KR101461667B1 - Apparatus for examining a patterned structure and method of managing metrology data - Google Patents

Abstract

An apparatus for inspecting a patterned structure formed on a semiconductor wafer using an optical metrology model includes a manufacturing system and a metrology processor. The manufacturing system includes a manufacturing cluster, a metrology cluster, a metrology model optimizer, and a real time profile estimator. The fabrication clusters are configured to process the wafers, and the wafers have a patterned and unpatterned structure. The patterned structure has a lower film thickness, a critical dimension and a profile. The metrology cluster comprises one or more optical metrology devices. The metrology clusters are configured to measure diffracted signals out of the patterned and non-patterned structures. The metrology model optimizer is configured to optimize the optical metrology model of the patterned structure using one or more measured diffraction signals out of the patterned structure and with floating profile parameters, material refraction parameters, and metering device parameters. The real-time profile estimator uses an optimized optical metrology model from a metrology model optimizer, a measured diffraction signal deviating from the patterned structure, and a fixed value within a range of values for at least one parameter of material refraction parameters and metrology parameters . The real-time profile estimator is configured to generate an output that includes the underlying film thickness, critical dimension, and profile of the patterned structure. The metrology data processor is configured to receive, process, store, and transmit a fixed value that is within a range of values for at least one parameter of material refraction parameters and metrology parameters.

An optical metrology model, a patterning structure, a manufacturing cluster, a profile parameter, a diffraction signal, a measuring device, a refractive index parameter, a metrology model optimizer,

Description

Field of the Invention [0001] The present invention relates to a patterning structure inspection apparatus,

BACKGROUND OF THE INVENTION Field of the Invention The present invention relates generally to optical metrology of structures formed on semiconductor wafers, and more specifically to optical metrology of patterned structures.

In semiconductor fabrication, periodic gratings are typically used for quality assurance. For example, one typical use of a periodic grating involves fabricating a periodic grating in the vicinity of the operating structure of the semiconductor chip. The periodic grating is irradiated with electromagnetic radiation. Electromagnetic radiation deviating from the periodic lattice is collected as a diffraction signal. The diffraction signal is then analyzed to determine whether the periodic lattice, and furthermore the operating structure of the semiconductor chip, has been fabricated according to the specification.

In one conventional system, the diffraction signal (measured diffraction signal) collected from the irradiation of the periodic grating is compared to a library of simulated diffraction signals. Each of the simulated diffraction signals in the library is associated with a hypothetical profile. When a match is made between one of the simulated diffraction signals in the library and the measured diffraction signal, the hypothetical profile associated with the simulated diffraction signal is assumed to represent the actual profile of the periodic grating.

A library of simulated diffraction signals can be generated using a precise method such as Rigorous Coupled Wave Analysis (RCWA). More specifically, in the diffraction modeling technique, the simulated diffraction signal is calculated to some extent based on a solution of the Maxwell's equation. The calculation of the simulated diffraction signal involves the implementation of a large amount of complex calculations, so it can be time consuming and expensive.

An apparatus for inspecting a patterning structure formed on a semiconductor wafer using an optical metrology model includes a first manufacturing system and a metrology processor. The first manufacturing system includes a first manufacturing cluster, a first metrology cluster, a first metrology model optimizer, and a first real-time profile estimator. The first fabrication cluster is configured to process the wafer, and the wafer has a first patterning and a first patterning structure, wherein the first patterning structure has a lower film thickness, a critical dimension and a profile. The first metrology cluster comprises one or more optical metrology devices connected to the first fabrication cluster. The first metrology cluster is configured to measure the diffraction signal out of the first patterning and the first un-patterned structure. The first metrology model optimizer is coupled to the first fabrication cluster and the first metrology cluster. The first metrology model optimizer may optimize the optical metrology model of the first patterned structure using one or more measured diffraction signals out of the first patterned structure and with floating profile parameters, material refraction parameters, and metering device parameters . The first real-time profile estimator is coupled to the first optical model optimizer and the first metrology cluster. The first real-time profile estimator includes an optimized optical metrology model from the first metrology model optimizer, a measured diffraction signal deviating from the first patterning structure, and a range of values for at least one parameter from the material refraction parameter and the metrology parameter Lt; / RTI > The first real-time profile estimator is configured to generate an output comprising a lower film thickness, a critical dimension and a profile of the first patterning structure. The metrology processor is connected to the first manufacturing system. The metrology data processor is configured to receive, process, store, and transmit a fixed value that is within a range of values for at least one parameter of material refraction parameters and metrology parameters.

In order to facilitate the description of the present invention, semiconductor wafers may be used to illustrate the application of this concept. The method and process are equally applied to other workpieces having a repeating structure. Also, in the present application, the term "structure" refers to a patterned structure, unless otherwise specified.

