Photomask measurement point determination method and device, computer equipment and storage medium
By performing coarse and fine sampling on the photomask, the distribution of measurement points was determined, which solved the problem of insufficient coverage of measurement points on the photomask, achieved uniform measurement in high-density areas, and improved the effect of process analysis.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-29
- Publication Date
- 2026-04-07
AI Technical Summary
In existing technologies, the selection of photomask measurement points is insufficient, making it impossible to effectively perceive the density of the pattern and affecting process analysis.
By obtaining the overall sample distribution map of the photomask, coarse sampling and fine sampling are performed to determine the measurement points in the edge and internal regions, generate a fine sampling measurement point distribution map, and conduct process analysis.
Ensure sufficient measurement points in high-density graphic areas to improve the sampling uniformity of measurement points and enhance the coverage and accuracy of process analysis.
Smart Images
Figure CN121806370A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of computer technology, and more specifically, to a method, apparatus, computer device, and storage medium for determining photomask measurement points. Background Technology
[0002] The photomask is the "master" in integrated circuit manufacturing, and its pattern accuracy directly affects the replication quality of the pattern on the wafer. Among them, the critical dimension uniformity (CDU) of the photomask refers to the consistency of the actual manufacturing size of the pattern at different locations on the photomask relative to its design target size.
[0003] In related technologies, the effective area of the photomask is usually divided into grids using a grid division method. A measurement point is randomly selected in each grid area, and then the pattern at that measurement point on the photomask is measured by CDU to monitor the dimensional fluctuations during the photomask manufacturing process, identify weak links or potential problems in the process, and thus comprehensively evaluate the process quality of the photomask.
[0004] However, the above method cannot detect the density of patterns on the photomask, and the measurement points in high-density pattern areas are not adequately covered, affecting process analysis. Summary of the Invention
[0005] In view of this, embodiments of this application provide a method, apparatus, computer equipment, and storage medium for determining photomask measurement points, in order to solve the problem that the selection and coverage of existing photomask measurement points are insufficient and affect process analysis.
[0006] In a first aspect, embodiments of this application provide a method for determining photomask measurement points, including: Based on the pattern distribution information on the photomask, a total sample distribution map of the photomask is obtained, which includes multiple sample points of the photomask. Coarse sampling is performed on the sample points in the total sample distribution map to obtain multiple coarse sampling points, and a coarse sampling measurement point distribution map is generated. Fine sampling is performed on the edge regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain fine edge sampling points; The coarse sampling points in the coarse sampling measurement point distribution map are subjected to fine sampling in the internal region to obtain the internal fine sampling points; Based on the edge fine sampling points and the internal fine sampling points, a fine sampling measurement point distribution map is generated to perform process analysis on the photomask based on the fine sampling measurement point distribution map.
[0007] In an optional implementation, the step of coarsely sampling the sample points in the total sample distribution map to obtain multiple coarse sampling points includes: The total sample distribution map is divided into multiple grids; The number of samples in each grid is obtained based on the number of sample points in each grid, the total number of sample points, and the preset coarse sampling number. Based on the number of samples in each grid, cluster analysis is performed on the sample points in each grid, and the cluster centers of each grid are obtained. The cluster centers of the multiple grids are determined as the multiple coarse sampling points.
[0008] In an optional implementation, the step of performing fine sampling of the edge regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain fine edge sampling points includes: The edge grid is determined from multiple grids in the coarse sampling measurement point distribution map; Fine sampling is performed on the coarse sampling points within the edge grid to obtain the edge fine sampling points.
[0009] In an optional implementation, the step of fine-sampling the coarse sampling points within the edge grid to obtain the fine-sampling edge points includes: Determine the first type of edge mesh and the second type of edge mesh from the edge mesh; The vertices of the first type of edge mesh are used as the first centroids, and sampling points that satisfy a preset distance condition with the first centroid are determined from the coarse sampling points in the first type of edge mesh as the first edge fine sampling points. The center point of the edge of the second type of edge grid is taken as the second centroid, and the sampling points that satisfy the preset distance condition with the second centroid are determined from the coarse sampling points in the second type of edge grid as the second edge fine sampling points; The first edge fine sampling point and the second edge fine sampling point are determined as the edge fine sampling point.
