Photoetching process window determination method

Abstract

The invention relates to a photolithographic process window determination method, which comprises the following steps: acquiring a focusing exposure matrix which comprises test images corresponding to a plurality of photolithographic process formulas; respectively carrying out clustering analysis on the pixel values of all the pixel points in each test image, and determining a first target area in each test image; according to the first target area in each test image, performing feature statistics on each test image to obtain feature data of each test image; and determining a photoetching process window according to the feature data of each test image and the photoetching process formula of each test image. According to the method, the characteristic data of each test image is obtained through clustering analysis and characteristic statistics, and finally, the photoetching process window is accurately and efficiently determined according to the characteristic data of each test image and the photoetching process formula of each test image.

Description

Technical Field

[0001] The present application relates to the technical field of photolithography process development, and in particular to a method for determining a photolithography process window. Background Art

[0002] Photolithography process development is a core component of semiconductor manufacturing. Its goal is to accurately transfer circuit patterns onto silicon wafers or other substrates through optical and chemical methods. Determining the photolithography process window is a crucial step in photolithography process development.

[0003] The lithography process window (PW) refers to the range of parameters within which acceptable patterns can be produced during the photolithography step on a silicon wafer or other substrate during semiconductor manufacturing. This includes the range of variations in exposure dose and focus. However, there is currently a lack of a method to accurately and efficiently determine the lithography process window. Summary of the Invention

[0004] Based on this, it is necessary to provide a lithography process window determination method that can accurately and efficiently determine the lithography process window in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for determining a photolithography process window. The method comprises: obtaining a focus exposure matrix, the focus exposure matrix including test images corresponding to multiple photolithography process recipes; performing cluster analysis on the pixel values ​​of all pixels in each of the test images to determine a first target area in each of the test images; performing feature statistics on each of the test images based on the first target area in each of the test images to obtain feature data for each of the test images; and determining a photolithography process window based on the feature data of each of the test images and the photolithography process recipe of each of the test images.

[0006] In one embodiment, cluster analysis is performed on the pixel values ​​of all pixels in each of the test images to determine the first target area in each of the test images, including: the first target area includes a bottom area and an edge area; based on a clustering algorithm, cluster analysis is performed on the pixel values ​​of all pixels in the target test image to obtain category information of each pixel and the pixel average value corresponding to each category information; the target test image is any image among the multiple test images; the pixel points corresponding to one or more category information with the lowest pixel average value are used as the bottom area of ​​the target test image; the pixel points corresponding to one or more category information with the highest pixel average value are used as the edge area of ​​the target test image.

[0007] In one embodiment, the feature data includes: the pixel ratio of each pixel point corresponding to the category information, the first standard deviation of the pixel ratio of each pixel point corresponding to the category information, the ranking of the average value of each pixel point corresponding to the category information, the identification result of each category information, the maximum continuous first target area ratio, the average value and second standard deviation of the ratio of the first few large continuous bottom areas.

[0008] In one embodiment, when the feature data includes: the pixel ratio of each pixel point corresponding to the category information and the first standard deviation of the pixel ratio of each pixel point corresponding to the category information, the feature statistics of each test image are performed according to the first target area in each test image to obtain the feature data of each test image, including: obtaining the total number of all pixels in the target test image; determining the number of pixels corresponding to each category information according to the category information of each pixel point in the target test image; determining the pixel ratio of each pixel point corresponding to the category information according to the number of pixels corresponding to each category information and the total number of all pixels; the pixel ratio of each pixel point corresponding to the category information includes the pixel ratio of the bottom area and / or the pixel ratio of the edge area; and determining the first standard deviation of the pixel ratio of each pixel point corresponding to the category information according to the pixel ratio of each pixel point corresponding to the category information.

[0009] In one embodiment, when the feature data includes: the maximum continuous first target area ratio, the feature statistics are performed on each test image according to the first target area in each test image, and the feature data of each test image obtained includes: the maximum continuous first target area ratio includes: the maximum continuous bottom area ratio and / or the maximum continuous edge area ratio; the total number of all pixels in the target test image is obtained; the maximum connected domain of the bottom area is determined according to the pixel points corresponding to the bottom area in the target test image; the maximum connected domain of the edge area is determined according to the pixel points corresponding to the edge area in the target test image; the number of pixels corresponding to the maximum connected domain of the bottom area and the number of pixels corresponding to the maximum connected domain of the edge area are counted; the maximum continuous bottom area ratio is determined according to the number of pixels corresponding to the maximum connected domain of the bottom area and the total number of all pixels; the maximum continuous edge area ratio is determined according to the number of pixels corresponding to the maximum connected domain of the edge area and the total number of all pixels.