BRIEF DESCRIPTION OF THE DRAWINGS Figure Ia is a schematic diagram illustrating an exemplary embodiment in which optical metrology can be used to determine the profile of a structure on a semiconductor wafer. The optical metrology system 40 includes a metrology beam source 41 that projects a beam 43 onto a target structure 59 of the wafer 47. The measurement beam 43 is projected at an incident angle? _I toward the target structure 59 and is diffracted at a diffraction angle? _D. The diffraction beam 49 is measured by the measurement beam receiver 51. The diffraction beam data 57 is transmitted to the profile application server 53. The profile application server 53 collates the measured diffraction beam data 57 against a library 60 of simulated diffraction beam data that represents a change in the combination of the critical dimension and resolution of the target structure. In one exemplary embodiment, the library 60 instance that best matches the measured diffraction beam data 57 is selected. Although a library of diffraction spectra or signals and associated hypothetical profiles are often used to illustrate this concept and principle, the present invention is applicable to a variety of applications, such as in similar methods used for regression, neural net, The present invention is equally applicable to a data space including a simulated diffraction signal and an associated set of profile parameters. The hypothetical profile and associated critical dimension of the selected library 60 instance is estimated to correspond to the actual cross-sectional profile and critical dimensions of the features of the target structure 59. The optical metrology system 40 may use a reflectometer, an ellipsometer, or other optical metrology device to measure the diffracted beam or signal. The optical metrology system is described in U.S. Patent No. 6,913,900 entitled GENERATION OF A LIBRARY OF PERIODIC GRATING DIFFRACTION SIGNAL, issued on September 13, 2005 to Niu et al., Which is incorporated herein by reference in its entirety. Other exemplary embodiments of the present invention in optical metrology that do not require the use of a library are discussed below.

A variation is to generate a library of simulated diffraction signals using a machine learning system (MLS). Prior to generating the library of simulated diffraction signals, the MLS is trained using known input and output data. In one exemplary embodiment, a machine learning system (MLS) using machine learning algorithms such as back-propagation, radial basis function, support vector, kernel regression, ) Can be used to generate a simulated diffraction signal. For a more detailed description of machine learning systems and algorithms, see Simon Haykin in "Neural Networks ", 1999, Prentice Hall, which is incorporated herein by reference in its entirety. See also United States Patent Application Serial No. 10 / 608,300 entitled OPTICAL METROLOGY OF STRUCTURES FORMED ON SEMICONDUCTOR WAFER USING MACHINE LEARNING SYSTEMS filed June 27,2003, which is incorporated herein by reference in its entirety.

The term "one-dimensional structure" is used herein to refer to a structure having a profile that changes only in one dimension. For example, Figure IB shows a periodic grating with a profile that changes in one dimension (i.e., x direction). The profile of the periodic grating shown in Fig. 1B changes in the z direction according to the function in the x direction. However, the profile of the periodic grating shown in Fig. 1B is assumed to be substantially uniform or continuous in the y-direction.

The term "two-dimensional structure" is used herein to denote a structure having a profile that varies at least two dimensions. For example, FIG. 1C shows a periodic grating with a profile that changes in two dimensions (i.e., x and y directions). The profile of the periodic grating shown in Fig. 1C changes in the y direction.

The discussion of Figures 2a, 2b and 2c below describes the features of a two-dimensional repeating structure for optical metrology modeling. 2A shows a top view of a typical orthogonal grid of a unit cell of a two-dimensional repeating structure. The imaginary line grid is superimposed on the top view of the repeating structure where the grid lines are shown along the periodicity direction. The imaginary line grid forms an area called a unit cell. The unit cells may be arranged in an orthogonal or non-orthogonal configuration. The two-dimensional repeating structure may include features such as a repeating post, a contact hole, a via, an island, or a combination of two or more features within a unit cell. Moreover, these features may have various shapes and may be concave or convex features or combinations of concave and convex features. Referring to FIG. 2A, the repeating structure 300 includes unit cells in which holes are arranged orthogonally. The unit cell 302 includes substantially all the features and components inside the unit cell 302 mainly including the hole 304 at the center of the unit cell 302. [

2B shows a top view of a two-dimensional repeating structure. The unit cell 310 includes concave elliptical holes. Figure 2b shows a unit cell 310 having a feature 320 that includes an elliptical hole whose dimension gradually becomes smaller to the bottom of the hole. The profile parameters used to characterize the structure include an X pitch 312 and a Y pitch 314. In addition, the major axis of the ellipse 316, which represents the top of the feature 320, and the major axis of the ellipse 318, which represents the bottom of the feature 320, may be used to characterize the feature 320. Moreover, the intermediate spindle between the top and bottom of the feature can also be used as well as any minor axis (not shown) of normal, middle, or bottom ellipse.

