Identification of array in semiconductor specimen

Abstract

To provide identification of an array in a semiconductor specimen.SOLUTION: There are provided a method and a system configured to obtain an image of a semiconductor specimen including one or more arrays, each including repetitive structural elements, and one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements. A PMC is configured to, during run-time scanning of the semiconductor specimen, perform a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, to obtain a correlation matrix, use the correlation matrix to distinguish between one or more first areas of the image corresponding to the one or more arrays and one or more second areas of the image corresponding the one or more regions, and output data informative of the one or more first areas of the image.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The subject matter of the present disclosure generally relates to the field of testing of samples, and more particularly to automating the testing of samples.

Background Art

[0002] Current requirements for high density and performance associated with the very large scale integration of manufactured devices require sub-micron features, improved transistor and circuit speeds, and improved reliability. Such requirements necessitate the formation of device features with high accuracy and uniformity, and as a result, careful monitoring of the manufacturing process, including automated testing of the devices while they are still in the form of semiconductor wafers.

[0003] To detect and classify defects in samples, the testing process is used at various steps during semiconductor manufacturing. The effectiveness of testing can be improved by automating the process, such as, for example, automatic defect classification (ADC), automatic defect review (ADR), and the like.

Summary of the Invention

[0004] According to certain aspects of the subject matter of the present disclosure, a system for testing a semiconductor sample is provided, the system including a processor and memory circuit (PMC) configured to acquire an image of a semiconductor sample including one or more arrays, each including repetitive structural elements, and one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements, the PMC performing a correlation analysis between the pixel intensity of the image and the pixel intensity of a reference image providing information of at least one of the repetitive structural elements during real-time scanning of the semiconductor sample to obtain a correlation matrix, using the correlation matrix to distinguish one or more first regions of the image corresponding to the one or more arrays from one or more second regions of the image corresponding to the one or more regions, and configured to output data providing information of the one or more first regions of the image.

[0005] According to some embodiments, the system determines a sub-region of an image corresponding to a value of a correlation matrix that meets an amplitude criterion, clusters the sub-regions into one or more clusters based on data providing information about distances between repetitive structural elements in the array, and is configured to determine one or more first regions based at least on the one or more clusters.

[0006] According to some embodiments, one or more arrays are separated from one or more regions by one or more boundaries, and the system is configured to estimate one or more first regions of an image that includes at least one or more arrays up to the boundaries.

[0007] According to some embodiments, the system is configured to apply image processing to a reference image, and the image processing attenuates a repetitive pattern of the reference image.

[0008] According to some embodiments, the system clusters sub-regions into one or more first clusters based on data providing information about distances between repetitive structural elements in the array along a first axis, clusters the sub-regions into one or more second clusters based on data providing information about distances between repetitive structural elements in the array along a second axis, and is configured to use the first and second clusters to distinguish between one or more first regions of an image corresponding to one or more arrays and one or more second regions of an image corresponding to one or more regions.

[0009] According to some embodiments, for each cluster, the system determines a polygon surrounding the one or more clusters and is configured to output the polygon as a first region of the image.

[0010] According to some embodiments, the system is configured to select only clusters for which some sub-regions meet a threshold.

[0011] According to some embodiments, the system is configured to obtain data that provides amplitude reference information during a setup phase prior to performing a runtime test on a semiconductor sample.

[0012] According to some embodiments, the system performs a correlation analysis between the pixel intensities of one or more first regions of an image and the pixel intensities of a second reference image that provides information about at least one of the repetitive structural elements to obtain a second correlation matrix, determines one or more sub-regions of the one or more first regions of the image corresponding to the values of the second correlation matrix that satisfy the amplitude reference, and determines a distortion map between the one or more first regions of the image and the array based at least on the positions of the sub-regions in the one or more first regions of the image and data that provides information about the planned positions of the repetitive structural elements in the array, and is configured to generate a corrected image based on the distortion map.

[0013] According to some embodiments, the system is configured to generate a corrected image such that the positions of the sub-regions in the corrected image and the data that provides information about the planned positions of the repetitive structural elements in the array satisfy a proximity reference.

[0014] According to some embodiments, the system determines a distortion DF central between the positions of the sub-regions of one or more first regions of the image and the data that provides information about the planned positions of the repetitive structural elements in the array, and is configured to determine a distortion map between the one or more first regions of the image and the array of semiconductor samples based on an interpolation method applied to at least DF central .

[0015] According to some embodiments, the system is configured to obtain a reference image that provides information about at least one of the repetitive structural elements and select only a subset of the reference image as the second reference image.

[0016] According to other aspects of the subject matter of this disclosure, a method of testing a semiconductor sample is provided, the method comprising, by a processor and a memory circuit (PMC), acquiring an image of a semiconductor sample including one or more arrays each including a repetitive structural element and one or more regions each region at least partially surrounding a corresponding array and including a feature different from the repetitive structural element; during runtime scanning of the semiconductor sample, performing a correlation analysis between the pixel intensity of the image and the pixel intensity of a reference image providing information of at least one of the repetitive structural elements to obtain a correlation matrix; using the correlation matrix to distinguish one or more first regions of the image corresponding to the one or more arrays from one or more second regions of the image corresponding to the one or more regions; and outputting data providing information of the one or more first regions of the image.

[0017] According to some embodiments, the method includes determining a sub-region of the image corresponding to a value of the correlation matrix that meets an amplitude criterion, clustering the sub-region into one or more clusters based on data providing information about the distance between repetitive structural elements in the array, and determining one or more first regions based at least on the one or more clusters.

[0018] According to some embodiments, the one or more arrays are separated from the one or more regions by one or more boundaries, and the method includes estimating one or more first regions of the image including at least the one or more arrays up to the boundaries and excluding the one or more second regions corresponding to the one or more regions.

[0019] According to some embodiments, the method includes clustering sub-regions into one or more first clusters based on data providing information about the distances between repeating structural elements in an array along a first axis, clustering sub-regions into one or more second clusters based on data providing information about the distances between repeating structural elements in an array along a second axis, and using the first and second clusters to distinguish one or more first regions of an image corresponding to one or more arrays from one or more second regions of an image corresponding to one or more regions.

[0020] According to some embodiments, the method includes selecting only clusters for which some sub-regions satisfy a threshold.

[0021] According to some embodiments, the method includes performing a correlation analysis between the pixel intensities of one or more first regions of an image and the pixel intensities of a second reference image providing information about at least one of the repeating structural elements to obtain a second correlation matrix, determining sub-regions of one or more first regions of the image corresponding to values of the second correlation matrix that satisfy an intensity criterion, determining a distortion map between one or more first regions of the image and an array based at least on the positions of the sub-regions in one or more first regions of the image and data providing information about the intended positions of the repeating structural elements in the array, and generating a corrected image based on the distortion map.