[0010] In an optional implementation, the step of performing fine sampling of the internal regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain internal fine sampling points includes: The internal grid is determined from multiple grids in the coarse sampling measurement point distribution map; The Poisson disk sampling algorithm is used to perform fine sampling on the coarse sampling points in the internal grid to obtain the internal fine sampling points.
[0011] In an optional implementation, after generating a fine sampling measurement point distribution map based on the edge fine sampling points and the internal fine sampling points, the method further includes: Based on the fine sampling measurement point distribution map, the photomask is measured to obtain the measurement results of each edge fine sampling point on the photomask for the preset measurement items, and the measurement results of each internal fine sampling point for the preset measurement items.
[0012] In an optional implementation, the method further includes: If the measurement result corresponding to any sampling point does not meet the preset conditions, then multiple other coarse sampling points in the area where the arbitrary sampling point is located, which meet the preset distance conditions with the arbitrary sampling point, are determined from the coarse sampling measurement point distribution map as multiple new measurement points. Obtain the measurement results of the new measurement points on the photomask for the preset measurement items; If the measurement results corresponding to the multiple new measurement points do not meet the preset conditions, then it is determined that there is a process defect in the area where any sampling point on the photomask is located. If the measurement result corresponding to any sampling point meets the preset condition, then it is determined that there are no process defects in the area where the arbitrary sampling point on the photomask is located.
[0013] Secondly, embodiments of this application also provide a device for determining photomask measurement points, comprising: The acquisition module is used to acquire the total sample distribution map of the photomask, which includes multiple sample points of the photomask. The sampling module is used to perform coarse sampling on the sample points in the total sample distribution map to obtain multiple coarse sampling points and generate a coarse sampling measurement point distribution map. The sampling module is also used to perform fine sampling of the edge regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain fine edge sampling points; The sampling module is also used to perform fine sampling of the internal region of the coarse sampling points in the coarse sampling measurement point distribution map to obtain internal fine sampling points; The generation module is used to generate a fine sampling measurement point distribution map based on the edge fine sampling points and the internal fine sampling points, so as to perform process analysis on the photomask based on the fine sampling measurement point distribution map.
[0014] Thirdly, embodiments of this application also provide a computer device, including: a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the method described in any of the first aspects.
[0015] Fourthly, embodiments of this application also provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the method described in any of the first aspects.
[0016] This application provides a method, apparatus, computer device, and storage medium for determining measurement points on a photomask. The method includes: obtaining a total sample distribution map of the photomask based on the pattern distribution information on the photomask; performing coarse sampling on the sample points in the total sample distribution map to obtain multiple coarse sampling points and generating a coarse sampling measurement point distribution map; performing fine sampling on the edge regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain edge fine sampling points; performing fine sampling on the internal regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain internal fine sampling points; and generating a fine sampling measurement point distribution map based on the edge fine sampling points and internal fine sampling points, so as to perform process analysis on the photomask based on the fine sampling measurement point distribution map. This scheme ultimately determines the measurement points used for photomask measurement through coarse sampling, edge region fine sampling, and internal region fine sampling, ensuring that there are sufficient measurement points in high-density pattern areas while enhancing the sampling uniformity of the measurement points. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 A flowchart illustrating the method for determining photomask measurement points provided in this application embodiment. Figure 1 ; Figure 2 A flowchart illustrating the method for determining photomask measurement points provided in this application embodiment. Figure 2 ; Figure 3 A total sample distribution diagram provided for embodiments of this application; Figure 4 A sampling distribution map of the first grid provided in the embodiments of this application; Figure 5 This is a distribution map of coarse sampling measurement points provided in the embodiments of this application; Figure 6 A flowchart illustrating the method for determining photomask measurement points provided in this application embodiment. Figure 3 ; Figure 7 This is a distribution map of coarse sampling measurement points provided in the embodiments of this application; Figure 8 The edge sampling and internal sampling result distribution diagrams provided for embodiments of this application; Figure 9 This is a distribution map of detailed sampling measurement points provided in the embodiments of this application; Figure 10A flowchart illustrating the method for determining photomask measurement points provided in this application embodiment. Figure 4 ; Figure 11 A schematic diagram of the device for determining photomask measurement points provided in an embodiment of this application; Figure 12 A schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of the embodiments of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.