2C is a diagram illustrating an exemplary technique for characterizing a top view of a two-dimensional repeating structure. The repeating unit cell 330 is feature 332, a walnut shaped island viewed from above. One modeling method involves approximating a feature or feature 332 having a combination of ellipses and polygons. After analyzing the topographic variation of feature 322, it has been found that two ellipses, Ellipsoid 1 and Ellipsoid 2, and two polygons, Polygon 1 and Polygon 2, fully characterize feature 332 Another assumption is made. Next, the parameters required to characterize two ellipses and two polygons include the following nine parameters: T1 and T2 for Ellipsoid 1; About Polygon 1 T3, T4, and θ _1; About Polygon 2 T4, T5, and θ _2; T6 and T7 for Ellipsoid 2. Many other combinations of shapes may be used to characterize the top surface of feature 332 in unit cell 330. [ For a detailed description of the modeling of a two-dimensional repeating structure, see U.S. Patent Application No. 11 / 061,303, entitled OPTICAL METROLOGY OPTIMIZATION FOR REPEAT STRUCTURES, filed April 27, 2004 by Vuong et al., Which is incorporated herein by reference in its entirety .

3 is a typical flow chart for inspecting the patterning structure formed on a semiconductor wafer. Referring to FIG. 3, in step 400, an optical metrology model of the patterned structure is created. The optical metrology model includes parameters (i. E., Profile parameters) that characterize the profile of the patterned structure, material refraction related parameters (i. E., Material refraction parameters) used in the layers of the structure And parameters related to angle setting (i.e., measurement device parameters).

As mentioned above, profile parameters may include height, width, sidewall angle, and characterization of profile features such as top-rounding, T-topping, footing, . As also noted, the profile parameters for the repeating structure may include the dimensions of the major and minor axes and polygons of the ellipse used to characterize the X pitch and Y pitch of the unit cell, top surface shape such as holes or islands.

3, the material refraction parameter includes a refractive index N parameter and an extinction coefficient K parameter as shown in the following equation:

Where? Is the wavelength,? Is the refractive index constant for the material, and b is the extinction coefficient constant for the material. Instead of floating N and K, the constants a and b can be floated in the optical metrology model.

In step 402, a range of profile parameters, material refraction parameters, and metering device parameters is defined. In one example, material refraction parameters (e.g., N and K parameters) and metrology device parameters (e.g., the angle of incidence and azimuth of the incident beam relative to the direction of the periodicity of the repeating structure) are defined. As indicated above, the constants a and b can be used for the N and K parameters.

In step 404, a measured diffraction signal is obtained, wherein the diffraction signal measured is measured off the patterned structure using an optical measuring instrument. In one example, a specific optical metrology device may be selected and used to obtain the measured diffraction signal. The optical measuring apparatus may be a reflection system, an elliptic system, a hybrid reflection system / an elliptic system, or the like.

In step 406, the optical metrology model is optimized using the measured diffraction signal and a range of profile parameters, material refraction parameters, and metering device parameters. For example, an initial optical metrology model may be limited. One or more simulated diffraction signals may be generated for the initial optical metrology model using values for the profile parameters, material refraction parameters, metering device parameters within a defined range at step 402. One or more simulated diffraction signals may be compared to the measured diffraction signal. The result of this comparison can be evaluated using one or more termination criteria such as a cost function, a goodness of fit (GOF), and the like. If more than one termination criterion is not satisfied, the initial optical metrology model may be modified to produce an improved optical metrology model. The process of generating one or more diffraction signals and comparing the one or more diffraction signals with the measured diffraction signal may be repeated. This optical metrology model change process can be repeated until one or more termination criteria is met to obtain an optimized metrology model. For a detailed description of metrology model optimization, see U.S. Patent Application Serial No. 10 / 206,491 entitled OPTIMIZATION MODEL AND PARAMETER SELECTION FOR OPTICAL METROLOGY filed by Vuong et al. On Jun. 27, 2002; U.S. Patent Application No. 10 / 946,729 entitled OPTICAL METROLOGY MODEL OPTIMIZATION BASED ON GOALS, filed September 21, 2004; And U.S. Patent Application Serial No. 11 / 061,303, entitled OPTICAL METROLOGY OPTIMIZATION FOR REPEAT STRUCTURES, filed April 27, 2004 by Vuong et al., All of which are incorporated herein by reference in their entirety.

At step 408, for at least one parameter among the material refraction parameter and the measuring device parameter, at least one parameter is set to a fixed value that is within a range of values for at least one parameter. Figures 4A and 4B are exemplary flow charts of techniques for obtaining the values of the parameters of the optical metrology model that can be used as fixed values in step 408.

4A is a typical flow chart of a technique for obtaining the values of the N and K parameters. In step 500, the N and K parameters, including the constants a and b, are obtained from experimental data, such as similar data from previous wafer structures, using the same material, previous runs and publications of the same recipe or history values from the handbook . In step 510, the N and K parameters, including constants a and b, are obtained from measurements using an optical metrology device integrated with the fabrication facility, such as an etch or track integrated fabrication facility. In step 520, the N and K parameters, including constants a and b, are obtained using an off-line optical metrology apparatus.

