Layout defect position determination method, device, equipment, medium and program product

Abstract

The application discloses a layout defect position determination method, device, equipment, medium and program product, and relates to the technical field of semiconductor integrated circuits. The layout defect position determination method comprises the following steps: determining a candidate layout matched with a pattern feature of a target pattern in a pattern library from a plurality of sub-layouts of a mask design layout; performing random sampling on the candidate layout according to a preset sampling strategy to obtain a plurality of second random sampling coordinates; matching each second random sampling coordinate with each first random sampling coordinate in the target pattern to obtain a pattern similarity of the candidate layout and the target pattern; and determining a position of the candidate layout as a layout defect position of the mask design layout in a case where the pattern similarity is greater than a preset similarity threshold, wherein the layout defect position is a position of a defect point in the mask design layout that does not meet a process window requirement.

Description

Technical Field

[0001] This application belongs to the field of semiconductor integrated circuit technology, and particularly relates to a method, apparatus, device, storage medium and program product for determining the location of layout defects. Background Technology

[0002] In the manufacturing process of semiconductor integrated circuits, it is necessary to model the mask design layout of the chip photolithography mask multiple times, and correct the location of the simulated bad points generated by the modeling to repair potential defects in the mask design.

[0003] In existing methods, a bad pixel database is established based on simulated bad pixels generated by modeling. Then, the location of the corresponding bad pixel in the mask design layout is determined by performing precise graphic matching between the mask design layout and the bad pixel database.

[0004] However, the patterns in the mask design layout often have missing parts or numerous minor modifications, resulting in lower accuracy in locating bad pixels. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and program product for determining the location of layout defects, which can improve the accuracy of the location of layout defects.

[0006] One aspect of this application provides a method for determining the location of a layout defect, comprising:

[0007] Candidate layouts that match the graphic features of the target graphic in the graphic library are determined from multiple sub-layouts of the mask design layout. The graphic library includes at least one simulated bad pixel graphic. The simulated bad pixel graphic is a local defect point graphic that does not meet the process window requirements and is determined after photolithography simulation of the mask design layout. The simulated bad pixel graphic includes multiple first random sampling coordinates obtained by random sampling according to a preset sampling strategy. The target graphic is any one of the simulated bad pixel graphics.

[0008] Random sampling is performed on the candidate layout according to a preset sampling strategy to obtain multiple second random sampling coordinates;

[0009] The second random sampling coordinates are matched with the first random sampling coordinates in the target graphic to obtain the graphic similarity between the candidate layout and the target graphic.

[0010] If the graphic similarity is greater than the preset similarity threshold, the position of the candidate layout is determined as the layout defect position of the mask design layout. The layout defect position is the location of the defect point in the mask design layout that does not meet the process window requirements.

[0011] One aspect of this application provides a device for determining the location of a layout defect, comprising:

[0012] The layout determination module is used to determine candidate layouts that match the graphic features of the target graphic in the graphic library from multiple sub-layouts of the mask design layout. The graphic library includes at least one simulated bad pixel graphic, which is a local defect point graphic that does not meet the process window requirements after photolithography simulation of the mask design layout. The simulated bad pixel graphic includes multiple first random sampling coordinates obtained by random sampling according to a preset sampling strategy. The target graphic is any one of the simulated bad pixel graphics.

[0013] The coordinate sampling module is used to randomly sample in the candidate layout according to a preset sampling strategy to obtain multiple second random sample coordinates;

[0014] The similarity determination module is used to match each second random sampling coordinate with each first random sampling coordinate in the target graphic to obtain the graphic similarity between the candidate layout and the target graphic.

[0015] The location determination module is used to determine the location of the candidate layout as the location of the layout defect in the mask design layout when the graphic similarity is greater than a preset similarity threshold. The layout defect location is the location of the defect point in the mask design layout that does not meet the process window requirements.

[0016] In one aspect of this application, an electronic device is provided, the device comprising: a memory and a program or instructions stored in the memory and executable on a processor, wherein when the program or instructions are executed by the processor, the method for determining the location of a layout defect as provided in any aspect of the above-described embodiments of this application is implemented.

[0017] In one aspect of the embodiments of this application, a readable storage medium is provided, on which a program or instruction is stored, and when the program or instruction is executed by a processor, it implements the method for determining the location of a layout defect as provided in any aspect of the embodiments of this application above.

[0018] In one aspect of the embodiments of this application, a computer program product is provided, wherein when the instructions in the computer program product are executed by the processor of an electronic device, the electronic device performs a method for determining the location of a layout defect as provided in any aspect of the embodiments of this application described above.