[0020] Critical Dimension Uniformity (CDU) directly reflects the degree of spatial variation in the characteristics of a pattern on a photomask (such as linewidth, spacing, and aperture). A smaller CDU indicates more uniform critical dimensions in the photomask pattern and higher process stability. By measuring the characteristics of the pattern at different locations on the photomask (such as the center, edges, and corners), dimensional fluctuations during photomask manufacturing can be monitored in a timely manner. This helps identify weak points or potential problems in the process and allows for a comprehensive evaluation of the photomask's manufacturing quality, avoiding production delays and increased costs. Therefore, the selection of CDU measurement points is crucial for optimizing the process flow, evaluating photomask manufacturing quality, and improving production efficiency.
[0021] The current method of selecting measurement points using grid division cannot detect the density of patterns on the photomask, resulting in insufficient coverage of measurement points in high-density pattern areas and affecting process analysis. Therefore, this solution obtains a total sample distribution map based on the pattern distribution information, and then performs coarse sampling, fine sampling of edge areas, and fine sampling of internal areas to ultimately determine the measurement points used for photomask measurement. This ensures sufficient measurement points in high-density pattern areas while enhancing the sampling uniformity of measurement points.
[0022] Figure 1 A flowchart illustrating the method for determining photomask measurement points provided in this application embodiment. Figure 1 In this embodiment, the executing entity can be a computer device.
[0023] like Figure 1 As shown, the method may include: S101. Obtain the overall sample distribution map of the photomask based on the graphic distribution information on the photomask.
[0024] The graphic distribution information on the photomask is used to indicate the distribution of all graphics on the photomask.
[0025] Based on the pattern distribution information on the photomask, a total sample distribution map of the photomask is generated. The total sample distribution map includes all sample points of the photomask. Each sample point corresponds to one pattern on the photomask, and the distribution of multiple sample points in the total sample distribution map is consistent with the distribution of multiple patterns on the photomask.
[0026] S102. Perform coarse sampling on the sample points in the total sample distribution map to obtain multiple coarse sampling points and generate a coarse sampling measurement point distribution map.
[0027] The total sample distribution map is divided into multiple grids, and sampling is performed within these grids to coarsely sample the sample points in the total sample distribution map, resulting in multiple coarse sampling points. This coarse sampling measurement point distribution map is used to indicate the distribution of the multiple coarse sampling points.
[0028] S103. Perform fine sampling on the edge regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain the edge fine sampling points.
[0029] S104. Perform fine sampling of the internal region of the coarse sampling points in the coarse sampling measurement point distribution map to obtain the internal fine sampling points.
[0030] The edge region and the interior region are determined from the coarse sampling measurement point distribution map. Fine sampling is performed on the edge region to determine the fine sampling points from multiple coarse sampling points, and fine sampling is performed on the interior region to determine the fine sampling points from multiple coarse sampling points.
[0031] S105. Generate a fine sampling measurement point distribution map based on the edge fine sampling points and the internal fine sampling points, and perform process analysis on the photomask based on the fine sampling measurement point distribution map.
[0032] The edge fine sampling points and the internal fine sampling points are identified as fine sampling points, and a fine sampling measurement point distribution map is generated. The fine sampling measurement point distribution map is used to indicate the distribution of edge fine sampling points and the distribution of internal fine sampling points.
[0033] The process analysis of the photomask is performed based on the distribution map of fine sampling points to obtain the consistency of the actual manufacturing size of each edge fine sampling point and each internal fine sampling point with its design target size, thereby monitoring the dimensional fluctuations in the photomask manufacturing process and evaluating the manufacturing quality of the photomask.
[0034] Figure 2 A flowchart illustrating the method for determining photomask measurement points provided in this application embodiment. Figure 2 ,like Figure 2 As shown, in an optional embodiment, step S102 above, which involves coarsely sampling the sample points in the total sample distribution map to obtain multiple coarsely sampled points, may include: S201. Divide the total sample distribution map into multiple grids.
[0035] Divide the total sample distribution map into an N×N (e.g., 10×10) grid.
[0036] Figure 3 The total sample distribution diagram provided for the embodiments of this application, such as Figure 3 As shown, sample points are marked with blue dots, and one sample point corresponds to one pattern on the photomask. The total sample distribution map is divided into an N×N (e.g., 10×10) grid.