In one embodiment, the position measured in step 520 is an unpatterned area adjacent to the patterned structure. In yet another embodiment, the measured position may be in a test area of the same wafer or in an area of the test wafer, not adjacent to the patterned structure. In another embodiment, one position is measured for each wafer or lot, and the obtained constants a and b are used for the same wafer, for the entire wafer lot, or for the entire process transfer. Alternatively, the previous correlation of the thickness of the layer with the constants a and b can be used to obtain the values of the constants a and b once the thickness of the layer is determined.

Referring to FIG. 4A, in step 540, material data obtained from various sources and using various techniques are processed to utilize the profile determination of the patterning structure. For example, if several measurements are made to determine the constants a and b, a statistical average can be calculated.

4B is a flow chart for obtaining values for the metering device parameters. In one embodiment, at step 600, based on the selected metrology device, the angle of incidence of the illumination beam is obtained from a vendor specification or from a setting used for an application if the metrology apparatus has a variable angle of incidence. Likewise, in step 610, the azimuth can be determined based on the selected optical metrology apparatus and wafer structure application. In step 640, the specifications and setting data of the process apparatus for optical metrology are processed. Given a reflective system with vertical incidence or an elliptical system with incidence of a fixed angle depending on the selected measuring device, the vertical incidence or fixed angle is converted to the format required by the optical metrology model. The same is true when the azimuth angle of the measuring apparatus is also converted into a format required for the optical measurement model.

Referring to FIG. 3, in step 410, the profile of the patterning structure may be determined using the optical metrology model and the fixed value optimized in step 408. In particular, at least one profile parameter of the patterned structure is determined using the optimized optical metrology model and the fixed value in step 408. The at least one profile parameter may be determined using a regression process or a library based process.

As described above, in a regression process, a measured diffraction signal measured off of the patterned structure is compared to a simulated diffraction signal, which is a profile parameter that produces the closest matching simulated diffraction signal as compared to the measured diffraction signal Lt; RTI ID = 0.0 > a < / RTI > set of profile parameters to obtain a convergence value for the set of profile parameters. For a more detailed description of the regression-based process, see U.S. Patent No. 6,785,638 entitled METHOD AND SYSTEM OF DYNAMIC LEARNING THROUGH REGRESSION-BASED LIBRARY GENERATION PROCESS, dated August 31, 2004, which is incorporated herein by reference in its entirety .

In a library-based process, an optical metrology data store is created using an optimized metrology model. The optical metrology data store has a pair of corresponding sets of simulated diffraction signals and profile parameters. A detailed description of the generation of optical metrology data, such as a library of corresponding sets of simulated diffraction signals and profile parameters, can be found in US Patent 6,913,900, titled " GENERATION OF A LIBRARY OF PERIODIC GRATING DIFFRACTION SIGNAL "Quot;, which is incorporated herein by reference in its entirety.

In one embodiment, the profile of the patterned structure is determined using a subset of the metrology data store and a measured diffraction signal that are within a fixed value at step 408. For example, if the a and b constant values of the N and K parameters are fixed at step 408, the portion of the optical metrology data store used is a set of profile parameters and simulated diffraction signals corresponding to fixed values a and b.

In yet another embodiment, the profile of the patterned structure is determined using the measured diffraction signal and the entire optical metrology data store, i. E. By searching the entire data space. For example, the profile of the patterned structure is determined by using the measured diffraction signal and the total measurement data, i.e., floating the a and b constants while searching for the best matching diffraction signal.

5 is a typical configuration diagram of a real-time profile estimator. The first fabrication cluster 916 is connected to the metrology cluster 912. The first fabrication cluster 916 can include one or more of photolithography, etching, thermal processing systems, metallization, implantation, chemical vapor deposition (CVD), chemical mechanical polishing (CMP) The first fabrication cluster 916 processes wafers (not shown) through one or more process steps. After each process step, the wafer can be measured in the metrology cluster 912. The metrology cluster 912 may be an in-line or off-line set of metrology devices such as a reflectometer, an ellipsometer, a hybrid reflectometer / ellipsometer, a scanning electronic microscope, a sensor,

After measuring the wafer structure, the metrology cluster 912 transmits the diffraction signal 811 to the model optimizer 904. The metrology model optimizer 904 includes the manufacturing recipe input information and the optimization parameter 803, previous experimental structural profile data 809 from the metrology data store 914 and the measured diffraction signal 811 from the metrology cluster 912 ) Is used to generate and optimize the optical metrology model of the measured structure. Recipe data 803 includes material in layers of the patterned and non-patterned structures in the laminated state. The optimization parameter 803 includes profile parameters, material refraction parameters, and metering device parameters that are floating in the optical metrology model. The model optimizer 904 optimizes the optical metrology model based on the measured diffraction signal 811 deviating from the patterned structure, the recipe data and optimization parameters 803, and the experimental data 809 from the metrology data store 914 And generates an optimized optical metrology model 815 that is transmitted to the real-time profile estimator 918.