[0019] The method for determining the location of layout defects provided in this application first obtains candidate layouts in the mask design layout that match the graphic features of a target graphic in a graphic library. The target graphic is any one of the simulated defective graphics in the graphic library. Preliminary screening of each sub-layout in the mask design layout is performed using graphic features. Then, a second random sampling coordinate is generated in the candidate layout, and this second random sampling coordinate is matched with the first random sampling coordinate in the target graphic. The location of the candidate layout is determined based on the graphic similarity to determine whether it is the location of a layout defect in the mask design layout. Thus, by performing fuzzy matching using random sampling coordinates, the location of a layout defect in the mask design layout can be determined as long as the graphic similarity is greater than a preset similarity threshold. Even if the graphic in the mask design layout is missing or has undergone numerous minor modifications, it is still possible to accurately determine whether the candidate layout matches the target graphic. This improves the accuracy of graphic matching and thus accurately determines the location of layout defects in the mask design layout, thereby enhancing the accuracy of the layout defect location. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a schematic flowchart illustrating a method for determining the location of a layout defect according to an embodiment of this application;

[0022] Figure 2 This is a schematic diagram illustrating the matching of a candidate layout and a target graphic according to an embodiment of this application;

[0023] Figure 3 This is a flowchart illustrating another method for determining the location of a layout defect provided in an embodiment of this application;

[0024] Figure 4 This is a schematic diagram of a device for determining the location of a layout defect according to an embodiment of this application;

[0025] Figure 5 This is a schematic diagram of a device for determining the location of a layout defect, provided in an embodiment of this application. Detailed Implementation

[0026] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0027] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0028] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations.

[0029] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0030] The purpose of this application is to provide a method, apparatus, device, storage medium, and program product for determining the location of layout defects. The method for determining the location of layout defects provided in this application first obtains candidate layouts in the mask design layout that match the graphic features of a target graphic in a graphics library. The target graphic is any one of the simulated defect graphics in the graphics library. Preliminary screening of each sub-layout in the mask design layout is performed based on the graphic features. Then, a second random sampling coordinate is generated in the candidate layout, and the second random sampling coordinate is matched with the first random sampling coordinate in the target graphic. The location of the candidate layout is determined based on the graphic similarity to determine whether it is the location of a layout defect in the mask design layout. Thus, by performing fuzzy matching using random sampling coordinates, the location of the layout defect in the mask design layout can be determined as long as the graphic similarity is greater than a preset similarity threshold. Even if the graphic in the mask design layout has missing parts or numerous minor modifications, it is still possible to accurately determine whether the candidate layout matches the target graphic. This improves the accuracy of graphic matching and thus accurately determines the location of layout defects in the mask design layout, thereby improving the accuracy of the layout defect location.

[0031] The following describes specific embodiments of the method, apparatus, device, storage medium, and program product for determining the location of layout defects provided in this application. The method for determining the location of layout defects will be described first.

[0032] Figure 1 A flowchart illustrating a method for determining the location of a layout defect is provided. This method can be applied to a server and may include steps S101 to S104.

[0033] S101, determine candidate layouts from multiple sub-layouts of the mask design layout that match the graphic features of the target graphic in the graphic library. The graphic library includes at least one simulated bad pixel graphic. The simulated bad pixel graphic is a local defect point graphic that does not meet the process window requirements and is determined after photolithography simulation of the mask design layout. The simulated bad pixel graphic includes multiple first random sampling coordinates obtained by random sampling according to a preset sampling strategy. The target graphic is any one of the simulated bad pixel graphics.

[0034] In this embodiment, the mask design layout can be divided into multiple sub-layouts according to a pre-defined partitioning strategy.

[0035] The graphics library includes multiple simulated defective pixel patterns that do not meet the process window requirements, determined after photolithography simulation of the mask design layout. The target graphic is one of the simulated defective pixel patterns.

[0036] The preset sampling strategy is used to characterize the pre-defined strategy for randomly sampling feature points of a graphic.

[0037] Graphical features are information that characterizes the graphic features of a simulated bad pixel. For example, graphic features could be the size information of a simulated bad pixel.

[0038] As an example, the server pre-creates a graphics library corresponding to the mask design layout. The library contains various types of simulated defective pixel patterns. These patterns are localized defects that do not meet the process window requirements, identified by lithography simulation software after simulating the mask design layout. Each simulated defective pixel pattern also records detailed coordinates of multiple first random samples obtained according to a preset sampling strategy (e.g., uniform or Gaussian distribution).

[0039] Then, the complete mask design layout is divided into multiple sub-layouts, and each sub-layout is matched against each simulated bad pixel graphic in the graphics library. If a sub-layout is found to have the same size as a simulated bad pixel graphic in the graphics library, then the sub-layout is determined as the candidate layout, and the simulated bad pixel graphic is determined as the target graphic.

[0040] S102, random sampling is performed in the candidate layout according to the preset sampling strategy to obtain multiple second random sampling coordinates.

[0041] In this embodiment, the second random sampling coordinates are used to characterize the coordinates of feature points sampled in the candidate layout according to a preset sampling strategy.

[0042] As an example, the server performs random sampling on the candidate map according to the same preset sampling strategy as the target map, and obtains multiple second random sampling coordinates.

[0043] S103, Match each second random sampling coordinate with each first random sampling coordinate in the target graphic to obtain the graphic similarity between the candidate layout and the target graphic.

[0044] In this embodiment, graphic similarity is used to characterize the degree of similarity between the candidate map and the target map.

[0045] As an example, the server matches the second random sample coordinates of the candidate layout with the first random sample coordinates of the target graphic, and then calculates the graphic similarity between the candidate layout and the target graphic based on the matching results.