[0022] According to some embodiments, the method includes determining a distortion DF central between the positions of sub-regions of one or more first regions of an image and data providing information about the intended positions of the repeating structural elements in the array, and determining a distortion map between one or more first regions of the image and an array of semiconductor samples based at least on an interpolation method applied to at least DF central .

[0023] According to other aspects of the subject matter of this disclosure, a non-transitory computer-readable medium including instructions is provided, where when the instructions are executed by a PMC, the PMC is caused to perform operations as described above.

[0024] According to other aspects of the subject matter of this disclosure, a system for testing a semiconductor sample is provided, the system including a processor and a memory circuit (PMC) configured to obtain an image of a semiconductor sample including one or more arrays each including repetitive structural elements and one or more regions each surrounding at least partially a corresponding array and including features different from the repetitive structural elements, and to obtain data D providing information on the pixel intensity of at least one of the one or more arrays and the one or more regions. threshold The PMC is configured to determine data D representing pixel intensities along a plurality of axes of the image during runtime scanning of the semiconductor sample, X D Y to use D to distinguish one or more first regions of the image corresponding to the one or more arrays and one or more second regions of the image corresponding to the one or more regions, and to output data providing information on one or more first regions of the image. X ,D Y , and D threshold

[0025] In some embodiments, the system is configured to determine data D representing pixel intensities along each of a plurality of lines of the image, X to determine data D representing pixel intensities along each of a plurality of columns of the image, Y to use D to distinguish one or more first regions of the image corresponding to the one or more arrays and one or more second regions of the image corresponding to the one or more regions, X ,D Y , and D threshold and to output data providing information on one or more first regions of the image.

[0026] According to some embodiments, each of one or more arrays includes structural elements that are indistinguishable by visual inspection of an image.

[0027] According to some embodiments, the system determines data D representing pixel intensities along each of a plurality of rows of an image, X D, X selects a subset S of the image that includes rows of the image where D exceeds a first threshold, L S, L determines data D representing pixel intensities along each of a plurality of columns of the subset S, Y,SL D, Y,L and determines a subset C of columns of S where D exceeds a second threshold, L C, SL C, SL and is configured to determine one or more first regions based at least on C.

[0028] According to some embodiments, the system determines data D representing pixel intensities along each of a plurality of columns of an image, Y D, Y selects a subset S of the image that includes columns of the image where D exceeds a first threshold, C S, C determines data D representing pixel intensities along each of a plurality of rows of the subset S, X,SC D, X,SC and determines a subset L of rows of S where D exceeds a second threshold, C L, SC L, SC and is configured to determine one or more first regions based at least on L.

[0029] According to some embodiments, the first threshold is stricter than the second threshold. According to some embodiments, the first threshold is stricter than a third threshold.

[0030] According to some embodiments, there is provided a corresponding method (including operations as described above with reference to the system), and a non-transitory computer-readable medium including instructions, wherein when the instructions are executed by a PMC, the corresponding operations are caused to be executed by the PMC.

[0031] According to some embodiments, the proposed solution enables distinguishing, in an image of a semiconductor sample, an array including repetitive structural elements and a surrounding region including features different from the repetitive structural elements. According to some embodiments, the proposed solution is efficient and functions during the runtime scanning of the semiconductor sample. According to some embodiments, an accurate identification of the array is performed, thereby enabling extraction of the array up to the boundary of the array separating the array from the surrounding region. According to some embodiments, the proposed solution enables correction of distortions present in the image of the array. In particular, efficient and accurate correction is possible.

[0032] To understand the present disclosure and to know how the present disclosure is actually implemented, next, embodiments will be described by way of non-limiting examples only with reference to the accompanying drawings.

Brief Description of the Drawings

[0033]

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[0034] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, one of ordinary skill in the art will understand that the subject matter of the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, components, and circuits have not been described in detail so as not to obscure the subject matter of the present disclosure.

[0035] Unless otherwise specified, as will be apparent from the following discussion, throughout this specification, discussions using terms such as "process", "acquire", "select", "determine", "generate", "output", "use", "execute", etc. refer to actions and / or processes of a computer that manipulate and / or transform data into other data, where the data is represented as a physical quantity such as an electronic quantity and / or the data represents a physical object. The term "computer" should be broadly construed to include, by way of non-limiting example, any type of hardware-based electronic device having data processing capabilities, including the system 103 and its respective parts disclosed in this application.

[0036] As used herein, the terms "non-transitory memory" and "non-transitory storage medium" should be broadly construed to include any volatile or non-volatile computer memory suitable for the subject matter of this disclosure.

[0037] As used herein, the term "sample" should be broadly construed to include any type of wafer, mask, and other structures, combinations thereof and / or parts thereof used to produce semiconductor integrated circuits, magnetic heads, flat panel displays, and other semiconductor manufactured products.

[0038] As used herein, the term "test" should be broadly construed to include any kind of metrology-related operation, as well as operations related to the detection and / or classification of defects in samples during manufacture. Tests are performed during or after the production of the sample to be tested by using non-destructive test tools. By way of non-limiting example, the test process can include run-time scans (single or multiple scans), sampling, review, measurement, classification, and / or other operations performed on the sample or a portion thereof using the same or different inspection tools. Similarly, tests can be performed prior to the production of the sample to be tested, for example, including generating test strategies and / or other setup operations. It should be noted that, unless otherwise specified, the term "test" or its derivatives as used herein are not limited with respect to the resolution or size of the inspection area. A wide variety of non-destructive test tools include, by way of non-limiting example, scanning electron microscopes, atomic force microscopes, optical inspection tools, and the like.

[0039] By way of non-limiting example, run-time tests can utilize a two-phase procedure, for example, inspection of the sample followed by review of the sampled locations of potential defects. During the first phase, the surface of the sample is inspected at high speed and relatively low resolution. In the first phase, a defect map is created to indicate suspected locations on the sample with a high probability of defects. During the second phase, at least a portion of the suspected locations is analyzed more fully at relatively high resolution. These two phases can be performed with the same inspection tool in some cases, or with different inspection tools in other cases.

[0040] As used herein, the term "defect" should be broadly construed to include any kind of abnormality or undesirable feature formed on or in the sample.

[0041] As used herein, the term "design data" should be construed broadly to include any data representing a hierarchical physical design (layout) of a sample. Design data may be provided by each designer and / or may be derived from the physical design (e.g., by complex simulations, simple geometric operations, and Boolean operations, etc.). Design data may be provided in various formats, such as, by way of non-limiting example, the GDSII format, the OASIS format, etc. Design data may be presented in vector format, grayscale intensity image format, or other ways.

[0042] It will be understood that, unless otherwise specified, particular features of the subject matter of this disclosure described in the context of separate embodiments may also be provided in combination in a single embodiment. Conversely, various features of the subject matter of this disclosure described in the context of a single embodiment may also be provided separately or in any suitable sub-combination. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the method and apparatus.

