample design data.

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