[0046] Specifically, graphic similarity can be confirmed based on multiple dimensions such as the degree of overlap of sampling coordinates and differences in spatial distribution.

[0047] S104, if the graphic similarity is greater than the preset similarity threshold, determine the position of the candidate layout as the layout defect position of the mask design layout, and the layout defect position is the location of the defect point in the mask design layout that does not meet the process window requirements.

[0048] In this embodiment, the location of the layout defect is the location of the local defect point in the mask design layout of the target graphic representation in the graphics library.

[0049] As an example, the server can pre-set a reasonable similarity threshold based on experience or experimental data.

[0050] Then, the graphic similarity between the candidate layout and the target graphic is compared with a preset similarity threshold. If the graphic similarity between the candidate layout and the target graphic is greater than the preset similarity threshold, the candidate layout is considered to match the target graphic, meaning that the position of the candidate layout is the location of the defect point in the mask design layout represented by the target graphic that does not meet the process window requirements.

[0051] The method for determining the location of layout defects provided in this application first obtains candidate layouts in the mask design layout that match the graphic features of a target graphic in a graphic library. The target graphic is any one of the simulated defective graphics in the graphic library. Preliminary screening of each sub-layout in the mask design layout is performed using graphic features. Then, a second random sampling coordinate is generated in the candidate layout, and this second random sampling coordinate is matched with the first random sampling coordinate in the target graphic. The location of the candidate layout is determined based on the graphic similarity to determine whether it is the location of a layout defect in the mask design layout. Thus, by performing fuzzy matching using random sampling coordinates, the location of a layout defect in the mask design layout can be determined as long as the graphic similarity is greater than a preset similarity threshold. Even if the graphic in the mask design layout is missing or has undergone numerous minor modifications, it is still possible to accurately determine whether the candidate layout matches the target graphic. This improves the accuracy of graphic matching and thus accurately determines the location of layout defects in the mask design layout, thereby enhancing the accuracy of the layout defect location.

[0052] As an optional embodiment, S101 may specifically include:

[0053] Obtain the mask design layout;

[0054] The mask design layout is divided into multiple sub-layouts according to the design rules and the corresponding partitioning strategy.

[0055] Each sub-pattern is matched with each simulated bad pixel image in the graphics library to obtain a target sub-pattern that matches the graphic features of the target image in the graphics library.

[0056] Extend each edge of the target sub-landmark along the corresponding extension direction to obtain the candidate landmark.

[0057] In this embodiment, Design Rule Check (DRC) is used to check whether the layout design violates predetermined design rules in order to prevent potential open circuits, short circuits or adverse effects.

[0058] As an example, the server obtains the mask design layout through electronic design automation (EDA) tools, and then uses DRC tools to perform a comprehensive check on the mask design layout to ensure that all design elements comply with the established design rules.

[0059] Based on the characteristics of design rule checks, a suitable partitioning strategy is formulated. Using the partitioning function in EDA tools or specialized layout partitioning software, the mask design layout is divided into multiple sub-layouts according to the formulated partitioning strategy.

[0060] Each sub-pattern is matched with simulated defective pixel images in the graphics library. Based on the matching results, the target sub-pattern that matches the graphic features of the target image in the graphics library is identified.

[0061] Finally, based on design rules and process requirements, the target length for expansion is determined, and each edge of the target sub-layout is expanded by the target length in the corresponding expansion direction to obtain the candidate layout. Specifically, the target length can be 1µm, and the target sub-layout can be rectangular. The left edge of the target sub-layout is expanded 1µm to the left, the right edge is expanded 1µm to the right, the top edge is expanded 1µm upwards, and the bottom edge is expanded 1µm downwards.

[0062] In this embodiment, the mask design layout is divided into multiple sub-layouts according to the design rules and the corresponding partitioning strategy. Each sub-layout is then matched with simulated defective pixel images in the image library to determine candidate layouts within the sub-layouts. Thus, this embodiment performs preliminary screening of the sub-layouts of the mask design layout through image feature matching, thereby accurately obtaining candidate layouts. Subsequent testing only needs to be performed on the candidate layouts, eliminating the need to test each sub-layout separately, thereby improving the efficiency of determining the location of defects in the layout.

[0063] As an optional embodiment, S103 may specifically include:

[0064] Match each second random sampling coordinate with each first random sampling coordinate in the target graphic to obtain the number of matching coordinates in the candidate layout that correspond to the target graphic;

[0065] Based on the number of matching coordinates, determine the proportion of matching coordinates to the number of coordinates in the second random sampling coordinates in the candidate layout;

[0066] The proportion of coordinates is used to determine the graphic similarity between the candidate map and the target map.

[0067] In this embodiment, matching coordinates are used to represent coordinates that are located at the same position.

[0068] As an example, for each second random sampling coordinate in the candidate layout, the server searches for whether there is a first random sampling coordinate at the same position in the target layout. If there is a first random sampling coordinate at the same position in the target layout, then the two coordinates are determined to be a match.

[0069] Then, the number of successfully matched second random sampling coordinates in the candidate layout is counted, and this number is divided by the total number of second random sampling coordinates in the candidate layout to obtain the graphic similarity between the candidate layout and the target graphic.