[0043] With this in mind, turn attention to FIG. 1, which shows a functional block diagram of a test system according to a particular embodiment of the subject matter of this disclosure. The test system 100 shown in FIG. 1 can be used to test a sample (e.g., a wafer and / or a part thereof) as part of a sample manufacturing process. The illustrated test system 100 includes a computer-based system 103 that can automatically determine metrology-related information and / or defect-related information using images acquired during sample manufacturing. System 103 can be operably connected to one or more low-resolution test tools 101, and / or one or more high-resolution test tools 102, and / or other test tools. The test tools are configured to capture images, and / or review captured images, and / or enable or perform measurements related to the captured images. System 103 can further be operably connected to a CAD server 110 and a data repository 109.

[0044] System 103 includes a processor and memory circuit (PMC) 104 operably connected to a hardware-based input interface 105 and a hardware-based output interface 106. PMC 104 is configured to perform all processing necessary to operate System 103 (see, for example, the methods described in FIGS. 2-5, 6, 8, and 9 that may be at least partially executed by System 103), and includes a processor (not shown separately) and a memory (not shown separately). The processor of PMC 104 can be configured to execute several functional modules in accordance with computer-readable instructions implemented in a non-transitory computer-readable memory included in the PMC. Such functional modules are hereinafter referred to as being included in the PMC. The functional modules included in PMC 104 include a deep neural network (DNN) 112. DNN 112 is configured to enable data processing using a machine learning algorithm that outputs application-related data based on an image of a sample.

[0045] As a non-limiting example, the layers of DNN 112 can be organized according to a convolutional neural network (CNN) architecture, a recurrent neural network architecture, a recurrent neural network architecture, an adversarial generative network (GAN) architecture, or other methods. Optionally, at least some of the layers can be organized in multiple DNN sub-networks. Each layer of the DNN can generally include a number of basic computational elements (CEs) referred to in the art as dimensions, neurons, or nodes.

[0046] Generally, the computational elements of a given layer can be connected to the CEs of the preceding and / or succeeding layers. Each connection between the CEs of the preceding layer and the CEs of the succeeding layer is associated with a weighting value. A given CE can receive inputs from the CEs of the previous layer via each connection, and each given connection is associated with a weighting value that can be applied to the input of the given connection. The weighting values can determine the relative strength of the connections and thus the relative influence of each input on the output of a given CE. A given CE can be configured to calculate an activation value (e.g., a weighted sum of the inputs) and further derive an output by applying an activation function to the calculated activation. The activation function can be, for example, an identity function, a deterministic function (e.g., linear, sigmoid, threshold, etc.), a probabilistic function, or other suitable function. The output from a given CE can be sent to the CEs of the succeeding layer via each connection. Similarly, as described above, each connection at the output of the CE can be associated with a weighting value that can be applied to the output of the CE before being received as an input to the CEs of the succeeding layer. In addition to the weighting values, there may be thresholds (including limiting functions) associated with the connections and CEs.

[0047] The weighting values and / or thresholds of DNN112 can first be selected before training and can further be iteratively adjusted or changed during training to achieve an optimal set of weighting values and / or thresholds in the trained DNN. After each iteration, the difference (also called a loss function) between the actual output produced by DNN112 and the target output associated with each training data set can be determined. The difference may also be called an error value. Training can be determined to be complete when a cost function or loss function indicating the error value is less than a predetermined value or when a limited change in performance between iterations is achieved. Optionally, at least a portion (if any) of the DNN subnetwork can be trained separately before training the entire DNN.

[0048] System 103 is configured to receive input data via input interface 105. The input data can include data created by a test tool (and / or its derivatives and / or metadata associated therewith), and / or data created and / or stored in one or more data repositories 109 and / or in CAD server 110 and / or another associated data repository. Note that the input data can include images (e.g., captured images, images derived from captured images, simulated images, composite images, etc.) and associated numerical data (e.g., metadata, manually created attributes, etc.). Further note that the image data can include data related to the layer of interest and / or data related to one or more other layers of the sample.

[0049] System 103 is further configured to process at least a portion of the received input data and send the result (or a portion thereof) via output interface 106 to storage system 107, a test tool, a computer-based graphical user interface (GUI) 108 for rendering the result, and / or an external system (e.g., a FAB yield management system (YMS)). GUI 108 can be further configured to enable user-specified input related to operating system 103.

[0050] As a non-limiting example, the sample can be tested with one or more low-resolution testers 101 (e.g., an optical inspection system, a low-resolution SEM, etc.). The result data providing information of the low-resolution image of the sample (hereinafter referred to as low-resolution image data 121) can be sent to the system 103 (directly or via one or more intermediate systems). Alternatively or additionally, the sample can be tested with a high-resolution machine 102 (e.g., a subset of potential defect locations selected for review can be reviewed by a scanning electron microscope (SEM) or an atomic force microscope (AFM)). The result data providing information of the high-resolution image of the sample (hereinafter referred to as high-resolution image data 122) can be sent to the system 103 (directly or via one or more intermediate systems).

[0051] Note that images of the desired location of the sample can be captured at different resolutions. As a non-limiting example, the so-called "defect image" of the desired location can be used to distinguish defects from false alarms, while the so-called "class image" of the desired location is acquired at a higher resolution and can be used for defect classification. In some embodiments, multiple images of the same location (by the same or different resolutions) can include some images registered therebetween (e.g., multiple images captured from a given location and one or more reference images corresponding to the given location).

[0052] Note that the image data may be received and processed together with the metadata associated therewith (e.g., pixel size, text description of the defect type, parameters of the image capture process, etc.).

[0053] When processing input data (e.g., low-resolution image data and / or high-resolution image data, optionally together with other data such as, for example, design data, synthetic data, etc.), system 103 sends the results (e.g., instruction-related data 123 and / or 124) to any of the test tools, stores the results (e.g., defect attributes, defect classifications, etc.) in storage system 107, renders the results via GUI 108, and / or sends the results to an external system (e.g., to YMS).

[0054] Those skilled in the art will understand that the teachings of the subject matter of this disclosure are not limited by the system shown in FIG. 1, and equivalent functions and / or modified functions may be integrated or divided in other ways and may be implemented by appropriately combining software with firmware and / or hardware.

[0055] It should also be noted that, without in any way limiting the scope of this disclosure, the test tools can be realized as various types of inspection machines such as, for example, optical imaging machines, electron beam inspection machines, etc. In some cases, the same test tool can provide both low-resolution image data and high-resolution image data. In some cases, at least one test tool can have a metrology function.