5, the real-time profile estimator 918 includes an optical metrology model 815 optimized for determining the patterned structural profile, critical dimension and bottom thickness 843, measured diffraction signal 817, Data 805 is used. The experimental metrology data 805 may include fixed profile parameters (such as pitch), N and K parameters (such as constants a and b), and / or metering device parameters (such as angle of incidence and / or azimuth) . The output of the real-time profile estimator 918 may also optionally be stored in the first manufacturing cluster 916 as data 841, in the memory metrology data store 914 as data 827, And transferred to the manufacturing cluster 930.

The data 841 transferred to the first fabrication cluster 916 may include values of one or more profile parameters of the underlying film thickness, CD, and / or patterned structure. The values of the one or more profile parameters of the underlying film thickness, CD, and / or patterned structure may be selected such that the first manufacturing cluster has a focus and dose for the photolithographic manufacturing cluster, or a dopant concentration for the ion- Can be used to select the above process parameters. The data 845 sent to the first fabrication cluster 930 may include a patterning structure CD that may be used to select the etchant concentration in the etch fabrication cluster or the deposition time in the deposition clusters. The data 827 transmitted to the metrology data store may include identification information such as wafer identification information (ID), lot ID, recipe, and patterning structure ID to facilitate searching for other applications, CD, and / or the profile parameter of the patterned structure.

Referring to FIG. 5, as described above, the measurement data store 914 can use identification information such as wafer ID, lot ID, recipe, and patterned structure ID as means for constructing and indexing measurement data. Data 813 from metrology cluster 912 includes a measured diffraction signal associated with identification information for a wafer, lot, recipe, place or wafer location, and a patterned or non-patterned structure. The data 809 from the metrology model optimizer 904 includes the variables associated with the patterning structure profile, the metering device type and associated variables, and the variables used in the variables that are floating in the modeling and fixed in the modeling. Experimental measurement data 805 may include fixed profile parameters such as pitch, N and K parameters (such as constants a and b), and / or metering device parameters (such as angle of incidence and / or azimuth) .

Figure 6 is a typical configuration diagram for creating and using a profile server to determine a profile corresponding to a measured diffraction signal. Fig. 6 is the same as Fig. 5 except for two. First, the model optimizer 904 in Fig. 6 can generate one or both of two data sets in addition to optimizing the metrology model. The first data set is a library of pairs of mapped diffraction signals and a corresponding set of profile parameters. The second data set is a trained machine learning system (MLS) that can be trained as a subset of the library of the first data set described above. The first and / or second data set 819 is stored in the metrology data store 914. Second, the real-time profile estimator 918 in FIG. 5 has been replaced with the profile server 920 in FIG. The profile server 920 may use either a library data set or a training MLS data set available from the metrology model optimizer 904. Alternatively, the profile server 920 may access the stored data set of the metrology data store 914. The profile server 920 may compare the measured diffraction signal 817, library, or metrology data store 914 from the metrology cluster 912 to determine the underlying film thickness, CD, and profile parameters of the patterning structure 843, RTI ID = 0.0 > MLS < / RTI > Moreover, the profile server 920 may use fixed profile parameters (such as pitch), N and K parameters (such as a constant a (e.g., pitch a), etc.) to set the boundaries of the trained MLS or library used to find the best match to the measured diffraction signal 817. [ And / or the like), and / or metrology instrument parameters (such as incident angle and / or azimuth angle).

FIG. 7 is a typical schematic diagram for connecting two or more manufacturing systems with a metrology processor and metrology data store to determine profile parameters of a patterned structure. FIG. The first manufacturing system 940 includes a model optimizer 942, a real time profile estimator 944, a profile server 946, a manufacturing cluster 948, and a metrology cluster 950. The first manufacturing system 940 is coupled to the metrology processor 1010. The metrology processor 1010 is coupled to the metrology data source 1000, metrology data store 1040, manufacturing host processor 1020, and process simulator 1050.

7, the configuration of the first manufacturing system 940, i.e., the model optimizer 942, the real-time profile estimator 944, the profile server 946, the manufacturing cluster 948 and the metering cluster 950, 5 and the corresponding device disclosed in Fig. 6, respectively. The metrology processor 1010 receives metrology data 864 from an off-line or remote metrology data source 1000. The offline measurement data source 1000 may be an offline cluster of metrology devices at a manufacturing location, such as a reflectometer, ellipsometer, SEM, and the like. The remote instrumentation data source 1000 may include a website, a remote data server, or a remote processor that provides instrumentation data for the application. The data 860 from the first manufacturing system 940 to the metrology processor 1010 may include a profile parameter range of the generated metrology model and the generated datastore to determine the structural profile parameters. The data store 1040 can include a trained MLS system that is capable of generating a set of profile parameters for a library of input diffraction signals or a pair of simulated diffraction signals and a corresponding set of profile parameters. Data 870 from data store 1040 to metrology processor 1010 includes a set of simulated diffraction signals and / or profile parameters. The data 860 from the metrology processor 1010 to the first metrology system 940 may include a profile parameter to specify the portion of the data space to be searched in the traced MLS store or library in the metrology data store 1040, Section parameter and the value of the measuring device parameter. The data 862 sent to and from the second manufacturing system 970 for the measurement processor 1010 is identical to the data 860 sent to and from the first manufacturing system 940. [