[0010] In one embodiment, when the feature data includes: the average value and the second standard deviation of the proportion of the first large continuous bottom areas, the feature statistics of each test image are performed according to the first target area in each test image to obtain the feature data of each test image, including: obtaining the total number of all pixels in the target test image; performing connected domain detection on the bottom area according to the pixel points corresponding to the bottom area in the target test image, and determining multiple target bottom areas corresponding to the first large connected domains; determining the pixel proportion of each target bottom area according to the number of pixel points corresponding to the multiple target bottom areas and the total number of all pixel points; determining the average value of the proportion of the first large continuous bottom areas according to the pixel proportion of each target bottom area; determining the second standard deviation of the proportion of the first large continuous bottom areas according to the pixel proportion of each target bottom area and the average value of the proportion of the first large continuous bottom areas.

[0011] In one embodiment, the method further includes: obtaining a first standard deviation threshold and / or a second standard deviation threshold; comparing the first standard deviation of each of the test images with the first standard deviation threshold, and filtering out test images whose first standard deviation is smaller than the first standard deviation threshold; and / or comparing the second standard deviation of each of the test images with the second standard deviation threshold, and filtering out test images whose second standard deviation is larger than the second standard deviation threshold.

[0012] In one embodiment, determining the photolithography process window based on the characteristic data of each test image and the photolithography process recipe of each test image includes: obtaining a preset data range; based on the characteristic data of multiple test images, taking the test image with the characteristic data within the preset data range as a qualified image; and determining the photolithography process window based on the photolithography process recipes corresponding to the multiple qualified images.

[0013] In one embodiment, determining the photolithography process window based on the characteristic data of each test image and the photolithography process recipe of each test image includes: obtaining a preset data range, a preset mathematical model, and a range of photolithography focal length and photolithography exposure energy in the photolithography process recipe of a focused exposure matrix; substituting the characteristic data of each test image and the photolithography process recipe corresponding to each test image into the preset mathematical model based on the preset mathematical model to determine a target fitting function; determining a target coordinate system based on the range of photolithography focal length and photolithography exposure energy in each photolithography process recipe, wherein the target coordinate system is a coordinate system related to photolithography focal length and photolithography exposure energy; Based on the upper and lower limits of the preset data range, and according to the target fitting function, the characteristic data of each test image, and the photolithography process recipe corresponding to each test image, a first contour line and a second contour line are drawn, the characteristic data of the test image corresponding to the point on the first contour line is equal to the upper limit of the preset data range, and the characteristic data of the test image corresponding to the point on the second contour line is equal to the lower limit of the preset data range; based on the first contour line, the second contour line, and the target coordinate system, the area enclosed by the first contour line, the second contour line, and the target coordinate system is used as the second target area; according to the photolithography process recipe corresponding to each test image in the second target area, the photolithography process window is determined.

[0014] In one embodiment, determining the photolithography process window based on the second target area includes: determining a maximum inscribed rectangle or a maximum inscribed ellipse within the target area based on the second target area; using an area corresponding to the maximum inscribed rectangle or the maximum inscribed ellipse as a third target area; and determining the photolithography process window based on the photolithography process recipes corresponding to each test image within the third target area.

[0015] The above-mentioned method for determining the photolithography process window, after acquiring the focused exposure matrix, performs cluster analysis on the pixel values ​​of all pixels in each test image to determine the first target area in each test image. Feature statistics are then performed on each test image to obtain feature data for each test image. Finally, the photolithography process window is determined based on the feature data of each test image and the photolithography process recipe for each test image. Through cluster analysis and feature statistics, feature data for each test image is obtained. Ultimately, based on the feature data of each test image and the photolithography process recipe for each test image, the photolithography process window is accurately and efficiently determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A diagram illustrating an application environment of a method for determining a photolithography process window in one embodiment;

[0017] Figure 21 is a flow chart of a method for determining a photolithography process window in one embodiment;

[0018] Figure 3 A schematic diagram of a process for determining a photolithography process window in one embodiment;

[0019] Figure 4 is a schematic diagram of a focused exposure matrix in another embodiment;

[0020] Figure 5 is a schematic diagram of a test image after filtering and cluster analysis in another embodiment;

[0021] Figure 6 is a schematic diagram of obtaining feature data of each test image in another embodiment;

[0022] Figure 7 is a schematic diagram of a photolithography process window in another embodiment;

[0023] Figure 8 is a schematic diagram of screening out part of a test image in another embodiment;

[0024] Figure 9 Schematic diagram of determining a target fitting function in other embodiments;

[0025] Figure 10 is a structural block diagram of a device for determining a photolithography process window in one embodiment;

[0026] Figure 11 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0028] Related technologies generally rely on the analysis of FEM data, but lack the analysis of FEM images. In addition, the analysis of FEM images generally adopts manual methods to check data and make judgments, and fails to form an automated process. There is currently a lack of a method that can accurately and efficiently determine the lithography process window.

[0029] The photolithography process window determination method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. The terminal 102 communicates with the server 104 via the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The server 104 is used to execute the photolithography process window determination method. The terminal 102 can be, but is not limited to, various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented as an independent server or a server cluster consisting of multiple servers.

[0030] In order to solve the above problem, in one embodiment of the present application, Figure 2 As shown, a method for determining a photolithography process window is provided, comprising the following steps:

[0031] Step 201: Obtain a focus exposure matrix.