[0037] S202. Based on the number of sample points in each grid, the total number of multiple sample points, and the preset coarse sampling number, obtain the number of samples in each grid.
[0038] The preset coarse sampling number (k) is a pre-set coarse sampling number, such as 500. The total number of sample points in the total sample distribution map can be 5000. The purpose of coarse sampling is to extract 500 coarse sampling points from 5000 sample points.
[0039] The number of sample points (m) in each grid is counted, and the proportion of the number of sample points in each grid to the total number of sample points is calculated. This proportion is expressed as: the proportion of the number of sample points in each grid to the total number of sample points (p) = the number of sample points in each grid (m) / the total number of sample points (K).
[0040] The number of samples (ki) in each grid can be the product of the proportion of the number of sample points in each grid to the total number of sample points and the preset coarse sampling number, expressed as: the proportion of the number of sample points in each grid to the total number of sample points (p) × the preset coarse sampling number (k).
[0041] See Figure 3In the overall sample distribution map, the first grid (red bold grid) contains 49 sample points, which accounts for 49 / 5000=1% of the total number of sample points. Then, the number of samples in this grid is calculated as 1%×500=5, that is, the number of samples in the first grid is 5.
[0042] S203. Based on the number of samples in each grid, perform cluster analysis on the sample points in each grid and obtain the cluster center of each grid.
[0043] S204. Determine the cluster centers of multiple grids as multiple coarse sampling points.
[0044] Using a pre-defined clustering analysis algorithm (such as the k-means clustering analysis algorithm), the sample points in each grid are clustered according to the number of samples (ki) in each grid. The sample points in each grid are clustered into ki classes, and the center point of each class of sample points is determined as the cluster center of each grid. The center point of each class of sample points can be a measurement point in each class of sample points.
[0045] In this case, each grid has ki cluster centers, for example, 5. Taking the first grid in the overall sample distribution map as an example, the 49 measurement points in the first grid are divided into 5 classes, and the 5 cluster centers are used as coarse sampling points. Figure 4 The sampling distribution map of the first grid provided in the embodiments of this application is as follows: Figure 4 As shown, the red crosses represent the cluster centers of the first grid, and there are a total of 5.
[0046] By traversing all the grids in the overall sample distribution map, k coarse sampling points can be obtained. Figure 5 The distribution map of coarse sampling measurement points provided in the embodiments of this application is as follows: Figure 5 As shown, the red dots represent the cluster centers of multiple grids, which are also multiple coarse sampling points.
[0047] In this embodiment, an improved spatial hierarchical sampling method is adopted, which combines gridding rules with cluster analysis to perform coarse sampling. The number of samples in the grid is determined according to the ratio of the number of measurement points in the grid to the total number of measurement points. At the same time, the measurement points in the grid are selected by combining cluster analysis. This can effectively perform fast and uniform coarse sampling of the total sample points, minimize the number of samples, and retain the information of the total sample points. This can ensure the uniformity of key dimensions in key areas of the photomask (such as high pattern density areas and sensitive circuit areas).
[0048] Figure 6 A flowchart illustrating the method for determining photomask measurement points provided in this application embodiment. Figure 3 ,like Figure 6 As shown, in an optional embodiment, step S103 above, which involves fine sampling of the edge regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain fine edge sampling points, may include: S301. Determine the edge grid from multiple grids in the coarse sampling measurement point distribution map.
[0049] The edge grid refers to the grid on the four outer edges of the coarse sampling measurement point distribution map, see [link / reference]. Figure 5 The grid selected by the black bold line in the image.
[0050] S302. Fine-sample the coarse sampling points within the edge grid to obtain fine-sampled edge points.
[0051] Fine-sample the coarse sampling points within the edge grid to determine the fine-sampled edge points from the coarse sampling points within the edge grid.
[0052] In an optional implementation, step S302 above, which involves fine-sampling the coarse sampling points within the edge grid to obtain fine-sampling edge points, may include: Determine the first type and the second type of edge mesh from the edge mesh; The vertices of the first type of edge mesh are taken as the first centroids, and the sampling points that satisfy the preset distance condition with the first centroids are determined from the coarse sampling points in the first type of edge mesh as the first edge fine sampling points. The center point of the edge of the second type of edge grid is taken as the second centroid, and the sampling points that satisfy the preset distance condition with the second centroid are determined from the coarse sampling points in the second type of edge grid as the second edge fine sampling points; The first and second edge fine sampling points are determined as edge fine sampling points.