Referring back to Figure 7, data 866 sent to and from the metrology processor 1010 for the manufacturing processor 1020 are stored in metrology clusters 950 and 980 in the first and second manufacturing systems 940 and 970, And data associated with the application recipe. Data 868, such as profile parameters calculated using process simulator 1050, is transmitted to the metrology processor 1010 for use in setting the selection variable of the metrology model to a fixed value. Examples of process simulators include Prolith ^TM , Raphael ^TM , and Athena ^TM . Alternatively, the profile parameter values may be used by the profile servers 946 and 976 to define a data space for searching in the trained MLS store or library in the metrology data store 1040. The metrology data store 1040 in Figure 7 is a repository of metrology data and this metrology data can be used in the first and / or second manufacturing systems 940 and 970. As described above, the first and / or second manufacturing systems 940 and 970 may be fabricated by any suitable process, such as photolithography, etching, thermal processing systems, metallization, implantation, chemical vapor deposition (CVD), chemical mechanical polishing ≪ / RTI >

Figure 8 is a typical flow chart for determining and using patterning structure profile determination and measurement data for automatic processing and device control. At step 1100, the optical metrology model is generated and optimized using the method disclosed in FIG. In step 1110, one or more data stores for determining structural profile parameters are generated using an optimized optical metrology model. The data store may comprise a trained MLS system capable of generating a set of profile parameters for an input measured diffraction signal, or a library of pairs of simulated diffraction signals with a corresponding set of profile parameters. In step 1120, profile parameters, material refraction parameters, and data for metering device parameters are obtained. As discussed above, the selected profile parameters are parameters that can be fixed or fixed using historical data or measured values for similar wafer applications. The value of the material refraction parameter is the constants a and b for the refractive index N and the loss coefficient K. The value of the metering device parameter, such as the angle of incidence, is obtained from the vendor specification of the metering device. The value of the azimuth is obtained from the setup used in the diffraction measurement. In step 1130, profile parameters, critical dimension (CD), and bottom thickness are determined using the measured diffraction signal.

Referring to Figure 8, in step 1140, the profile parameters and material data of the structure are associated with the identification information. The identification information includes the location of the measured structures, wafers, wafer lots, runs, application recipes, and other manufacturing related data. In step 1150, the metrology data and association identification information are stored in the metrology data store. The metrology data and / or associated identification information may be transmitted to a later or previous manufacturing process step in step 1160. In step 1170, the transmitted metrology data and / or associated identification information is used to control at least one process variable in a subsequent or previous manufacturing process step, or a device control variable in a previous, current, or subsequent manufacturing process step. For example, the value of the MCD (Middle Critical Dimension) of the structure in the etch process step may be the same as the value of the MCD in the previous lithography process (not shown) where the value of the MCD is used to adjust the focus and / or dose of the stepper in the photolithography process step Step. Alternatively, the bottom critical dimension (BCD) of the structure may be transferred to the etch process step and the value of BCD is used to adjust the etchant concentration or etch length. In another embodiment, the MCD may be sent to the current process, such as a post exposure bake (PEB) process step, where the value of the MCD is used to control the temperature of the PEB process. The MCD can also be used to adjust process parameters in the current process, such as the reaction chamber pressure in the etching process.

In particular, it is contemplated that the functional implementations of the invention disclosed herein may equally be implemented in hardware, software, firmware, and / or other available functional configurations or building blocks. For example, the metrology data store may be in computer memory or in an actual computer storage device or medium. Other variations and embodiments may be made in light of the above teachings, and the scope of the present invention is therefore not to be limited by the details of the description, but is intended to be limited only by the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS Figure Ia is a schematic diagram illustrating an exemplary embodiment in which optical metrology can be used to determine the profile of a structure on a semiconductor wafer.

Figure 1B shows a typical one-dimensional repeating structure.

Figure 1C shows a typical two-dimensional repeating structure.

2A shows a typical orthogonal grid of unit cells of a two-dimensional repeating structure.

2B is a top view of a two-dimensional repeating structure.

Figure 2c shows a typical technique for characterizing a top view of a two-dimensional repeating structure.

3 is a typical flow chart for determining profile parameters of a wafer structure using the obtained values of optical metrology parameters;

4A is a typical flow chart of a technique for obtaining refractive index for a wafer structure.

Figure 4b is a typical flow chart for obtaining values for a metering device variable.