[0070] As another example, such as Figure 2 The diagram illustrates the matching of a candidate layout with a target graphic. The left side (a) represents the target graphic, and the right side (b) represents the candidate layout. The candidate layout lacks a shaded area compared to the target graphic. Although the first and second random sampling coordinates are the same, the corresponding first random sampling coordinate in the target graphic is located in the shaded area, while the corresponding second random sampling coordinate in the candidate layout is located in the blank area. Therefore, they are still in different positions, meaning the first and second random sampling coordinates do not match. However, if all other first and second random sampling coordinates match, the graphic similarity between the candidate layout and the target graphic will still be greater than a preset similarity threshold, indicating that the candidate layout and the target graphic still match.

[0071] This embodiment performs a fuzzy comparison between candidate layouts and target graphics. Based on the number of matching coordinates between the candidate layout and the target graphic, the graphic similarity between the two is determined. If the graphic similarity exceeds a preset similarity threshold, the candidate layout is considered a match for the target graphic. This improves the accuracy of graphic matching, thereby precisely determining the location of layout defects in the mask design layout and enhancing the accuracy of defect location.

[0072] As an optional embodiment, such as Figure 3 As shown, prior to S101, the method for determining the location of defects in this layout may further include the following S301 to S303:

[0073] S301 performs photolithography simulation and photolithography rule check on the mask design layout to obtain multiple simulated defective pixel patterns;

[0074] S302, perform fuzzy classification on each simulated bad pixel image to obtain the bad pixel category corresponding to each simulated bad pixel image;

[0075] S303 retrieves a simulated bad pixel image from each bad pixel category to form a graphics library corresponding to the mask design layout.

[0076] In this embodiment, lithography simulation is used to characterize the lithography process of the mask design layout through computer software, in order to predict and evaluate the defects that may exist during the lithography process.

[0077] Layout Versus Rule Check (LRC) is used to characterize a comprehensive check of the chip design before the implementation of the lithography process, to identify and resolve potential manufacturability issues in the chip design in advance, thereby reducing the risk of integrated circuit manufacturing failure.

[0078] Fuzzy classification is used to characterize methods for classifying elements that cannot be precisely described. Specifically, it can include fuzzy C-means clustering or fuzzy support vector machine methods.

[0079] As an example, the server imports the mask design layout into the lithography simulation software and sets the corresponding simulation parameters, including the lithography machine model, exposure wavelength, and photoresist type. The simulation program is then run to simulate the lithography process, including exposure and development steps, thereby generating the simulated pattern. A lithography rule checking tool is then used to inspect the simulated pattern, identifying any irregularities, i.e., potential defects, resulting in multiple simulated defective pixel images.

[0080] Then, the simulated defective pixel images are classified using a corresponding fuzzy classification algorithm to obtain the defective pixel category corresponding to each simulated defective pixel image. Finally, for each defective pixel category, a representative simulated defective pixel image is selected from all simulated defective pixel images in that category. Based on the bounding box of the representative simulated defective pixel image, the design layout of the corresponding defective pixel pattern is cut out, thus forming the graphic library corresponding to the mask design layout.

[0081] This embodiment uses photolithography simulation and rule checks on the mask design layout to predict and evaluate potential defects during photolithography, obtaining corresponding simulated defect patterns. This forms a pattern library corresponding to the mask design layout. This helps to detect and correct defects in the mask design layout in advance based on the pattern library, thereby improving the photolithography quality of the mask design layout.

[0082] As an optional embodiment, S301 may specifically include:

[0083] The mask design layout is simulated using photolithography to obtain the simulated mask design layout.

[0084] The photolithography rules are checked on the mask design layout after photolithography simulation to obtain multiple defective pixel results and the size of each defective pixel result.

[0085] The size of each bad pixel result is expanded according to the preset expansion size to obtain the simulated bad pixel pattern corresponding to each bad pixel result.

[0086] In this embodiment, the server imports the mask design layout into the photolithography simulation software, sets the photolithography parameters (such as light source wavelength, exposure time, and focal length), performs simulation calculations, and obtains the mask design layout after photolithography simulation. This layout reflects the influence of the photolithography process on the mask pattern.

[0087] Then, the mask design layout after photolithography simulation is imported into the photolithography rule inspection tool, inspection rules (such as minimum linewidth, minimum spacing, etc.) are set, and rule inspection is performed to obtain the results of multiple bad pixels and their dimensions corresponding to the mask design layout after photolithography simulation.

[0088] Finally, based on a preset expansion size (e.g., 10% of the defect size), the size of each defect is expanded to obtain simulated defect patterns corresponding to each defect result. These patterns can be used for subsequent photolithography process optimization or mask repair.

[0089] In this embodiment, multiple defective pixel results are obtained by performing photolithography simulation and photolithography rule checks on the mask design layout. The dimensions of each defective pixel result are then expanded to obtain a simulated defective pixel pattern. Thus, this embodiment can accurately obtain the simulated defective pixel pattern corresponding to the mask design layout, providing an important basis for subsequent process optimization and mask repair.