[0053] The first type of edge grid can be the grid located at the four corners of the coarse sampling measurement point distribution map, and the second type of edge grid can be other grids in the edge grid besides the grids located at the four corners of the coarse sampling measurement point distribution map.
[0054] Figure 7 The distribution map of coarse sampling measurement points provided in the embodiments of this application is as follows: Figure 7 As shown, the first type of edge grid is a set of multiple grids selected by a blue bold outline, and the second type of edge grid is a set of multiple grids selected by a red bold outline.
[0055] The vertices of the first type of edge mesh are taken as the first centroid, where the vertices of the first type of edge mesh can be the four vertices of the coarse sampling measurement point distribution map, such as... Figure 7 The first edge fine sampling point is determined from the coarse sampling points in the first type of edge grid that satisfy a preset distance condition (e.g., the closest distance) with the first centroid.
[0056] In other words, for each type of edge mesh, the vertex of each type of edge mesh is taken as the first centroid, and the sampling points that satisfy the preset distance condition with the first centroid are determined from the coarse sampling points in each type of edge mesh as the first edge fine sampling points.
[0057] Among them, the edge of the second type of edge mesh is the mesh edge in the second type of edge mesh that has no adjacent edge (such as...). Figure 7 In the EF), the center point of the edge edge of the second type of edge mesh (such as EF). Figure 7 The center point G of EF in the second centroid is used as the second centroid, and the sampling point that satisfies the preset distance condition (e.g., the closest distance) with the second centroid is determined from the coarse sampling points in the second type of edge grid as the second edge fine sampling point.
[0058] In other words, for each second-type edge grid, the center point of the edge of each second-type edge grid is taken as the second centroid, and sampling points that satisfy the preset distance condition with the second centroid are determined from the coarse sampling points in each second-type edge grid as the second edge fine sampling points.
[0059] Among them, the edge fine sampling points include the first edge fine sampling point and the second edge fine sampling point. Figure 8 The edge sampling and internal sampling result distribution diagrams provided for embodiments of this application are as follows: Figure 8 As shown, the red dots in the first type of edge grid are the first edge fine sampling points, and the red dots in the second type of edge grid are the second edge fine sampling points.
[0060] In some embodiments, if there is no grid in the coarse sampling measurement point distribution map, the coarse sampling measurement point distribution map can be further divided into an N×N (e.g., 10×10) grid.
[0061] Step S104 above, which involves fine sampling of the internal regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain internal fine sampling points, may include: S303. Determine the internal grid from multiple grids in the coarse sampling measurement point distribution map.
[0062] S304. The Poisson disk sampling algorithm is used to perform fine sampling on the coarse sampling points in the internal grid to obtain the internal fine sampling points.
[0063] The internal grid consists of all grids except the edge grids among the multiple grids distributed by the coarse sampling measurement points.
[0064] The cluster centers of the internal grid are the coarse sampling points within the internal grid. Then, the Poisson disk sampling algorithm is used to refine the coarse sampling points within the internal grid, resulting in the internal fine sampling points, such as... Figure 8 Red dots within the inner grid.
[0065] Figure 9The fine sampling measurement point distribution map provided in the embodiments of this application is as follows: Figure 9 As shown, the fine sampling measurement point distribution map includes the first edge fine sampling point, the second edge fine sampling point, and the internal fine sampling point.
[0066] In this embodiment, edge region fine sampling and internal region fine sampling belong to fine sampling. The purpose of fine sampling is to extract fine sampling points from multiple coarse sampling points, for example, extracting 100 fine sampling points from 500 coarse sampling points. By introducing grid edge region fine sampling rules and combining them with the Poisson disk sampling algorithm to perform fine sampling on the coarse sampling points after improved spatial layer sampling, the sampling of edge region graphics can be strengthened and the sampling uniformity of measurement points can be improved.