[0056] The test system shown in FIG. 1 can be implemented in a distributed computing environment, and it should be noted that the functional modules shown in FIG. 1 can be distributed across several local and / or remote devices and linked through a communication network. Further, in other embodiments, at least some of the test tools 101 and / or 102, the data repository 109, the storage system 107, and / or the GUI 108 are outside the test system 100 and can operate by communicating data with the system 103 via the input interface 105 and the output interface 106. The system 103 can be implemented as a stand-alone computer that can be used in connection with the test tools. Alternatively, each function of the system can be at least partially integrated into one or more of the test tools.

[0057] Next, attention is directed to FIG. 2. The method includes obtaining (operation 200) an image 250 of a sample. According to some embodiments, the image 250 is acquired by a test tool (such as test tool 101) during the in-operation scanning of the sample. The sample includes one or more arrays 260. The array 260 includes repetitive structural elements (represented as reference 261 in one of the arrays). The repetitive structural elements include, for example, memory cells (such as SRAM, DRAM, FRAM, flash memory), programmable logic cells, and the like. These examples are not limiting. Generally, the repetitive structural elements are arranged in each array according to a repetitive pattern or lattice. For example, the distance (by the horizontal and vertical axes) between two adjacent repetitive structural elements is constant or at least substantially constant among the various arrays.

[0058] The sample includes one or more regions 265. Each region 265 at least partially surrounds a corresponding array 260. The regions 265 do not include repetitive structural elements present within the array 260. In the non-limiting example of FIG. 2A, the sample includes vertical and horizontal regions 265 that surround the array 260. The regions 265 can correspond to, for example, stitches. Each region 265 includes features that are different from the repetitive structural elements 260. In some embodiments, the regions 265 can include non-repetitive features and / or repetitive features that are different from the repetitive structural elements 260. Examples of non-repetitive features include, for example, logic. However, this is not limiting.

[0059] This method further includes performing (operation 210) a correlation analysis between the pixel intensities of the image 250 and the pixel intensities of a reference image that provides information about at least one of the repetitive structural elements. The reference image can include, for example, an image of one of the repetitive structural elements. The reference image is also referred to as a "golden cell". According to some embodiments, the reference image is generated based on design data. According to some embodiments, the reference image is obtained from an image of a structural element that is known to be defect-free (e.g., from a previous analysis). According to some embodiments, the reference image is obtained during a setup phase prior to the in-operation testing of the sample. During the setup phase, the time and processing constraints are not as stringent, and thus it is possible to acquire an image of one of the repetitive structural elements that make up the reference image.

[0060] The output of the correlation analysis performed in 210 can include a correlation matrix that includes a plurality of values. Each value is associated with a sub-region of the image 250 and indicates the level of correlation between the pixel intensities of the sub-region and the pixel intensities of the reference image.

[0061] This method can further include using a correlation matrix (operation 220) to distinguish one or more first regions of the image 250 corresponding to one or more arrays 260 from one or more second regions of the image corresponding to one or more regions 265. FIG. 2A shows an example of one or more first regions 270 and one or more second regions 275. In some embodiments, all regions of the image not identified as belonging to one or more first regions 270 are considered part of one or more second regions 275.

[0062] This method further includes outputting 230 data providing information about one or more first regions 270 of the image 250. This can include, for example, outputting the location of one or more first regions 270 in the image 250 and / or outputting a selected one of the images 250 that includes only one or more first regions 270. According to some embodiments, this method can include outputting the location of one or more second regions 275 and / or outputting a selected one of the images 250 that includes only one or more second regions 275.

[0063] According to some embodiments, at least operations 210, 220, and 230 are performed during the run-time scanning of the sample. In other words, the method of identifying an array in an image is efficient and can therefore be performed during the run-time phase.

[0064] According to some embodiments, the identification of one or more first regions 270 in the image is used during the run-time scanning of the sample, for example, by a PMC configured to determine data representing defects of the array (e.g., location of the defect, class of the defect, etc.). In particular, the PMC can implement an algorithm for detecting defects that is specifically designed to detect defects in an array containing repetitive structural elements.

[0065] As shown in FIG. 2A, one or more arrays 260 are separated from one or more regions 265 by one or more boundaries 266. The boundary 266 defines a physical limit between the array 260 and the corresponding surrounding region 265.

[0066] According to some embodiments, this method enables estimating one or more first regions 270 of an image 250 that include only at least one or more arrays 260 up to the boundary 266. In particular, according to some embodiments, this method enables identifying the array 260 up to the boundary 266, excluding one or more second regions 275 corresponding to the one or more regions 265.

[0067] Turning to FIG. 3, which shows a non-limiting embodiment of operations 210 to 230.

[0068] As described with reference to operation 210, a correlation matrix is obtained. A non-limiting example of a correlation matrix 365 obtained in a given region of the image 250 is shown in FIG. 3A.

[0069] Therefore, this method can include determining (operation 300) a sub-region of the image 250 corresponding to the values of the correlation matrix 365 that satisfy an amplitude criterion. The amplitude criterion can be defined, for example, such that a sub-region of the image 250 where a local maximum correlation peak is identified (in some embodiments, an absolute threshold can be set) corresponds to the location of a repetitive structural element in the image 250. According to some embodiments, during a setup phase prior to the in-run testing of the sample, a first estimate of the amplitude of the correlation peak obtained in a sub-region including one of the repetitive structural elements is obtained, which can be used to determine the amplitude criterion used during the in-run testing, and the presence of the structural element is taken into account in the amplitude criterion.

[0070] As shown in FIG. 3A, the correlation matrix 365 includes correlation peaks (maxima) 367 located in sub-regions 375 of the image. These sub-regions 375 correspond to the estimation of the locations of the repetitive structural elements. In fact, since the correlation analysis involves correlating the pixel intensities of the image with the pixel intensities of a reference image providing information on the repetitive structural elements, it is expected that the sub-regions of the image containing the repetitive structural elements will provide higher correlation values as compared to the sub-regions of the image not containing the repetitive structural elements (region 265).

[0071] This method can further include clustering (operation 310) the sub-regions 375 into one or more clusters based on data providing information on the distances between the repetitive structural elements in the array.

[0072] As described above, the repetitive structural elements are generally arranged according to a repetitive pattern or a lattice. Therefore, it is possible to obtain the distance between two consecutive structural elements in the array. In some embodiments, data providing information on the distances between the repetitive structural elements 361 in the array along a first axis (e.g., the horizontal axis, corresponding to the rows of the image) (see reference 368 in FIG. 3B), and data providing information on the distances between the repetitive structural elements in the array along a second axis (e.g., the vertical axis, corresponding to the columns of the image) (see reference 369 in FIG. 3B) can be obtained. As shown, the distance can be measured between the centers of the structural elements.