[0090] As an optional embodiment, S302 may specifically include:

[0091] Move the graphic feature points of each simulated bad pixel image to a preset position to obtain the moved simulated bad pixel images;

[0092] Each edge of the moved simulated defective pixel is extended by the target length in the corresponding extension direction to obtain the extended simulated defective pixel.

[0093] Random sampling is performed in each of the expanded simulated defective pixel images according to a preset sampling strategy to obtain multiple first random sampling coordinates;

[0094] Based on the first random sampling coordinates in each simulated bad pixel image, fuzzy classification is performed on each simulated bad pixel image to obtain the bad pixel category corresponding to each simulated bad pixel image.

[0095] In this embodiment, graphic feature points are used to characterize locations with significant features in the simulated bad pixel image. For example, a graphic feature point can be the center point or a vertex of the simulated bad pixel image.

[0096] As an example, the server moves the feature points of each simulated bad pixel image to a preset position according to a preset displacement vector, thereby obtaining the moved simulated bad pixel images.

[0097] Then, according to the preset expansion direction and length, each edge of the simulated defective pixel pattern is expanded to obtain the expanded simulated defective pixel patterns. Specifically, the target length can be 1µm, and the simulated defective pixel pattern can be rectangular. The left edge of the simulated defective pixel pattern is expanded 1µm to the left, the right edge is expanded 1µm to the right, the top edge is expanded 1µm upwards, and the bottom edge is expanded 1µm downwards.

[0098] Then, a preset sampling strategy is designed based on factors such as the complexity and area of ​​the image. According to the preset sampling strategy, multiple sampling points are randomly selected inside the simulated bad pixel image, and their coordinates are recorded to obtain the first random sampling coordinates.

[0099] Then, the server matches the first random sampled coordinates between each simulated defective pixel image, and calculates the image similarity between each simulated defective pixel image based on the matching results. Specifically, the image similarity can be confirmed based on multiple dimensions such as the overlap of sampled coordinates and differences in spatial distribution.

[0100] Finally, if the similarity between simulated defective pixel images exceeds a preset similarity threshold, the corresponding simulated defective pixel images can be confirmed to belong to the same defective pixel category. This achieves fuzzy classification of each simulated defective pixel image, obtaining the defective pixel category corresponding to each simulated defective pixel image.

[0101] In this embodiment, fuzzy classification is performed on each simulated defective pixel pattern to obtain the defective pixel category corresponding to each pattern. Finally, only one simulated defective pixel pattern needs to be obtained from each category to construct a pattern library corresponding to the mask design layout. This reduces the number of simulated defective pixel patterns in the pattern library, eliminating the need to match each simulated defective pixel pattern when determining the location of layout defects, thereby improving the efficiency of defect location determination.

[0102] As an optional embodiment, the graphic feature point is the center of the bad point in the simulated bad point graphic, and the preset position is the midpoint of the edge closest to the simulated bad point graphic.

[0103] In this embodiment, the defective pixels identified by the photolithography rule check may have slight positional deviations. Therefore, it is necessary to first move the center of the defective pixel in the simulated defective pixel pattern to the midpoint of the edge closest to the simulated defective pixel pattern, thereby aligning the simulated defective pixel pattern identified by the photolithography rule check with the corresponding sub-pattern obtained from the mask design layout.

[0104] In this embodiment, by moving the center of the simulated defective pixel pattern to the midpoint of the edge closest to the simulated defective pixel pattern, the simulated defective pixel pattern and its corresponding sub-layout can be aligned. This facilitates subsequent matching of the aligned simulated defective pixel patterns with their corresponding sub-layouts based on graphic features, enabling preliminary screening of simulated defective pixel patterns using graphic features and improving the efficiency of determining the location of layout defects.

[0105] As an optional embodiment, based on the first random sampled coordinates in each simulated bad pixel image, fuzzy classification is performed on each simulated bad pixel image to obtain the bad pixel category corresponding to each simulated bad pixel image. Specifically, this may include:

[0106] The first random sampling coordinate in the first simulated bad pixel image is matched with the first random sampling coordinate in the second simulated bad pixel image to obtain the image similarity between the first simulated bad pixel image and the second simulated bad pixel image. The first simulated bad pixel image and the second simulated bad pixel image are any one of the simulated bad pixel images.

[0107] If the similarity between the first simulated bad pixel image and the second simulated bad pixel image is greater than a preset similarity threshold, then the first simulated bad pixel image and the second simulated bad pixel image belong to the same bad pixel category.

[0108] If the similarity between the first simulated bad pixel image and the second simulated bad pixel image is less than or equal to a preset similarity threshold, it is determined that the first simulated bad pixel image and the second simulated bad pixel image do not belong to the same bad pixel category.

[0109] In this embodiment, for each first random sampling coordinate in the first simulated bad pixel image, the server searches for whether there is a first random sampling coordinate at the same position in the second simulated bad pixel image. If there is a first random sampling coordinate at the same position in the second simulated bad pixel image, then the two coordinates are determined to be a match.

[0110] Then, the number of first random sample coordinates that successfully match in the first simulated bad pixel image is counted, and this number is divided by the total number of first random sample coordinates in the first simulated bad pixel image to obtain the image similarity between the first simulated bad pixel image and the second simulated bad pixel image.