[0067] Figure 10 A flowchart illustrating the method for determining photomask measurement points provided in this application embodiment. Figure 4 ,like Figure 10 As shown, in an optional embodiment, after generating the fine sampling measurement point distribution map based on the edge fine sampling points and the internal fine sampling points in step S105, the method may further include: S401. Based on the distribution map of fine sampling measurement points, the photomask is measured to obtain the measurement results of each edge fine sampling point on the photomask for the preset measurement items, as well as the measurement results of each internal fine sampling point for the preset measurement items.
[0068] Based on the distribution map of fine sampling measurement points, find the corresponding graphics of the edge fine sampling points and the corresponding graphics of the internal fine sampling points on the photomask, and measure these graphics on the photomask to obtain the measurement results of the edge fine sampling points for the preset measurement items and the measurement results of the internal fine sampling points for the preset measurement items. The preset measurement items are CDU measurement items, such as line width measurement items.
[0069] In an alternative implementation, the method may further include: S402. If the measurement result corresponding to any sampling point does not meet the preset conditions, then multiple other coarse sampling points in the area where any sampling point is located, which meet the preset distance conditions with any sampling point, are determined from the coarse sampling measurement point distribution map as multiple new measurement points.
[0070] The measurement result corresponding to any sampling point refers to the measurement result of any sampling point for a preset measurement item. The arbitrary sampling point can be an edge fine sampling point or an internal fine sampling point. If the difference between the measurement result corresponding to any sampling point and its design target size exceeds a preset threshold, for example, if the difference between the line width corresponding to any sampling point and the design target line width exceeds a preset threshold, then it is determined that the measurement result corresponding to any sampling point does not meet the preset condition.
[0071] The preset distance condition can be determined by ranking the distances to any sampling point from the nearest to the farthest n coarse sampling points. From the distribution map of coarse sampling measurement points, multiple other coarse sampling points within the area where any sampling point is located that meet the preset distance condition are determined as multiple new measurement points. The area where any sampling point is located can be a preset area centered on the arbitrary sampling point, and the number of multiple other coarse sampling points is n.
[0072] S403. Obtain the measurement results of multiple new measurement points on the photomask for preset measurement items.
[0073] Find the corresponding patterns of each new measurement point on the photomask, and measure the corresponding patterns of each new measurement point on the photomask to obtain the measurement results of each new measurement point on the photomask for the preset measurement items.
[0074] S404. If the measurement results corresponding to multiple new measurement points do not meet the preset conditions, then it is determined that there is a process defect in the area where any sampling point on the photomask is located.
[0075] The measurement result corresponding to the new measurement point refers to the measurement result of the new measurement point for the preset measurement item. If the measurement results corresponding to multiple new measurement points do not meet the preset conditions, it indicates that there is a process defect in the area where any sampling point on the photomask is located.
[0076] The preset number of times can be, for example, 3 or 5 times, and this embodiment does not make any special limitation on this.
[0077] S405. If the measurement result corresponding to any sampling point meets the preset conditions, then it is determined that there are no process defects in the area where any sampling point on the photomask is located.
[0078] In this embodiment, in actual production applications, if the measurement result corresponding to any sampling point does not meet the preset conditions, the measurement result is fed back to the upsampling module to increase the sampling density in the area with poor measurement results, thereby realizing real-time dynamic resampling of the measurement point samples, which greatly improves the sampling efficiency and measurement cost of the measurement point. At the same time, the measurement result corresponding to the new measurement point is combined to determine whether there are process defects in the photomask, thereby improving the accuracy of defect identification.
[0079] Figure 11 This is a schematic diagram of the structure of the photomask measurement point determination device provided in the embodiments of this application. This device can be integrated into a computer device.
[0080] like Figure 11 As shown, the device may include: The acquisition module 501 is used to acquire the overall sample distribution map of the photomask, which includes multiple sample points of the photomask. The sampling module 502 is used to perform coarse sampling on the sample points in the total sample distribution map, obtain multiple coarse sampling points, and generate a coarse sampling measurement point distribution map. The sampling module 502 is also used to perform fine sampling of the edge regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain fine edge sampling points; The sampling module 502 is also used to perform fine sampling of the internal region of the coarse sampling points in the coarse sampling measurement point distribution map to obtain the internal fine sampling points. The generation module 503 is used to generate a fine sampling measurement point distribution map based on the edge fine sampling points and the internal fine sampling points, so as to perform process analysis on the photomask based on the fine sampling measurement point distribution map.