[0032] The focused exposure matrix includes test images corresponding to various photolithography process recipes, such as Figure 4 As shown, Figure 4 Figure A in the figure shows the focus exposure matrix. Figure 4 Figure B in the figure shows one of the test images. The lithography process recipe includes the lithography focal length and the lithography exposure energy. The focus exposure matrix is ​​a two-dimensional matrix formed by dividing the wafer into multiple areas under different lithography focal lengths and lithography exposure energies. The test image is an image of a certain area on the wafer formed under a lithography process recipe. The lithography focal length is the distance from the focus of parallel light after passing through the lens to the area on the wafer. The lithography exposure energy is the energy of light received by each area of ​​the wafer.

[0033] Step 202: Perform cluster analysis on the pixel values ​​of all pixels in each test image to determine the first target area in each test image.

[0034] The cluster analysis is based on a clustering algorithm, and classifies all pixels in each test image according to the pixel values ​​of all pixels in each test image to obtain the category information of each pixel and the pixel average value corresponding to each category information. The first target area includes the bottom area and the edge area.

[0035] Step 203 : Perform feature statistics on each test image according to the first target area in each test image to obtain feature data of each test image.

[0036] The feature data includes: the pixel ratio of each pixel point corresponding to the category information, that is, the ratio between the pixel point corresponding to each pixel point of the category information and the total number of all pixel points in each test image, the first standard deviation of the pixel ratio of each pixel point corresponding to the category information, that is, the standard deviation between the pixel ratios of each pixel point corresponding to the category information, the ranking of the average pixel values ​​corresponding to each category information, that is, the result of sorting each category information according to the average pixel value corresponding to each category information, the identification result of each category information, the ratio of the largest continuous first target area, that is, the ratio between the number of pixel points in the largest continuous area in the first target area and the total number of all pixel points in the test image, the average value and the second standard deviation of the ratio of the first large continuous bottom areas, that is, the average value and standard deviation of the ratio between the pixel points of the multiple continuous bottom areas with the largest number of pixels and the total number of all pixel points in each test image.

[0037] The calculation method for the pixel ratio of each pixel point corresponding to the category information is: for each category, count the number of pixels in the image that are classified into this category, and then divide this number by the total number of pixels in the image. The obtained ratio is the pixel ratio of this category. The calculation method for the first standard deviation of the pixel ratio of each pixel point corresponding to the category information is: in this image, the standard deviation

[0038] Where K is the total number of classes, r i is the proportion of pixels in each category, is the average value of the proportion of all types of pixels. The calculation method of the average proportion of the first few continuous bottom areas is: the user needs to provide the number of statistics to be counted according to the needs. Figure 5 For example, the user believes that an image contains 5 lines and there are 4 bottom areas between the lines. Therefore, the proportion of the first 4 continuous bottom areas should be counted and their average value should be calculated, such as Figure 6 The second standard deviation of the proportion of the previous large consecutive bottom areas is calculated as follows: similar to the average of the proportion of the previous large consecutive bottom areas, but the standard deviation of the proportion of the previous 4 large consecutive bottom areas is calculated and multiplied by 100 (still based on Figure 5 For example; multiplying by 100 is to reduce the number of decimal places for easy representation, and it can be omitted). Figure 6 .

[0039] Exemplarily, feature statistics are performed on each test image to obtain feature data of each test image.

[0040] Step 204 : Determine a photolithography process window according to the feature data of each test image and the photolithography process recipe of each test image.

[0041] That is, by comparing whether the feature data of each test image is within a preset data range, the photolithography process window is determined based on each test image whose feature data is within the preset data range and the photolithography process recipe corresponding to each test image. The preset data range is the preset range of feature data. If the feature data of a test image is within the preset data range, it indicates that the photolithography process recipe of the test image is within the photolithography process window. For example, taking the pixel ratio of each pixel corresponding to the category information as an example, after obtaining the preset data range corresponding to the pixel ratio of each pixel corresponding to the category information, the pixel ratio of each pixel corresponding to the category information of each test image is compared with the preset data range, and the test images whose pixel ratio of each pixel corresponding to the category information is within the preset data range are screened out. Based on the photolithography process recipes corresponding to the multiple screened test images, the photolithography process window is finally determined. The photolithography process window refers to the parameter range that can produce a qualified pattern in the step of photolithography on silicon wafers or other substrates in the semiconductor manufacturing process, that is, the range of variation of photolithography exposure energy and photolithography focal length.

[0042] It should be noted that the method for determining the lithography process window also includes: filtering the focus exposure matrix. That is, using a filtering algorithm to process each test image in the focus exposure matrix to remove or reduce noise in the test image. The filtering algorithms that can be used include mean filtering, median filtering, Gaussian filtering, bilateral filtering, or other existing open source algorithms. Figure 5 As shown, Figure 5 Figure A in the figure shows the unfiltered test image. Figure 5 Figure B in FIG shows a test image processed using a bilateral filtering algorithm.