[0111] If the similarity between the first simulated bad pixel image and the second simulated bad pixel image is greater than a preset similarity threshold, then the first simulated bad pixel image and the second simulated bad pixel image belong to the same bad pixel category; if the similarity between the first simulated bad pixel image and the second simulated bad pixel image is less than or equal to the preset similarity threshold, then the first simulated bad pixel image and the second simulated bad pixel image do not belong to the same bad pixel category.

[0112] In this embodiment, fuzzy classification is performed on each simulated defective pixel pattern to obtain the defective pixel category corresponding to each pattern. Finally, only one simulated defective pixel pattern needs to be obtained from each category to construct a pattern library corresponding to the mask design layout. This reduces the number of simulated defective pixel patterns in the pattern library, eliminating the need to match each simulated defective pixel pattern when determining the location of layout defects, thereby improving the efficiency of defect location determination.

[0113] A method for determining the location of layout defects. Accordingly, this application also provides specific embodiments of a device for determining the location of layout defects.

[0114] like Figure 4 As shown, the layout defect location determination device 400 provided in this application embodiment includes a layout determination module 410, a coordinate sampling module 420, a similarity determination module 430, and a location determination module 440.

[0115] The layout determination module 410 is used to determine candidate layouts that match the graphic features of a target graphic in a graphic library from multiple sub-layouts of the mask design layout. The graphic library includes at least one simulated defect graphic, which is a local defect point graphic that does not meet the process window requirements and is determined after photolithography simulation of the mask design layout. The simulated defect graphic includes multiple first random sampling coordinates obtained by random sampling according to a preset sampling strategy. The target graphic is any one of the simulated defect graphics.

[0116] The coordinate sampling module 420 is used to randomly sample in the candidate layout according to a preset sampling strategy to obtain multiple second random sampling coordinates.

[0117] The similarity determination module 430 is used to match each second random sampling coordinate with each first random sampling coordinate in the target graphic to obtain the graphic similarity between the candidate layout and the target graphic.

[0118] The location determination module 440 is used to determine the location of the candidate layout as the layout defect location of the mask design layout when the graphic similarity is greater than a preset similarity threshold. The layout defect location is the location of the defect point in the mask design layout that does not meet the process window requirements.

[0119] The layout defect location determination device provided in this application first acquires candidate layouts in the mask design layout that match the graphic features of a target graphic in a graphic library. The target graphic is any one of the simulated defect graphics in the graphic library. Preliminary screening of each sub-layout in the mask design layout is performed using graphic features. Then, a second random sampling coordinate is generated in the candidate layout, and this second random sampling coordinate is matched with the first random sampling coordinate in the target graphic. Based on the graphic similarity, it is determined whether the location of the candidate layout is a layout defect location in the mask design layout. Thus, by performing fuzzy matching using random sampling coordinates, the layout defect location in the mask design layout can be determined as long as the graphic similarity is greater than a preset similarity threshold. Even if the graphic in the mask design layout is missing or has numerous minor modifications, it can still accurately determine whether the candidate layout matches the target graphic. This improves the accuracy of graphic matching and thus accurately determines the layout defect location in the mask design layout, thereby enhancing the accuracy of the layout defect location.

[0120] As an optional embodiment, the layout determination module 410 may specifically include the following units:

[0121] The layout acquisition unit is used to acquire the mask design layout;

[0122] The layout partitioning unit is used to divide the mask design layout into multiple sub-layouts according to the design rules and the corresponding partitioning strategy.

[0123] The layout matching unit is used to match each sub-layout with each simulated bad pixel graphic in the graphics library to obtain a target sub-layout that matches the graphic features of the target graphic in the graphics library.

[0124] The map expansion unit is used to expand each edge of the target sub-map to the target length in the corresponding expansion direction to obtain the candidate map.

[0125] As an optional embodiment, the similarity determination module 430 may specifically include the following units:

[0126] The coordinate matching unit is used to match each second random sample coordinate with each first random sample coordinate in the target graphic to obtain the number of matching coordinates in the candidate layout corresponding to the target graphic;

[0127] The proportion determination unit is used to determine the proportion of the number of matching coordinates in the candidate layout relative to the number of second random sampling coordinates, based on the number of matching coordinates.

[0128] The similarity determination unit is used to determine the proportion of coordinates as the graphic similarity between the candidate map and the target map.

[0129] As an optional embodiment, before determining a candidate layout that matches the graphic features of a target graphic in a graphics library from multiple sub-layouts of the mask design layout, the apparatus for determining the location of layout defects may further include the following modules:

[0130] The layout simulation module is used to perform photolithography simulation and photolithography rule checking on the mask design layout to obtain multiple simulated defective pixel patterns.

[0131] The image classification module is used to perform fuzzy classification on each simulated bad pixel image to obtain the bad pixel category corresponding to each simulated bad pixel image.

[0132] The graphics library construction module is used to obtain a simulated bad pixel image from each bad pixel category to form a graphics library corresponding to the mask design layout.

[0133] As an optional embodiment, the layout simulation module specifically includes the following units:

[0134] The photolithography simulation unit is used to perform photolithography simulation on the mask design layout to obtain the mask design layout after photolithography simulation.