[0081] In an optional implementation, the sampling module 502 is specifically used for: The overall sample distribution map is divided into multiple grids; The number of samples in each grid is obtained based on the number of sample points in each grid, the total number of sample points, and the preset coarse sampling number. Based on the number of samples in each grid, cluster analysis is performed on the sample points in each grid, and the cluster centers of each grid are obtained. Cluster centers of multiple grids are determined as multiple coarse sampling points.
[0082] In an optional implementation, the sampling module 502 is specifically used for: Determine the edge grid from multiple grids in the coarse sampling measurement point distribution map; Fine sampling is performed on the coarse sampling points within the edge grid to obtain fine sampling points at the edge.
[0083] In an optional implementation, the sampling module 502 is specifically used for: Determine the first type and the second type of edge mesh from the edge mesh; The vertices of the first type of edge mesh are taken as the first centroids, and the sampling points that satisfy the preset distance condition with the first centroids are determined from the coarse sampling points in the first type of edge mesh as the first edge fine sampling points. The center point of the edge of the second type of edge grid is taken as the second centroid, and the sampling points that satisfy the preset distance condition with the second centroid are determined from the coarse sampling points in the second type of edge grid as the second edge fine sampling points; The first and second edge fine sampling points are determined as edge fine sampling points.
[0084] In an optional implementation, the sampling module 502 is specifically used for: Determine the internal grid from multiple grids in the coarse sampling measurement point distribution map; The Poisson disk sampling algorithm is used to perform fine sampling on the coarse sampling points in the internal grid to obtain the internal fine sampling points.
[0085] In an optional embodiment, the device further includes: The measurement module 504 is used to measure the photomask according to the distribution map of fine sampling measurement points, and to obtain the measurement results of each edge fine sampling point on the photomask for the preset measurement items, as well as the measurement results of each internal fine sampling point for the preset measurement items.
[0086] In an optional embodiment, the device further includes: The determination module 505 is used to determine multiple other coarse sampling points in the area where any sampling point is located, which meet the preset distance condition with any sampling point, as multiple new measurement points if the measurement result corresponding to any sampling point does not meet the preset condition. The acquisition module 506 acquires the measurement results of multiple new measurement points on the photomask for preset measurement items; The determination module 505 is also used to determine that if the measurement results corresponding to multiple new measurement points do not meet the preset conditions, then the area where any sampling point on the photomask is located has a process defect. The determination module 505 is also used to determine that there are no process defects in the area where any sampling point on the photomask is located if the measurement result corresponding to any sampling point meets the preset conditions.
[0087] The processing flow of each module in the device and the interaction flow between each module can be referred to the relevant descriptions in the above method embodiments, and will not be detailed here.
[0088] Figure 12 A schematic diagram of the structure of the computer device provided in the embodiments of this application, such as... Figure 12 As shown, the device may include a processor 601, a memory 602, and a bus 603. The memory 602 stores machine-readable instructions that can be executed by the processor 601. When the computer device is running, the processor 601 communicates with the memory 602 through the bus 603, and the processor 601 executes the machine-readable instructions to perform the above-described method.
[0089] This application also provides a computer-readable storage medium storing a computer program, which is executed by a processor to perform the above-described method.
[0090] In this embodiment, the computer program, when run by the processor, can also execute other machine-readable instructions to perform other methods as described in the embodiments. For details on the specific execution steps and principles, please refer to the description of the embodiments, which will not be repeated here.
[0091] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. The apparatus embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the displayed or discussed mutual couplings, direct couplings, or communication connections may be through some communication interfaces; indirect couplings or communication connections between devices or units may be electrical, mechanical, or other forms.
[0092] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.
[0093] In addition, the functional units in the embodiments provided in this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.
[0094] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0095] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, the terms "first", "second", "third", etc. are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0096] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features, within the scope of the technology disclosed in this application; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application. All should be covered within the protection scope of this application. Therefore, the protection scope of this application should be determined by the protection scope of the claims.