[0043] In the above-mentioned method for determining the photolithography process window, after obtaining the focused exposure matrix, cluster analysis is performed on the pixel values ​​of all pixels in each test image to determine the first target area in each test image. Feature statistics are then performed on each test image to obtain feature data for each test image. Finally, the photolithography process window is determined based on the feature data of each test image and the photolithography process recipe for each test image. Through cluster analysis and feature statistics, feature data for each test image is obtained. Ultimately, based on the feature data of each test image and the photolithography process recipe for each test image, the photolithography process window is accurately and efficiently determined.

[0044] In other embodiments of the present application, performing cluster analysis on the pixel values ​​of all pixels in each of the test images to determine the first target area in each of the test images includes:

[0045] Step 1: Based on the clustering algorithm, perform cluster analysis on the pixel values ​​of all pixels in the target test image to obtain the category information of each pixel and the pixel average value corresponding to each category information.

[0046] A clustering algorithm aims to group similar objects in a dataset into clusters, maximizing the similarity within clusters and the differences between clusters. Examples include K-Means clustering, mean-shift clustering, density-based clustering (DBSCAN), Gaussian mixture model (DMM) clustering, or other existing open-source algorithms. The clustering algorithm and its associated parameters can be selected and adjusted based on the noise level of the test image and the desired number of classifications. Its core objective is to discover patterns through the inherent structure of the data, without relying on pre-labeled labels. For example, cluster analysis classifies all pixels in each test image based on their pixel values ​​or grayscale values, obtaining category information for each pixel and the pixel mean corresponding to each category. The category information is the classification result of the pixels in each test image using the clustering algorithm. The pixel mean corresponding to each category is the average of the pixel values ​​for each pixel corresponding to a particular category.

[0047] It's important to note that a filtered test image is fed into a clustering algorithm. Based on the information about each pixel in the image, the clustering algorithm assigns a label to each pixel, thereby classifying it into several (at least three) clusters. However, the clustering algorithm itself does not reveal the differences between clusters, and the order of the labels is random. (For example, if a basket of mixed apples and oranges is fed to the clustering algorithm, it will label all apples and oranges with labels A and B, respectively; however, the algorithm's output does not indicate whether label A or B corresponds to an apple or an orange, requiring verification by the operator.) Therefore, classification is necessary. The clustering algorithm outputs a set of data indicating the label assigned to each pixel in the input image (i.e., the category assigned to it). Classification: Once the image pixels are classified into these categories, the average grayscale value of each category is required (i.e., whether a particular label is an apple or an orange). Based on the clustering algorithm's output—that is, the classification result for each pixel—the grayscale value of each pixel in the original image is statistically analyzed to determine the average grayscale value of each category. The class with the highest average grayscale is identified as the "edge", and the class with the lowest average grayscale is identified as the "bottom". That is, based on the output of clustering, the grayscale of pixels on the original image is counted, and what is identified is the category information.

[0048] The first target area includes a bottom area and an edge area; the target test image is any one of the multiple test images. It should be noted that although the relevant steps of this embodiment are performed on the target test image in this embodiment, in actual circumstances, each test image in the focused exposure matrix is ​​used as a target test image, and the relevant steps of this embodiment are performed on each test image.

[0049] The bottom area is the area of ​​pixels corresponding to one or more category information with the lowest pixel average value. The edge area is the area of ​​pixels corresponding to one or more category information with the highest pixel average value.

[0050] Step 2: The pixel points corresponding to one or more category information with the lowest pixel average value are used as the bottom area of ​​the target test image.

[0051] Exemplarily, after executing a clustering algorithm to perform cluster analysis on the pixel values ​​of all pixels in the target test image and obtaining the category information of each pixel and the pixel average value corresponding to each category information, the category information of each pixel is sorted based on the pixel average value corresponding to each category information, and one or more category information with the lowest pixel average value is taken, and the pixel points corresponding to the one or more category information with the lowest pixel average value are used as the bottom area of ​​the target test image.

[0052] Step 3: Pixel points corresponding to one or more category information with the highest pixel average value are taken as edge areas of the target test image.

[0053] Exemplarily, after executing a clustering algorithm to perform cluster analysis on the pixel values ​​of all pixels in the target test image and obtaining the category information of each pixel and the pixel average value corresponding to each category information, the category information of each pixel is sorted based on the pixel average value corresponding to each category information, and one or more category information with the highest pixel average value is taken, and the pixel points corresponding to the one or more category information with the highest pixel average value are used as the edge area of ​​the target test image.

[0054] It should be noted that the cluster analysis of the pixel values ​​of all pixels in each of the test images to determine the first target area in each of the test images further includes: coloring according to the category information of each pixel. Figure 5 As shown in Figure C, after coloring, the colors of pixels of different categories of information are different.

[0055] It should be noted that the number of category information is greater than or equal to 3.

[0056] The average grayscale value of pixels belonging to each category in the test image is calculated, and the categories are sorted based on the calculated average grayscale value. The category with the lowest average grayscale value is considered the "bottom," and the category with the highest average grayscale value is considered the "edge." The clustering algorithm processes only one image at a time and classifies pixels within that image, but it can process multiple images in parallel.