5 is a typical configuration diagram of an embodiment of a real time profile estimator;

6 is an exemplary configuration diagram of an embodiment for creating and using a profile server datastore.

Figure 7 is a typical configuration diagram for coupling two or more manufacturing systems with an instrumentation processor and a metrology data store to determine profile parameters of the patterned structure.

Figure 8 is a typical flow chart for managing and using metrology data for automation processes and equipment control.

Description of the Related Art

40: Optical measurement system

41: Measuring beam source

43: beam

47: wafer

49: Diffraction beam

51: Measurement beam receiver

53: Profile Application Server

57: Diffraction beam data

59: target structure

Claims (31)

An apparatus for inspecting a patterned structure formed on a semiconductor wafer using an optical metrology model, 1. A first fabrication cluster configured to process a wafer, the wafer having a first patterning and a first patterning structure, the first patterning structure having a lower film thickness, a critical dimension and a profile Said first manufacturing cluster; A first metrology cluster comprising at least one optical metrology device coupled to the first fabrication cluster, the first metrology cluster being configured to measure the diffraction signal out of the first patterning and the first non-patterning structure; And a second measurement cluster, coupled to the first fabrication cluster and the first metrology cluster, wherein the at least one measured diffraction signal deviates from the first patterned structure, and with a floating profile parameter, a material refraction parameter, A first optical metrology model optimizer configured to optimize an optical metrology model of the first patterned structure; A first optical metrology model optimizer coupled to the first metrology cluster and comprising an optimized optical metrology model from the first optical metrology model optimizer, a measured diffraction signal deviating from the first patterned structure, And to generate an output comprising a lower film thickness, a critical dimension, and a profile of the first patterned structure, the first patterned structure being configured to use a fixed value that is within a range of values for at least one parameter of the refractive parameter and the metrology tool parameter A first real-time profile estimator, A first manufacturing system comprising: Processing, storing, and transmitting a fixed value that is within a range of values for at least one parameter of the material refraction parameter and the metrology device parameter, the metrology data processor being configured to receive, process, store, Lt; / RTI > The first optical metrology model optimizer compares the measured diffraction signal with one or more simulated diffraction signals generated for an initial optical metrology model and if the comparison result does not satisfy a predetermined end criterion, Wherein the optical metrology model is changed to an improved optical metrology model. The method according to claim 1, Wherein the metrology data processor is further configured to receive, process, store and transmit the optimized optical metrology model and the underlying film thickness, profile parameters, critical dimensions, material refraction parameters, and metrology parameters of the first patterned structure Wherein the patterned structure inspection apparatus comprises: The method according to claim 1, The first manufacturing system comprises: A first optical metrology model optimizer and a second metrology model optimizer coupled to the first metrology cluster, the optimized optical metrology model from the first optical metrology model optimizer, the measured diffracted signal deviating from the first patterned structure, Value and configured to generate an output comprising a lower film thickness, a critical dimension, and a profile of the first patterned structure. ≪ Desc / Clms Page number 24 > The method according to claim 1, Wherein the material refraction parameter comprises a refractive index (N) parameter and an extinction coefficient (K) parameter. 5. The method of claim 4, Wherein the N parameter is denoted by a vector a and denoted by a vector expression K (?, B), where? Is a wavelength. Inspection device. 6. The method of claim 5, Wherein the N parameter and the K parameter are fixed to N and K values of a layer of the patterned structure measured using the first metrology cluster. The method according to claim 6, Wherein the N and K values of the layer of the first patterned structure are the unpatterned areas adjacent to the first patterned structure or the unpatterned areas of the same wafer not adjacent to the first patterned structure, Patterned structure is measured from the non-patterned region. 8. The method of claim 7, Wherein at least two positions on the wafer are measured and statistical averages are calculated. 6. The method of claim 5, Wherein the N parameter and the K parameter are fixed to experimental or theoretical N and K values for the same material. 10. The method of claim 9, Wherein the metrology device parameter includes either or both of an incident angle and an azimuth angle. The method according to claim 1, Further comprising a metrology data store coupled to the metrology data processor and configured to store and access fixed values of the first patterned structure and an optimized optical metrology model. 12. The method of claim 11, Wherein the metrology data store is configured to store and access a corresponding set of profile parameters and a simulated diffraction signal pair for the first patterning structure. 12. The method of claim 11, Wherein the metrology data store is configured to store and access a set of training data for either or both of an optical metrology machine language system and a trained optical metrology machine language system for the first patterning structure. Structure inspection device. 12. The method of claim 11, Wherein the metrology data processor is coupled to a second fabrication system. 