Claims
1. A method for determining measurement points on a photomask, characterized in that, include: Based on the pattern distribution information on the photomask, a total sample distribution map of the photomask is obtained, which includes multiple sample points of the photomask. Coarse sampling is performed on the sample points in the total sample distribution map to obtain multiple coarse sampling points, and a coarse sampling measurement point distribution map is generated. Fine sampling is performed on the edge regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain fine edge sampling points; The coarse sampling points in the coarse sampling measurement point distribution map are subjected to fine sampling in the internal region to obtain the internal fine sampling points; Based on the edge fine sampling points and the internal fine sampling points, a fine sampling measurement point distribution map is generated to perform process analysis on the photomask based on the fine sampling measurement point distribution map.
2. The method according to claim 1, characterized in that, The process of coarsely sampling the sample points in the total sample distribution map yields multiple coarsely sampled points, including: The total sample distribution map is divided into multiple grids; The number of samples in each grid is obtained based on the number of sample points in each grid, the total number of sample points, and the preset coarse sampling number. Based on the number of samples in each grid, cluster analysis is performed on the sample points in each grid, and the cluster centers of each grid are obtained. The cluster centers of the multiple grids are determined as the multiple coarse sampling points.
3. The method according to claim 1, characterized in that, The step of performing fine sampling of the edge regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain fine edge sampling points includes: The edge grid is determined from multiple grids in the coarse sampling measurement point distribution map; Fine sampling is performed on the coarse sampling points within the edge grid to obtain the edge fine sampling points.
4. The method according to claim 3, characterized in that, The step of fine-sampling the coarse sampling points within the edge grid to obtain the fine-sampling edge points includes: Determine the first type of edge mesh and the second type of edge mesh from the edge mesh; The vertices of the first type of edge mesh are used as the first centroids, and sampling points that satisfy a preset distance condition with the first centroid are determined from the coarse sampling points in the first type of edge mesh as the first edge fine sampling points. The center point of the edge of the second type of edge grid is taken as the second centroid, and the sampling points that satisfy the preset distance condition with the second centroid are determined from the coarse sampling points in the second type of edge grid as the second edge fine sampling points; The first edge fine sampling point and the second edge fine sampling point are determined as the edge fine sampling point.
5. The method according to claim 1 or 3, characterized in that, The step of performing fine sampling of the internal regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain internal fine sampling points includes: The internal grid is determined from multiple grids in the coarse sampling measurement point distribution map; The Poisson disk sampling algorithm is used to perform fine sampling on the coarse sampling points in the internal grid to obtain the internal fine sampling points.
6. The method according to claim 1, characterized in that, After generating a fine sampling measurement point distribution map based on the edge fine sampling points and the internal fine sampling points, the method further includes: Based on the fine sampling measurement point distribution map, the photomask is measured to obtain the measurement results of each edge fine sampling point on the photomask for the preset measurement items, and the measurement results of each internal fine sampling point for the preset measurement items.
7. The method according to claim 6, characterized in that, The method further includes: If the measurement result corresponding to any sampling point does not meet the preset conditions, then multiple other coarse sampling points in the area where the arbitrary sampling point is located, which meet the preset distance conditions with the arbitrary sampling point, are determined from the coarse sampling measurement point distribution map as multiple new measurement points. Obtain the measurement results of the plurality of new measurement points on the photomask for the preset measurement items; If the measurement results corresponding to the multiple new measurement points do not meet the preset conditions, then it is determined that there is a process defect in the area where any sampling point on the photomask is located. If the measurement result corresponding to any sampling point meets the preset condition, then it is determined that there are no process defects in the area where the arbitrary sampling point on the photomask is located.
8. A device for determining the measurement points of a photomask, characterized in that, include: The acquisition module is used to acquire the total sample distribution map of the photomask, which includes multiple sample points of the photomask. The sampling module is used to perform coarse sampling on the sample points in the total sample distribution map to obtain multiple coarse sampling points and generate a coarse sampling measurement point distribution map. The sampling module is also used to perform fine sampling of the edge regions of the coarse sampling points in the coarse sampling measurement point distribution map to obtain fine edge sampling points; The sampling module is also used to perform fine sampling of the internal region of the coarse sampling points in the coarse sampling measurement point distribution map to obtain internal fine sampling points; The generation module is used to generate a fine sampling measurement point distribution map based on the edge fine sampling points and the internal fine sampling points, so as to perform process analysis on the photomask based on the fine sampling measurement point distribution map.
9. A computer device, characterized in that, include: The computer device includes a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the computer device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the method according to any one of claims 1 to 7.