[0057] When the number of clusters is equal to or greater than four, users of this technical solution must compare the clustering results with the test images and decide whether to add more clusters during the classification process. For the same set of FEM images, obtained on the same machine, using the same wafer and pattern measurement recipe, classification only needs to be performed once and then applied to all other images in the set.

[0058] The following is an example explanation of "When the number of categories is equal to or greater than 4, the user of this technical solution should compare the clustering results with the original image and decide whether to add more categories during identification." For example, Figure 5 Center (C) illustrates the results of a four-category clustering and coloring using K-Means clustering. The clusters in the image are colored orange, yellow, green, and blue, based on their average grayscale, from brightest (largest grayscale value) to darkest (smallest grayscale value). By comparing this image with the test image, the user can conclude that the blue portion represents the bottom region; however, the orange portion is too small to adequately represent the edge region; only the orange and yellow colors together can represent the edge region. Therefore, the blue cluster is designated as the "bottom," and the orange and yellow clusters are designated as the "edge." Here, the blue cluster represents the darkest cluster, the orange cluster represents the brightest cluster, and the yellow cluster represents the second-brightest cluster. Therefore, for the same set of FEM images to which this image belongs, the darkest cluster is designated as the "bottom," and the brightest and second-brightest clusters are collectively designated as the "edge."

[0059] In other embodiments of the present application, when the feature data includes: a pixel ratio of each pixel corresponding to the category information and a first standard deviation of the pixel ratio of each pixel corresponding to the category information, the feature data of each test image obtained by performing feature statistics on each test image according to the first target area in each test image includes:

[0060] Step 1: Get the total number of all pixels in the target test image.

[0061] Step 2: According to the category information of each pixel in the target test image, determine the number of pixels corresponding to each category information.

[0062] That is, according to the category information of each pixel in the target test image, the number of pixels corresponding to each category information in the target test image is counted.

[0063] Step 3: Determine the pixel ratio of the pixels corresponding to each category information based on the number of pixels corresponding to each category information and the total number of all pixels.

[0064] That is, the number of pixels corresponding to each category of information is divided by the total number of all pixels to obtain the pixel ratio of each category of information. The pixel ratio of each category of information includes the pixel ratio of the bottom area and the pixel ratio of the edge area.

[0065] Step 4: Determine a first standard deviation of the pixel ratio of each category information corresponding to the pixel points according to the pixel ratio of each category information corresponding to the pixel points.

[0066] That is, based on the calculated pixel ratio of each category information corresponding to the pixel point, the average value of the pixel ratio of each category information corresponding to the pixel point is calculated, and finally, based on the pixel ratio of each category information corresponding to the pixel point and the average value of the pixel ratio of each category information corresponding to the pixel point, the first standard deviation of the pixel ratio of each category information corresponding to the pixel point is determined.

[0067] In other embodiments of the present application, when the feature data includes: the maximum continuous first target area ratio, feature statistics are performed on each test image according to the first target area in each test image, and the feature data obtained for each test image includes:

[0068] Step 1: Get the total number of all pixels in the target test image.

[0069] The maximum continuous first target area ratio includes the maximum continuous bottom area ratio and the maximum continuous edge area ratio. The maximum continuous bottom area ratio is the ratio of the number of pixels in the continuous bottom area with the largest number of pixels to the total number of all pixels. The maximum continuous edge area ratio is the ratio of the number of pixels in the continuous edge area with the largest number of pixels to the total number of all pixels.

[0070] Step 2: Determine the largest connected domain in the bottom area based on the pixel points corresponding to the bottom area in the target test image.

[0071] That is, according to the pixel points corresponding to the bottom area in the target test image, the maximum connected domain of the bottom area is taken as the maximum continuous bottom area.

[0072] Step 3: Determine the maximum connected domain of the edge region based on the pixel points corresponding to the edge region in the target test image.

[0073] That is, according to the pixel points corresponding to the edge area in the target test image, the maximum connected domain of the edge area is taken as the maximum continuous edge area.

[0074] Step 4: Count the number of pixels corresponding to the largest connected domain in the bottom area and the number of pixels corresponding to the largest connected domain in the edge area.

[0075] Step 5: Determine the maximum continuous bottom area ratio based on the number of pixels corresponding to the largest connected domain in the bottom area and the total number of all pixels.

[0076] That is, the number of pixels corresponding to the largest connected domain in the bottom area is divided by the total number of all pixels to calculate the maximum continuous bottom area ratio.

[0077] Step 6: Determine the maximum continuous edge area ratio based on the number of pixels corresponding to the largest connected domain in the edge area and the total number of all pixels.

[0078] That is, the number of pixels corresponding to the largest connected domain in the edge area is divided by the total number of all pixels to calculate the maximum continuous edge area ratio.

[0079] For example, taking the maximum continuous bottom area ratio as an example, in the bottom area identified in the target test image, first randomly select a pixel point, and then select all the adjacent pixels belonging to the bottom area, until all the pixels adjacent to the area do not belong to the bottom area, and count the number of pixels in the area. Then select a pixel belonging to the bottom area that has not been selected and counted before, and then select all the adjacent pixels belonging to the bottom area (because of the requirement of being adjacent, it is impossible to select pixels that have been selected and counted before), count the number of these pixels, and repeat this step until all the pixels belonging to the bottom area have been selected and counted once. Among the multiple selections and statistics, find the one with the largest number of pixels, and record this number as the number of pixels in the maximum continuous bottom area.