[0073] According to some embodiments, operation 310 can include clustering sub-regions 375 into one or more first clusters based on data providing information about the distances between repeating structural elements in the array along a first axis 372. According to some embodiments, within a given cluster, a sub-region 375 is located at a distance less than or equal to the distance between repeating structural elements along the first axis from another sub-region of the cluster. A non-limiting example of sub-regions assigned to the same cluster 370 along the first axis 372 is shown in FIG. 3A. As shown, sub-region 374 is not assigned to cluster 370 because the distance from each sub-region to cluster 370 exceeds the distance between two repeating structural elements along the first axis 372.

[0074] According to some embodiments, operation 310 can include clustering sub-regions into one or more second clusters based on data providing information about the distances between repeating structural elements in the array along a second axis 373. According to some embodiments, within a given cluster, a sub-region is located at a distance less than or equal to the distance between repeating structural elements along the second axis from another sub-region of the cluster. A non-limiting example of sub-regions assigned to the same cluster 381 along the second axis 373 is shown in FIG. 3A. As shown, sub-region 383 is not assigned to cluster 381 because the distance from sub-region 383 to cluster 381 exceeds the distance between two repeating structural elements along the second axis 373.

[0075] This method includes determining (operation 320) one or more first regions based at least on one or more clusters. In particular, the first cluster can be used to determine the size and location of one or more first regions along the first axis 372, and the second cluster can be used to determine the size and location of one or more first regions along the second axis 373. For example, cluster 370 provides the size and location of a first region along axis 372, and cluster 381 that intersects cluster 370 provides the size and location of the same first region along axis 373. As a result, the first region 384 is identified. This can be done for all clusters, and all clusters are used to determine the boundaries of different first regions.

[0076] According to some embodiments, another operation is performed to identify the first region using the clusters. In particular, this method can include determining a polygon (e.g., a rectangle or a square) that encloses one or more clusters identified to define the first region, and outputting the polygon as the first region. For example, in the example of FIG. 3A, a rectangle 392 can be generated that encompasses the first region identified based on the first cluster 370 and the second cluster 381.

[0077] Next, focus on FIG. 3C. According to some embodiments, this method can include selecting (operation 330) only clusters in which some sub-regions present in the cluster meet a threshold (e.g., exceed the threshold). This is shown in FIG. 3C. A plurality of clusters 3831 to 3835 are identified along axis 372. The cluster referred to as 3831 includes only one sub-region 384. Since it is known that the array includes repeating structural elements arranged along a repeating pattern (e.g., a grid), it can be assumed that the sub-region 384 does not correspond to a structural element because the repeating pattern does not include isolated structural elements. Therefore, this cluster can be ignored or deleted when determining one or more first regions in operation 320. The same can be applied to clusters (second clusters) determined along a second axis 373. If a given cluster includes some sub-regions that are below the threshold, the given cluster is ignored when determining one or more first regions in operation 320.

[0078] Next, focus on FIG. 4. According to some embodiments, a reference image providing information about at least one of the repeating structural elements can be processed using an image processing algorithm. According to some embodiments, the image processing algorithm attenuates the repeating pattern of the reference image. For example, partial whitening can be applied to the reference image. Partial whitening can include, for example, converting the reference image to the frequency domain (e.g., converting X(i,j) representing the pixels of the reference image to X(f) in the frequency domain) and degrading high / strong frequencies (e.g.,

[0079]

Number

[0080] A non-limiting example of the method of FIG. 4 is shown in FIG. 4A. As shown, the array 460 includes repetitive structural elements 410 and conductive lines 411. The conductive lines 411 extend to a region 465 surrounding the array 460. As shown in FIG. 4B, a reference image 470 providing information on the repetitive structural elements 410 is obtained. Image processing (e.g., partial whitening) that attenuates the repetitive pattern is applied to the reference image 470 to obtain a modified reference image 475. As shown, both the conductive lines 411 (corresponding to the repetitive pattern) and the structural elements 410 (also corresponding to the repetitive pattern) are attenuated in the modified reference image 475. As a result, when a correlation is performed between the modified reference image 475 and the image (as described with reference to operation 210), both the sub-regions and the regions include a common repetitive feature (conductive lines 411) in this embodiment, but the sub-regions corresponding to the structural elements provide a higher correlation value than the sub-regions corresponding to the regions, thereby facilitating the distinction between the array and the surrounding area.

[0081] According to some embodiments, the method can include obtaining (operation 500) an image of a sample that includes one or more arrays and one or more surrounding regions. Operation 500 is similar to operation 200 described above. In some embodiments, the image is obtained by an electron beam test tool. In some cases, the signal-to-noise ratio of the image may be low, and thus the method of FIG. 2 that requires correlation with a reference image may not always be applicable. The low signal-to-noise ratio may be due to, for example, the size of the features present in the sample, charging effects, etc. In some embodiments, due to the low signal-to-noise ratio, the structural elements of the array cannot be identified / distinguished within the array by visual inspection of the image. In some embodiments, the pixel size of the image may be larger than the size of the structural elements, and thus the structural elements cannot be distinguished by visual inspection.

[0082] The method further includes obtaining (operation 510) data D that provides information on the pixel intensity of at least one of the array and the surrounding region. threshold thresholdcan be obtained prior to the run-time test of the sample, particularly during the setup phase. For example, during the setup phase, an image of a sample similar to the sample under test during run-time is obtained. An operator or an automatic algorithm (e.g., the K-means algorithm) provides an initial estimate of the location of the array and the surrounding area within the image. The average value P array of the pixel intensities of the array is calculated, and the average value P region of the pixel intensities of the surrounding area is calculated. These two values are expected to be different since the array and the surrounding area contain different structural features. D threshold can be calculated, for example, based on P array and P region . D threshold can correspond to, for example, the average between these two values, but this is not limiting.

[0083] This method further includes determining (operation 520) data representing pixel intensities along multiple axes of the image. This particularly includes data D X representing pixel intensities along each of a plurality of rows of the image and data D Y representing pixel intensities along each of a plurality of columns of the image. Data D X (or D Y ) can be calculated, for example, as the average value of the pixel intensities along each row (or column) of the image.

[0084] This method further includes using (operation 530) D X , D Y , and D threshold to distinguish one or more first regions of the image corresponding to one or more arrays from one or more second regions of the image corresponding to one or more areas.

[0085] Operation 530 includes rows of the image for which D X exceeds (or is less than, depending on whether the pixel intensity is higher with respect to the array or the surrounding area) a threshold D threshold (e.g., obtained during the setup phase), and DY is the threshold D threshold It can include identifying a column of images that exceeds (e.g., obtained during the setup phase). The location of the array is identified by the intersection of the identified rows and columns.