15. The method of claim 14, Wherein the second fabrication system is configured to process the wafer, wherein the wafer has a second patterning and a second patterning structure, the second patterning structure having a lower film thickness, a critical dimension, and a profile, The second manufacturing system comprises: A second fabrication cluster configured to process a wafer, the wafer having a second patterned and a second patterned structure, the second patterned structure having a lower film thickness, a critical dimension, and a profile, 2 manufacturing clusters; A second metrology cluster comprising at least one optical metrology device coupled to the second fabrication cluster, the second metrology cluster configured to measure diffraction signals out of the second patterning and the second un-patterned structure; Wherein the first and second measurement clusters are coupled to the second fabrication cluster and the second metrology cluster using one or more measured diffraction signals deviating from the second patterning structure and with a floating profile parameter, a material refraction parameter, A second optical metrology model optimizer configured to optimize a second optical metrology model of the patterned structure; The second optical metrology model optimizer and the second metrology cluster, the optimized optical metrology model from the second optical metrology model optimizer, the measured diffraction signal deviating from the second pattern structure, Wherein the second patterning structure is configured to use a fixed value having a range of values for at least one parameter from among the refractive parameter and the metrology device parameter, and to generate an output comprising a lower film thickness, a critical dimension, Lt; RTI ID = 0.0 > The patterning structure inspection apparatus comprising: 16. The method of claim 15, Wherein the second manufacturing system comprises: The second optical metrology model optimizer and the second metrology cluster, the optimized optical metrology model from the second optical metrology model optimizer, the measured diffracted signal deviating from the second patterned structure, and the fixed Value and configured to generate an output comprising a lower film thickness, a critical dimension and a profile of the second patterning structure. ≪ Desc / Clms Page number 24 > 17. The method of claim 16, Wherein at least one of a lower film thickness, a critical dimension and a profile of the first patterned structure is used to change at least one process parameter of the second fabrication cluster. 17. The method of claim 16, Wherein at least one of the underlying film thickness, critical dimension, and profile of the second patterned structure is used to modify at least one process parameter of the first fabrication cluster. 17. The method of claim 16, Wherein at least one of a lower film thickness, a critical dimension and a profile of the first patterned structure is used to change at least one process parameter of the first manufacturing cluster. 17. The method of claim 16, Wherein at least one of the underlying film thickness, critical dimension, and profile of the second patterned structure is used to modify at least one process parameter of the second fabrication cluster. 17. The method of claim 16, Wherein the metrology data store is configured to store and access a corresponding set of profile parameters and a simulated diffraction signal pair for the second patterning structure. 17. The method of claim 16, Wherein the metrology data store is configured to store and access a set of training data for either or both of an optical metrology machine language system and a trained optical metrology machine language system for the second patterning structure. Structure inspection device. 17. The method of claim 16, Wherein the metrology data processor is configured to receive, process and transmit metrology data for an off-line or remote metrology data source. 17. The method of claim 16, Wherein the metrology data processor is coupled to a process simulator. 17. The method of claim 16, Wherein the metrology data processor is coupled to a process simulator, wherein the metrology data processor is configured to receive and process metrology data comprising profile parameters, material refraction parameters, and metering device parameter values from another source, And a configuration in which the measurement data of the patterning structure is processed and transmitted to another manufacturing system. A method for managing metrology data related to a structure in a wafer in which one or more fabrication processes are processed, a) generating an optical metrology model for the patterned structure, the optical metrology model having profile parameters, material refraction parameters, and metering device parameters; b) defining a range of values for said profile parameter, material refraction parameter, and metering device parameter; c) obtaining at least one measured diffraction signal of the patterned structure; d) optimizing the optical metrology model using a range of values defined in step b) and one or more measured diffraction signals of the patterned structure obtained in step c) to obtain an optimized optical metrology model; e) for at least one parameter among the material refraction parameter and the metrology device parameter, setting the at least one parameter to a fixed value within a range of values for the at least one parameter; f) generating one or more metrology data stores using the optimized optical metrology model, wherein the one or more metrology data stores are used to determine profile parameters of the patterning structure; Wow; g) determining a profile parameter of the patterning structure using the measured diffraction signal and one or more generated metrology data stores deviating from the patterning structure; h) associating the profile parameter of the determined patterning structure with at least one of material data, metrology data, location of the patterning structure in the wafer, identification of the wafer, and identification information associated with the manufacturing step identification information Lt; / RTI > Wherein optimizing the optical metrology model comprises comparing at least one simulated diffraction signal generated for an initial optical metrology model with the measured diffraction signal and if the comparison result does not satisfy a predetermined end criterion , And changing the initial optical metrology model to an improved optical metrology model. 27. The method of claim 26, i1) storing the profile parameter and identification information of the associated patterning structure in the metrology data store. 28. The method of claim 27, i2) adjusting at least one process variable or adjusting the setting of the manufacturing apparatus in the manufacturing cluster of the manufacturing system, using either or both of the profile parameters and the identification information of the patterning structure, How to manage your data. 29. The method of claim 28, i3) transmitting either or both of the profile parameter and the identification information of the patterning structure to another manufacturing system, Wherein the profile parameter is used to adjust at least one process variable of the another manufacturing system. delete delete

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