[0080] In other embodiments of the present application, when the feature data includes: the average value and the second standard deviation of the proportion of the first number of large continuous bottom areas, the feature statistics of each test image are performed based on the first target area in each test image, and the feature data of each test image obtained includes:

[0081] Step 1: Get the total number of all pixels in the target test image.

[0082] Step 2: Perform connected domain detection on the bottom area according to the pixel points corresponding to the bottom area in the target test image, and determine multiple target bottom areas corresponding to the top several large connected domains.

[0083] That is, traverse the bottom area and select multiple target bottom areas corresponding to the top large connected domains from the bottom area. For example, in the bottom area identified in the target test image, arbitrarily select a pixel point, then select all the pixels belonging to the bottom area adjacent to it, until all the pixels adjacent to the area do not belong to the bottom area, and count the number of pixels in the area. Then select a pixel belonging to the bottom area that has not been selected and counted before, and then select all the pixels belonging to the bottom area adjacent to it (because of the requirement of adjacency, it is impossible to select pixels that have been selected and counted before), count the number of these pixels, and repeat this step until all the pixels belonging to the bottom area have been selected and counted once. Among the multiple connected bottom areas obtained by multiple selections and statistics, find the multiple connected bottom areas with the largest number of pixels, and use the multiple connected bottom areas with the largest number of pixels as the multiple target bottom areas corresponding to the top large connected domains. The number of target bottom areas can be set by the staff based on experience.

[0084] Step 3: Determine the pixel ratio of each target bottom area based on the number of pixels corresponding to the multiple target bottom areas and the total number of all pixels.

[0085] That is, the number of pixels corresponding to the target bottom area is divided by the total number of all pixels to calculate the pixel ratio of each target bottom area.

[0086] Step 4: Based on the pixel ratio of each target bottom area, determine the average ratio of the previous largest consecutive bottom areas.

[0087] Step 5: Determine the second standard deviation of the proportions of the first several large consecutive bottom areas based on the pixel proportion of each target bottom area and the average proportion of the first several large consecutive bottom areas.

[0088] For example, Figure 6 As shown, in one embodiment of the present application, the number of target bottom areas is 4, among which the pixel proportion of the largest continuous bottom area is 11.6%, the pixel proportion of the second largest continuous bottom area is 11.5%, the pixel proportion of the third largest continuous bottom area is 11.4%, and the pixel proportion of the fourth largest continuous bottom area is 11.3%. The average of the proportions of the first several large continuous bottom areas is 11.45%, and taking three significant decimal places, the average of the proportions of the first several large continuous bottom areas is 11.5%, and the second standard deviation of the proportions of the first several large continuous bottom areas is 0.1118.

[0089] In other embodiments of the present application, the method further includes:

[0090] Step 1: Obtain a first standard deviation threshold and / or a second standard deviation threshold.

[0091] The first standard deviation threshold is a preset threshold of the first standard deviation of the pixel ratio of each pixel point corresponding to the category information. The second standard deviation threshold is a preset threshold of the second standard deviation of the ratio of the first number of large continuous bottom areas.

[0092] Step 2: Compare the first standard deviation of each test image with the first standard deviation threshold, and filter out test images whose first standard deviation is smaller than the first standard deviation threshold; and / or compare the second standard deviation of each test image with the second standard deviation threshold, and filter out test images whose second standard deviation is larger than the second standard deviation threshold.

[0093] Users can use the first standard deviation of the pixel count ratio or the second standard deviation of the top largest continuous bottom area ratio to filter images and data, thereby eliminating erroneous or defective images and data. Users must specify the standard deviation threshold for the pixel count ratio and the first or second standard deviation threshold for the top largest continuous bottom area ratio.

[0094] If a standard deviation threshold for the proportion of each type of pixel is given, then images and data with a value smaller than this threshold should be filtered out, because for good images, the differences in the proportions of various regions are more obvious.

[0095] If a standard deviation threshold of the proportion of the first few large continuous "bottom areas" is given, then images and data with a value greater than this threshold should be filtered out, because for good images, the proportion of the first few large continuous "bottom areas" should be almost the same.

[0096] Take the standard deviation threshold of the previous large continuous "bottom area ratio" as an example, and Figure 4 Take the image and data in as an example. The user sets the standard deviation threshold of the first 4 consecutive "bottom area ratios" to 1.5. Figure 8 A in FIG is the result after performing step 202 on all images. Figure 8 B in step 203 is the result of calculating the standard deviation of the proportion of the first four consecutive "bottom areas" of each image. The data with a standard deviation greater than the threshold of 1.5 are added with a black border, and the images corresponding to these data are Figure 8 A in Figure 1 is bordered in red. These images show the collapsed photoresist after overexposure. The corresponding measurement data does not reflect the actual situation and needs to be filtered out.