[0135] The layout inspection unit is used to perform lithography rule inspection on the mask design layout after lithography simulation, and obtain multiple bad point results and the size of each bad point result corresponding to the mask design layout after lithography simulation.

[0136] The size expansion unit is used to expand the size of each bad pixel result according to the preset expansion size to obtain the simulated bad pixel pattern corresponding to each bad pixel result.

[0137] As an optional embodiment, the image classification module specifically includes the following units:

[0138] The graphic movement unit is used to move the graphic feature points of each simulated bad pixel graphic to a preset position to obtain the moved simulated bad pixel graphic.

[0139] The graphic expansion unit is used to expand each edge of each simulated defective pixel graphic after the movement by the target length in the corresponding expansion direction to obtain the expanded simulated defective pixel graphics.

[0140] The graphic sampling unit is used to randomly sample from each of the expanded simulated bad pixel graphics according to a preset sampling strategy to obtain multiple first random sampling coordinates.

[0141] The image classification unit is used to perform fuzzy classification on each simulated bad pixel image based on the first random sampled coordinates in each simulated bad pixel image, so as to obtain the bad pixel category corresponding to each simulated bad pixel image.

[0142] As an optional embodiment, the graphic classification unit specifically includes the following sub-units:

[0143] The similarity calculation subunit is used to match the first random sampling coordinates in the first simulated bad pixel image with the first random sampling coordinates in the second simulated bad pixel image to obtain the image similarity between the first simulated bad pixel image and the second simulated bad pixel image. The first simulated bad pixel image and the second simulated bad pixel image are any one of the simulated bad pixel images.

[0144] The type determination subunit is used to determine that the first simulated bad pixel image and the second simulated bad pixel image belong to the same bad pixel category when the similarity between the first simulated bad pixel image and the second simulated bad pixel image is greater than a preset similarity threshold.

[0145] The type determination subunit is also used to determine that the first simulated bad pixel image and the second simulated bad pixel image do not belong to the same bad pixel category when the similarity between the first simulated bad pixel image and the second simulated bad pixel image is less than or equal to a preset similarity threshold.

[0146] A method for determining the location of layout defects. Accordingly, this application also provides specific embodiments of a device for determining the location of layout defects.

[0147] Figure 5 A schematic diagram of the hardware structure of the device for determining the location of layout defects provided in an embodiment of this application is shown.

[0148] The device for determining the location of a layout defect may include a processor 501 and a memory 502 storing computer program instructions.

[0149] Specifically, the processor 501 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0150] Memory 502 may include mass storage for data or instructions. For example, and not limitingly, memory 502 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 502 may include removable or non-removable (or fixed) media. Where appropriate, memory 502 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, memory 502 is non-volatile solid-state memory.

[0151] The processor 501 reads and executes computer program instructions stored in the memory 502 to implement any of the layout defect location determination methods in the above embodiments.

[0152] In one example, the device for determining the location of a layout defect may further include a communication interface 503 and a bus 510. Wherein, as Figure 5 As shown, the processor 501, memory 502, and communication interface 503 are connected through bus 510 and complete communication with each other.

[0153] The communication interface 503 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0154] Bus 510 includes hardware, software, or both, that couples components of a defined device at a layout defect location together. For example, and not as a limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 510 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0155] Furthermore, in conjunction with the method for determining the location of layout defects in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the methods for determining the location of layout defects in the above embodiments.

[0156] In addition, in conjunction with the method for determining the location of layout defects in the above embodiments, this application embodiment can provide a computer program product for implementation. When the instructions in the computer program product are executed by the processor of an electronic device, the electronic device executes the method for determining the location of layout defects as provided in any aspect of the above embodiments of this application.

[0157] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0158] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0159] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0160] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0161] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for determining the location of a layout defect, characterized in that, include: Candidate layouts that match the graphic features of a target graphic in a graphic library are determined from multiple sub-layouts of the mask design layout. The graphic library includes at least one simulated defect graphic, which is a local defect point graphic that does not meet the process window requirements and is determined after photolithography simulation of the mask design layout. The simulated defect graphic includes multiple first random sampling coordinates obtained by random sampling according to a preset sampling strategy. The target graphic is any one of the simulated defect graphics. Random sampling is performed on the candidate layout according to the preset sampling strategy to obtain multiple second random sampling coordinates; The second random sampling coordinates are matched with the first random sampling coordinates in the target graphic to obtain the graphic similarity between the candidate layout and the target graphic. If the similarity of the graphic is greater than a preset similarity threshold, the position of the candidate layout is determined as the layout defect position of the mask design layout, and the layout defect position is the location of the defect point in the mask design layout that does not meet the process window requirements; The step of matching each of the second random sampling coordinates with each of the first random sampling coordinates in the target graphic to obtain the graphic similarity between the candidate layout and the target graphic includes: Each of the second random sampling coordinates is matched with each of the first random sampling coordinates in the target graphic to obtain the number of matching coordinates in the candidate layout that correspond to the target graphic; Based on the number of matching coordinates, determine the proportion of the number of matching coordinates in the candidate layout relative to the number of coordinates in the second random sampling coordinates; The proportion of the number of coordinates is determined as the graphic similarity between the candidate map and the target map.