[0086] Non-limiting examples are provided in FIG. 5A. For example, during the setup phase, it is determined that the array has a higher (on average) pixel intensity than the surrounding area (P array is P region greater than), and D threshold is P array and P region is assumed to be set as the average value of. The data D providing information on the pixel intensity along the row X is shown as curve 545 (this curve is purely illustrative and not limiting). In the row of the image where the array 562 is located, as shown, the curve exceeds the threshold D threshold (referred to as 548). The data D providing information on the pixel intensity along the column Y is shown as curve 561 (this curve is purely illustrative and not limiting). In the column of the image where the array 562 is located, as shown, the curve exceeds the threshold D threshold (referred to as 548).

[0087] This method further includes outputting (operation 540) data providing information on one or more first regions of the image. Operation 540 is similar to operation 230 described above. In particular, according to some embodiments, the location of the first region corresponding to the location of the array is estimated by the intersection between the row where curve 545 exceeds threshold 548 and the column where curve 561 exceeds threshold 548.

[0088] According to some embodiments, at least operations 510, 530, and 540 are performed during the in - run scanning of the sample. In other words, the method for identifying an array in an image is efficient and can therefore be performed during the in - run phase.

[0089] Next, turn attention to FIG. 6. In some cases, data representing pixel intensities along the rows and / or columns of the image in which the array is located may be close to data representing pixel intensities along other rows and / or columns. A non-limiting example where the array 660 is located at the lower left corner of the image and surrounded by a large region 665 is shown in FIG. 6A. The method of FIG. 6 is a possible embodiment of a solution that enables improving the distinction between the rows and columns of the image in which the array is located and other rows and columns.

[0090] This method includes determining (operation 610) data D representing pixel intensities (e.g., the average of pixel intensities along a row) along each of a plurality of rows of the image. X D X is represented as curve 668 in FIG. 6A. Assume that a first threshold D threshold,1 has been obtained (e.g., during a setup phase before a run-time test). D threshold,1 provides information on the pixel intensities of at least one of the array and the region. In some embodiments, D threshold,1 can be selected as a strict (high) threshold in order to maximize the probability of distinguishing the row of the image corresponding to the array from other rows. For example, during the setup phase (performed on an image of a sample similar to the sample under test during run-time), the average value P array of the pixel intensities of the array is calculated, and assume that the average value P region of the pixel intensities of the surrounding region has been calculated (as described above). For example, assume that P array is higher than P region . D threshold,1 can be selected with a value higher than P region to maximize the probability of removing the row corresponding to the surrounding region. For example, D threshold,1 can be selected as follows. D threshold,1 = P region + N * σ (where σ is the standard deviation of the pixel intensities of the surrounding region and N is an integer equal to, for example, 2).

[0091] This method includes X when D threshold,1A subset S of the image that includes the rows of the image that exceed L (represented as 682). As shown, the subset S L includes the row 683 of the image where the array 660 is located and additional rows 684 of the image that do not include the array 660 (however, the pixel intensity of these additional rows is D threshold,1 exceeds). This method is for the subset S L Determine (operation 630) data D (such as the average of the pixel intensities along the columns) representing the pixel intensities along each of the multiple columns of Y,SL (curve 686 in FIG. 6B). This method is such that D Y,SL exceeds the second threshold 690 for the subset C L of the columns of S SL (referred to as 689). This second threshold 690 can be obtained based on the measurements performed during the setup phase prior to the run-time test. For example, the second threshold can be set equal to the average value of P array (the average pixel intensity of the array) and P region (the average pixel intensity of the surrounding area). However, this is not limiting.

[0092] Column C SL (reference 689) indicates the position of the array along the row axis. Then, it is possible to perform the determination of the position and size of the array along the column axis (Y-axis) of the image, thereby resulting in one or more first regions corresponding to the array in the image (operation 650). It is possible to provide the position of one or more first regions (corresponding to the array) in the image and / or the position of one or more second regions (corresponding to the surrounding area) (corresponding to all regions not identified as the first region) in the image.

[0093] In fact, after the column 689 of the image corresponding to the array is identified, then, as can be clearly seen in FIG. 6C, it is easier to distinguish the rows of the image that include the array from the other rows of the image. A subset S' of the image L(Referenced as 692 in FIG. 6C) is possible. This subset S’ L includes all rows of the image and is limited to the columns 689 of the image identified in the previous operation. This method is for subset S’ L (692) determining data (referred to as 693) representing the pixel intensity (e.g., average pixel intensity) along each of a plurality of rows, and determining the rows 694 of the image where the data 693 exceeds a third threshold 695 (in some embodiments, the third threshold 695 is equal to the second threshold 690, but this is not essential). As can be clearly seen in FIG. 6C, it is now easier to distinguish the rows of the image containing the array from the other rows based on the pixel intensity. These rows 694, together with column 689, define one or more first regions of the image corresponding to the array. The other regions of the image correspond to the second regions of the image corresponding to the area surrounding the array.

[0094] In the example from FIGS. 6 to 6C, this method starts by selecting a subset S of the image that includes the rows where the average pixel intensity exceeds a threshold. L It should be understood that this method can be executed in the same way by first selecting a subset of columns. In this case, the method is - determining data D representing the pixel intensity along each of a plurality of columns of the image (corresponding to operation 610) Y and - selecting a subset S of the image that includes the columns of the image where D Y exceeds a first threshold (corresponding to operation 620) C and - determining data D representing the pixel intensity along each of a plurality of rows of subset S C (corresponding to operation 630) X,SC and - determining a subset L of the rows of S X,SC where D C exceeds a second threshold (corresponding to operation 640) SC and - L SCdetermining one or more first regions corresponding to the array based at least on (corresponding to operation 650 - since the rows of the image corresponding to the array are known, it becomes easier to identify the columns of the image corresponding to the array, similar to that described with reference to FIG. 6C) and can include.

[0095] According to some embodiments, this method further enables estimating one or more first regions of an image that includes only at least one or more arrays up to a boundary separating the array from the surrounding region. In particular, according to some embodiments, this method enables identifying the array up to the boundary, excluding one or more second regions corresponding to one or more regions.

[0096] Next, turn to FIG. 7. Assume that an image of the sample has been acquired and one or more first regions corresponding to one or more arrays 710, each including a repetitive structural element 720, have been identified. This identification can rely on, for example, the various embodiments described above or on other identification methods. Therefore, the image 700 limited to one or more first regions (corresponding to the arrays) is available without the region surrounding the one or more arrays. In some embodiments, the image 700 includes both one or more first regions (corresponding to the arrays) and one or more second regions (corresponding to the regions). Since the position of the first region is known, it is possible to operate only on the first region. Hereinafter, the image 700 including only the first region (corresponding to the array) will be referred to, but it should be understood that this method can be similarly applied to an image including both the first region and the second region by applying this method only to the first region of the image.

[0097] As can be clearly seen in FIG. 7, in some embodiments, the image 700 of the array is distorted. In particular, the position of the structural element 720 in the array that is clearly visible in the image 700 does not match the intended position in the array (the true position in the sample). This may be due to various factors such as measurement errors in test tools and the like.