[0097] It should be noted that the relevant steps of this embodiment can be performed before or after determining the lithography process window based on the characteristic data of each test image and the lithography process recipe of each test image. In other embodiments of the present application, the lithography process window is not determined based on the characteristic data of each test image and the lithography process recipe of each test image. Instead, the relevant steps of this embodiment are performed after obtaining the characteristic data of each test image, thereby filtering the data.

[0098] In other embodiments of the present application, Figure 3 As shown, determining the photolithography process window according to the feature data of each test image and the photolithography process recipe of each test image includes:

[0099] Step 301: Obtain a preset data range.

[0100] The preset data range is a range of preset feature data. If the feature data of a test image is within the preset data range, it indicates that the photolithography process recipe of the test image is within the photolithography process window.

[0101] Step 302: Based on the feature data of the plurality of test images, the test images whose feature data are within a preset data range are regarded as images that meet the requirements.

[0102] That is, test images whose characteristic data are within the preset data range are screened out as images that meet the requirements.

[0103] Step 303: Determine a photolithography process window according to the photolithography process recipes corresponding to the multiple images that meet the requirements.

[0104] For example, Figure 7 As shown, Figure 7 Figure A is the focus exposure matrix. Figure 7 Figure B in the figure is the average value of the proportion of the first several continuous bottom areas corresponding to each test image in the focused exposure matrix. That is, according to the lithography process recipe corresponding to multiple images that meet the requirements, the variation range of the lithography exposure energy and lithography focal length in the lithography process window is determined. The lithography process window is Figure 7 The area selected in the middle box.

[0105] It should be noted that users must select the analysis method based on their needs. Analysis methods include: Bottom Area Fraction. If this method is selected, the user must also provide the allowable bottom area fraction range, including the minimum and maximum allowable fractions. Within the entire set of FEM images, those with a bottom area fraction greater than or equal to the minimum allowable fraction and less than or equal to the maximum allowable fraction are considered to meet the requirements. The corresponding exposure energy and focal length ranges are considered to be the lithography process window. Maximum Continuous Bottom Area Fraction. If this method is selected, the analysis method is similar to the Bottom Area Fraction, except that the Bottom Area Fraction is replaced by the Maximum Continuous Bottom Area Fraction. Edge Area Fraction. If this method is selected, the analysis method is similar to the Bottom Area Fraction, except that the Bottom Area Fraction is replaced by the Edge Area Fraction. Maximum Continuous Edge Area Fraction. If this method is selected, the analysis method is similar to the Bottom Area Fraction, except that the Bottom Area Fraction is replaced by the Maximum Continuous Edge Area Fraction. Average of the First Continuous Bottom Area Fractions. If this method is selected, the analysis method is similar to the Bottom Area Fraction, except that the Bottom Area Fraction is replaced by the Average of the First Continuous Bottom Area Fractions.

[0106] For example, the analysis method of the average value of the previous large continuous bottom area ratio is used as an example, and Figure 4 For example, the image and data in [1] are used. The user sets the allowable range for the average of the first four consecutive "bottom area ratios" to be 10.5% to 12.5%. Figure 7 A in FIG is the result after performing step 202 on all images. Figure 7 B in step 203 is the result of calculating the average value of the proportion of the first four consecutive "bottom areas" of each image. According to the allowed range, Figure 7 A portion of the data in B is bordered with black, and the image corresponding to this data is in Figure 7 A in Figure 1 is bordered in red. The lithography process recipe within this border is the lithography process window.

[0107] It should be noted that if it is necessary to execute the relevant steps of this embodiment, it is necessary to obtain at least any preset data range of the pixel ratio of the bottom area, the pixel ratio of the edge area, the maximum continuous bottom area ratio, the maximum continuous edge area ratio and the average value of the previous large continuous bottom area ratios.

[0108] In other embodiments of the present application, determining the photolithography process window according to the feature data of each test image and the photolithography process recipe of each test image includes:

[0109] Step 1: Obtain a preset data range, a preset mathematical model, and a range of photolithography focal length and photolithography exposure energy in a photolithography process recipe of a focused exposure matrix.

[0110] The preset data range is the range of preset feature data. If the feature data of a test image is within the preset data range, it indicates that the photolithography process recipe of the test image is within the photolithography process window. The preset mathematical model is a preset mathematical model used to fit the feature data of each test image and the photolithography process recipe corresponding to each test image, for example: or

[0111] Where Data is the feature data, a ij is the unknown coefficient, E is the lithography focal length, F is the lithography exposure energy, and M and N are the coefficients of the mathematical model specified by the user.

[0112] Step 2: Based on a preset mathematical model, the characteristic data of each test image and the photolithography process recipe corresponding to each test image are substituted into the preset mathematical model to determine a target fitting function.

[0113] like Figure 9 As shown, based on a preset mathematical model, the characteristic data of each test image and the corresponding lithography process recipe are substituted into the preset mathematical model for fitting, ultimately obtaining a target fitting function. The target fitting function is a fitting function of the characteristic data of each test image with respect to the lithography focal length and lithography exposure energy.