2. The method according to claim 1, characterized in that, The process of determining candidate layouts that match the graphic features of a target graphic in a graphics library from multiple sub-layouts of a mask design layout includes: Obtain the mask design layout; The mask design layout is divided into multiple sub-layouts according to the design rules and the corresponding partitioning strategy. Each of the sub-layouts is matched with each of the simulated bad pixel graphics in the graphics library to obtain a target sub-layout that matches the graphic features of the target graphic in the graphics library. The candidate layout is obtained by extending each edge of the target sub-layout along the corresponding extension direction to achieve the target length.

3. The method according to any one of claims 1-2, characterized in that, Before determining candidate layouts that match graphic features of a target graphic in a graphics library from multiple sub-layouts of the mask design layout, the method further includes: Photolithography simulation and photolithography rule checks were performed on the mask design layout to obtain multiple simulated defect patterns; Fuzzy classification is performed on each of the simulated defective pixel images to obtain the defective pixel category corresponding to each simulated defective pixel image; One simulated defective pixel image is obtained from each of the aforementioned defective pixel categories to form the image library corresponding to the mask design layout.

4. The method according to claim 3, characterized in that, The process of performing photolithography simulation and photolithography rule checks on the mask design layout yields multiple simulated defect patterns, including: The mask design layout is subjected to photolithography simulation to obtain the mask design layout after photolithography simulation; The photolithography simulation mask design layout is subjected to photolithography rule checks to obtain multiple defective pixel results and the size of each defective pixel result corresponding to the photolithography simulation mask design layout; The dimensions of each of the aforementioned bad pixel results are expanded according to a preset expansion size to obtain the simulated bad pixel pattern corresponding to each of the aforementioned bad pixel results.

5. The method according to claim 3, characterized in that, The step of performing fuzzy classification on each of the simulated bad pixel images to obtain the bad pixel category corresponding to each simulated bad pixel image includes: The graphic feature points of each of the simulated bad pixel images are moved to a preset position to obtain the moved simulated bad pixel images. Each edge of the moved simulated bad pixel pattern is extended by the target length in the corresponding extension direction to obtain the extended simulated bad pixel pattern. Random sampling is performed in each of the expanded simulated defective pixel graphics according to the preset sampling strategy to obtain multiple first random sampling coordinates; Based on the first random sampling coordinates in each of the simulated bad pixel images, fuzzy classification is performed on each of the simulated bad pixel images to obtain the bad pixel category corresponding to each of the simulated bad pixel images.

6. The method according to claim 5, characterized in that, The graphic feature point is the center of the bad point in the simulated bad point graphic, and the preset position is the midpoint of the edge closest to the simulated bad point graphic.

7. The method according to claim 5, characterized in that, The step of performing fuzzy classification on each of the simulated bad pixel images based on the first random sampled coordinates in each of the simulated bad pixel images to obtain the bad pixel category corresponding to each simulated bad pixel image includes: The first random sampling coordinate in the first simulated bad pixel image is matched with the first random sampling coordinate in the second simulated bad pixel image to obtain the image similarity between the first simulated bad pixel image and the second simulated bad pixel image. The first simulated bad pixel image and the second simulated bad pixel image are any one of the simulated bad pixel images. If the similarity between the first simulated bad pixel image and the second simulated bad pixel image is greater than a preset similarity threshold, it is determined that the first simulated bad pixel image and the second simulated bad pixel image belong to the same bad pixel category. If the similarity between the first simulated bad pixel image and the second simulated bad pixel image is less than or equal to a preset similarity threshold, it is determined that the first simulated bad pixel image and the second simulated bad pixel image do not belong to the same bad pixel category.

8. A device for determining the location of a layout defect, characterized in that, include: The layout determination module is used to determine candidate layouts that match the graphic features of a target graphic in a graphic library from multiple sub-layouts of the mask design layout. The graphic library includes at least one simulated defect graphic, which is a local defect point graphic that does not meet the process window requirements and is determined after photolithography simulation of the mask design layout. The simulated defect graphic includes multiple first random sampling coordinates obtained by random sampling according to a preset sampling strategy. The target graphic is any one of the simulated defect graphics. The coordinate sampling module is used to randomly sample the candidate layout according to the preset sampling strategy to obtain multiple second random sampled coordinates. The similarity determination module is used to match each of the second random sampling coordinates with each of the first random sampling coordinates in the target graphic to obtain the graphic similarity between the candidate layout and the target graphic; It is also used to match each of the second random sampling coordinates with each of the first random sampling coordinates in the target graphic to obtain the number of matching coordinates in the candidate layout corresponding to the target graphic; based on the number of matching coordinates, determine the proportion of the number of matching coordinates in the candidate layout to the number of second random sampling coordinates; and determine the proportion of the number of coordinates as the graphic similarity between the candidate layout and the target graphic. The location determination module is used to determine the location of the candidate layout as the layout defect location of the mask design layout when the graphic similarity is greater than a preset similarity threshold. The layout defect location is the location of the defect point in the mask design layout that does not meet the process window requirements.

9. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the method for determining the location of layout defects as described in any one of claims 1-7.

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