[0098] When attempting to use the image 700 for various applications such as defect detection and / or classification, the distortion can be a problem. Therefore, it is necessary to correct this distortion. FIG. 8 shows an embodiment of a method for correcting the distortion present in the image of the array.

[0099] This method includes performing (operation 800) a correlation analysis between the pixel intensities of the image 700 and the pixel intensities of a reference image that provides information about at least one of the repetitive structural elements. The reference image used in operation 800 may be different from the reference image used in operation 210 to identify the first region of the image corresponding to the array (in this case, a second reference image different from the first reference image used in operation 210 is used in operation 800). However, this is not essential. The output of the correlation analysis is a second correlation matrix (which may be different from the correlation matrix obtained in operation 210). In some embodiments, it is possible to reuse the correlation matrix obtained in operation 210 (in this case, only the values corresponding to one or more first regions are used).

[0100] This method can further include determining (operation 810) a sub-region of the image corresponding to the values of the second correlation matrix that meet the amplitude criterion. In particular, the amplitude criterion can require that a sub-region of the image related to the maximum value (e.g., local maximum value) of the second correlation matrix be identified.

[0101] As shown in FIG. 8A, the second correlation matrix 860 includes the peaks (maxima) of the correlations located in a given sub-region 685. These sub-regions 865 correspond to the estimation of the locations of the repetitive structural elements (particularly, to the central regions of each structural element). In fact, since the correlation analysis involves correlating the pixel intensities of the image with the pixel intensities of a reference image providing information on the repetitive structural elements, it is expected that the sub-regions of the image 700 containing the repetitive structural elements will provide higher correlation values as compared to the sub-regions of the image 700 not containing the repetitive structural elements.

[0102] This method can further include determining (operation 820) a distortion map between the image 700 and the array. The distortion map can be determined based on data providing information on the positions of the sub-regions (determined using the second correlation matrix) and the planned positions of the repetitive structural elements in the array.

[0103] A non-limiting example showing the positions of the sub-regions 865 corresponding to the maxima of the second correlation matrix, and the planned positions 866 of the structural elements in the array, is shown in FIG. 8B. For each sub-region, it is possible to determine a distortion vector 867 showing the difference between the position of the structural element in the image (estimated using the maxima of the second correlation matrix) and the planned position 866 of the corresponding structural element.

[0104] According to some embodiments, the distortion map can be determined for the entire image. In fact, as described above, the distortion (see 867, hereinafter "DF") between the positions of the sub-regions 865 in the image and the data providing information on the planned positions of the repetitive structural elements in the array is determined. This corresponds to the distortion of the central part of each of the structural elements with respect to the planned position. To determine the distortion of the other pixels of the image (not necessarily corresponding to the central parts of the structural elements), this method calculates DF central across the image. centralIt can include applying an interpolation method to the value of . This enables the estimation of the distortion of all other pixels located between different sub-regions 865. According to some embodiments, the interpolation method is applied separately to the distortion along the X-axis (rows of the image) and the distortion along the Y-axis (columns of the image).

[0105] This method can further include generating (operation 830) a corrected image 880 (see FIG. 8C) based on the distortion map. This can include moving the pixels of the image based on the distortion map, such that the position of the sub-region 865 in the corrected image 880 (corresponding to the peak of the second correlation matrix) and the position of the data providing information on the planned position of the repetitive structural elements in the array satisfy a proximity criterion (e.g., the difference in positions is less than a threshold).

[0106] Next, attention is drawn to FIG. 9. According to some embodiments, the method can include obtaining (900) a reference image providing information on at least one of the repetitive structural elements and selecting (910) only a subset of the reference image as a second reference image. According to some embodiments, the size of the subset is based on a compromise. On the one hand, the size of the subset must be large enough to identify the positions of the structural elements of the image, and on the other hand, the size of the subset must be small enough to obtain a sufficient number of correlation values.

[0107] A non-limiting example is shown in FIG. 9A.

[0108] A reference image 920 has been obtained. A subset 930 of the reference image 920 is selected. This subset can be used as the second reference image in the method of FIG. 8.

[0109] According to some embodiments, subset 930 can be selected using an iterative method, for example, during a setup phase. This method starts with a first subset, the maximum size of which can be set by the user, for example. The method of FIG. 8 is executed using this first subset. Then, the resolution is improved, which means that the size of the first subset is decreased. The method of FIG. 8 is executed again using this new subset, and the performance of the output is compared to the previous iteration. If the performance is improved, the method is repeated with a new subset of a smaller size. If the performance is not improved, the method is stopped and the subset obtained in the previous iteration is selected.

[0110] It should be understood that the present invention is not limited to the details described in the description contained herein or shown in the drawings in its application.

[0111] It will also be understood that the system according to the present invention can be implemented, at least in part, by a suitably programmed computer. Similarly, the present invention contemplates a computer program readable by a computer for performing the method of the present invention. The present invention further contemplates a non-transitory computer readable memory tangibly embodying a program of instructions executable by a computer for performing the method of the present invention.

[0112] It should be understood that the present invention is capable of other embodiments and of being practiced and carried out in various ways. Accordingly, the syntax and terminology used herein are for the purpose of description and should not be regarded as limiting. Thus, those skilled in the art will understand that the concepts on which this disclosure is based can be readily utilized as a basis for designing other structures, methods, and systems for carrying out some of the purposes of the subject matter of this disclosure.

[0113] Those skilled in the art will readily understand that various modifications and changes can be applied to the embodiments of the present invention as described above without departing from the scope defined in the appended claims and by the appended claims.

Description of Reference Numerals

[0114] 100 Test system 101 Low-resolution test tool 102 High-resolution test tool 103 Computer-based system, operating system 104 Processor and memory circuit (PMC) 105 Hardware-based input interface 106 Hardware-based output interface 107 Storage system 108 Graphical user interface (GUI) 109 Data repository 110 CAD server 112 Deep neural network (DNN) 121 Low-resolution image data 122 High-resolution image data 123 Instruction-related data 124 Instruction-related data 250 Image 260 Iterative structure element, array 265 Region 266 Boundary 270 First area 275 Second area 361 Iterative structure element 365 Correlation matrix 367 Peak of correlation (maximum value) 368 Reference 369 Reference 370 Cluster 372 First axis 373 Second axis 374 Sub-region 375 Sub-region 381 Cluster Clusters 3831 - 3835 383 Sub - region 384 Sub - region 392 Rectangle 410 Iterative structure element 411 Conductive line 460 Array 465 Region 470 Reference image 475 Modified reference image 545 Curve 548 Threshold value 561 Curve 562 Array 660 Array 665 Large region 668 Curve 683 Image row 684 Additional image row 685 Sub - region 686 Curve 689 Column 690 Second threshold value 692 Image subset 693 Data 694 Row 695 Third threshold value 700 Image 710 Array 720 Iterative structure element, structure element 860 Second correlation matrix 865 Sub - region 866 Scheduled position 867 Vector of distortion 880 Modified image 920 Reference image 930 Subset

Claims

1. A system for testing a semiconductor sample, the system comprising: one or more arrays, each including a repetitive structural element; one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements; a processor and memory circuit (PMC) configured to obtain an image of the semiconductor sample including the above; wherein the PMC is configured to perform a correlation analysis between the pixel intensity of the image and the pixel intensity of a reference image providing information of at least one of the repetitive structural elements during runtime scanning of the semiconductor sample to obtain a correlation matrix; use the correlation matrix to distinguish one or more first areas of the image corresponding to the one or more arrays from one or more second areas of the image corresponding to the one or more regions; and output data providing information of the one or more first areas of the image. A system configured as such.

2. Determine a sub-region of the image corresponding to a value of the correlation matrix that meets an amplitude criterion, cluster the sub-region into one or more clusters based on data providing information about the distance between the repetitive structural elements in the array, and determine the one or more first areas based at least on the one or more clusters. The system according to Claim 1, configured as such.

3. The system according to Claim 1, wherein the one or more arrays are separated from the one or more regions by one or more boundaries, and the system is configured to estimate the one or more first areas of the image including only the at least one or more arrays up to the boundaries.

4. The system according to Claim 1, configured to apply image processing to the reference image, wherein the image processing attenuates the repetitive pattern of the reference image.

5. Cluster the sub-region into one or more first clusters based on data providing information about the distance between the repetitive structural elements in the array along a first axis, and cluster the sub-region into one or more second clusters based on data providing information about the distance between the repetitive structural elements in the array along a second axis. ​ ​ ​ ​ ​ ​ ​ Using the first cluster and the second cluster to distinguish one or more first regions of the image corresponding to the one or more arrays and one or more second regions of the image corresponding to the one or more regions The system according to claim 1, configured as such.

6. For each cluster, - Determine a polygon surrounding the one or more clusters, - Output the polygon as a first region of the image The system according to claim 2, configured as such.

7. The system according to claim 2, configured to select only clusters in which some sub-regions satisfy a threshold.

8. The system according to claim 2, configured to obtain data for providing information on the amplitude reference in a setup phase before a run-time test of the semiconductor sample.

9. To obtain a second correlation matrix, perform a correlation analysis between the pixel intensities of the one or more first regions of the image and the pixel intensities of a second reference image providing information on at least one of the repetitive structural elements, Determine sub-regions of the one or more first regions of the image corresponding to values of the second correlation matrix that satisfy an amplitude reference, Based at least on the positions of the sub-regions in the one or more first regions of the image and data providing information on the planned positions of the repetitive structural elements in the array, determine a distortion map between the one or more first regions of the image and the array, Generate a corrected image based on the distortion map The system according to claim 1, configured as such.

10. The system according to claim 9, configured to generate the corrected image such that the positions of the sub-regions in the corrected image and data providing information on the planned positions of the repetitive structural elements in the array satisfy a proximity reference.

11. The distortion DF between the position of the sub-region of the one or more first regions of the image and the data providing information on the planned position of the repetitive structural elements in the array central is determined, At least DF central Based on an interpolation method applied to at least central , determine a distortion map between the one or more first regions of the image and the array of the semiconductor sample The system according to claim 9, configured as such.

12. The system according to claim 9, configured to obtain a reference image providing information on at least one of the repetitive structural elements and select only a subset of the reference image as the second reference image.

13. A method for testing a semiconductor sample, the method being performed by a processor and a memory circuit (PMC), One or more arrays each including a repetitive structural element, Each region at least partially surrounds the corresponding array and includes one or more regions having features different from the repeating structural elements obtaining an image of the semiconductor sample including during the on-the-fly scanning of the semiconductor sample performing a correlation analysis between the pixel intensity of the image and the pixel intensity of a reference image providing information on at least one of the repeating structural elements to obtain a correlation matrix using the correlation matrix to distinguish one or more first regions of the image corresponding to the one or more arrays from one or more second regions of the image corresponding to the one or more regions outputting data providing information on the one or more first regions of the image A method comprising

14. determining a sub-region of the image corresponding to a value of the correlation matrix that meets an amplitude criterion clustering the sub-region into one or more clusters based on data providing information on the distance between the repeating structural elements in the array determining the one or more first regions based at least on the one or more clusters The method according to claim 13, comprising

15. The method according to claim 13, wherein the one or more arrays are separated from the one or more regions by one or more boundaries, and the method includes estimating one or more first regions of the image including only the at least one or more arrays up to the boundaries and excluding the one or more second regions corresponding to the one or more regions

16. clustering the sub-region into one or more first clusters based on data providing information on the distance between the repeating structural elements in the array along a first axis clustering the sub-region into one or more second clusters based on data providing information on the distance between the repeating structural elements in the array along a second axis using the first cluster and the second cluster to distinguish one or more first regions of the image corresponding to the one or more arrays from one or more second regions of the image corresponding to the one or more regions The method according to claim 13, comprising

17. The method according to claim 13, comprising selecting only clusters in which some sub-regions satisfy a threshold value.

18. Performing a correlation analysis between the pixel intensities of the one or more first regions of the image and the pixel intensities of a second reference image providing information of at least one of the iterative structural elements to obtain a second correlation matrix; Determining sub-regions of the one or more first regions of the image corresponding to values of the second correlation matrix that satisfy an intensity criterion; Determining a distortion map between the one or more first regions of the image and the array based at least on the positions of the sub-regions in the one or more first regions of the image and data providing information on the planned positions of the iterative structural elements in the array; Generating a corrected image based on the distortion map The method according to claim 13, comprising.

19. The distortion DF between the position of the sub-region of the one or more first regions of the image and the data providing information on the planned position of the repetitive structural elements in the array central and determining At least DF central determining a distortion map between the one or more first regions of the image and the array of the semiconductor sample based on an interpolation method applied to central The method according to claim 18, comprising.

20. A non-transitory computer-readable medium including instructions that, when executed by a PMC, cause the PMC to acquire an image of the semiconductor sample including one or more arrays each including an iterative structural element; one or more regions each surrounding at least partially a corresponding array and including features different from the iterative structural elements; During the in-operation scanning of the semiconductor sample, Performing a correlation analysis between the pixel intensities of the image and the pixel intensities of a reference image providing information of at least one of the iterative structural elements to obtain a correlation matrix; Using the correlation matrix to distinguish one or more first regions of the image corresponding to the one or more arrays from one or more second regions of the image corresponding to the one or more regions; Outputting data providing information on the one or more first regions of the image A non-transitory computer-readable medium for causing an operation to be performed, including. ​

Images