[0114] Specifically, it can be implemented using open-source algorithm libraries or data analysis software. The input of this step is a set of data, including the feature data of each test image and the lithography process recipe corresponding to each test image, namely (E k , F k ,Data k ), k∈[1,P], where Data k is the characteristic data, E k and F k is the lithography process recipe (lithography focal length and lithography exposure energy) corresponding to the feature data, and P is the number of this set of data.

[0115] Based on a pre-set mathematical model, the aforementioned data is then input into the model. The program, algorithm, or software then fits the input data to the mathematical model, thereby solving for the unknown coefficients within the model. Once the values ​​of the unknown coefficients are known, the mathematical model becomes a function whose dependent variables are exposure energy E and focal length F, and whose independent variables are characteristic data. For arbitrary inputs of E and F, the corresponding data can be calculated, thus obtaining a quantitative functional relationship between the lithography focal length, lithography exposure energy, and characteristic data, thereby determining the target fitting function.

[0116] Step 3: Determine the target coordinate system based on the range of the photolithography focal length and the photolithography exposure energy in each photolithography process recipe.

[0117] The target coordinate system is a two-dimensional coordinate system related to the photolithography focal length and the photolithography exposure energy. The ranges of its X-axis and Y-axis are related to the ranges of the photolithography focal length and the photolithography exposure energy. For example, the range of the X-axis of the target coordinate system is equal to the range of the photolithography focal length, and the range of the Y-axis of the target coordinate system is equal to the range of the photolithography exposure energy.

[0118] Exemplarily, the quantitative functional relationship is plotted in the form of contour lines with the lithography focal length and the lithography exposure energy as coordinate axes, and the lower and upper limits of the allowable characteristic data, that is, the target coordinate system, are marked in the figure.

[0119] Step 4: Based on the upper and lower limits of the preset data range, and according to the target fitting function, the characteristic data of each test image, and the photolithography process recipe corresponding to each test image, draw the first contour line and the second contour line.

[0120] Specifically, the upper and lower limits of the preset data range are substituted into the target fitting function, and a mathematical transformation is performed to obtain a two-dimensional relationship expression for the upper limit of the characteristic data of the lithography focal length and the lithography exposure energy, and a two-dimensional relationship expression for the lower limit of the reference data of the lithography focal length and the lithography exposure energy. The two-dimensional relationship expression for the upper limit of the characteristic data and the two-dimensional relationship expression for the lower limit of the reference data are then plotted in the form of contour lines on the target coordinate system, thereby obtaining a first contour line and a second contour line. The feature data of the test image corresponding to the point on the first contour line is equal to the upper limit of the preset data range, and the feature data of the test image corresponding to the point on the second contour line is equal to the lower limit of the preset data range.

[0121] Step 5: Based on the first contour line, the second contour line and the target coordinate system, an area enclosed by the first contour line, the second contour line and the target coordinate system is used as a second target area.

[0122] It should be noted that because the X-axis and Y-axis of the target coordinate system have ranges, that is, the ranges of the lithography focal length and the lithography exposure energy have upper and lower limits, the area enclosed by the first contour line, the second contour line and the target coordinate system must be a sealed area, and the sealed area is the second target area.

[0123] Step 6: Determine the photolithography process window according to the photolithography process recipe corresponding to each test image in the second target area.

[0124] Exemplarily, the variation range of the lithography exposure energy and the lithography focal length in the lithography process window is determined based on the lithography process recipe corresponding to each test image in the second target area. Specifically, the lithography process recipe corresponding to each test image in the second target area is traversed, and the minimum and maximum values ​​of the lithography exposure energy therein are used as the variation range of the lithography exposure energy in the lithography process window, and the minimum and maximum values ​​of the lithography focal length therein are used as the variation range of the lithography focal length in the lithography process window.

[0125] It should be noted that within the area enclosed by the contour lines of the lower and upper limits of the preset data range, the largest rectangle or ellipse is found. The lithography process recipe corresponding to this rectangle or ellipse is a more practical lithography process window. In other embodiments of the present application, determining the lithography process window based on the second target area includes:

[0126] Step 1: According to the second target area, determine the largest inscribed rectangle or the largest inscribed ellipse in the target area.

[0127] That is, any point belonging to the first contour line or the second contour line is randomly selected as the target intersection point; according to the target intersection point, all rectangles or ellipses set in the second target area are traversed to screen out the rectangle or ellipse with the largest area.

[0128] Step 2: The area corresponding to the maximum inscribed rectangle or the maximum inscribed ellipse is used as the third target area.

[0129] Step 3: Determine the photolithography process window according to the photolithography process recipe corresponding to each test image in the third target area.

[0130] That is, based on the lithography process recipes corresponding to the test images within the third target area, the variation ranges of the lithography exposure energy and the lithography focal length within the lithography process window are determined. Specifically, the lithography process recipes corresponding to the test images within the third target area are traversed, and the minimum and maximum values ​​of the lithography exposure energy therein are used as the variation range of the lithography exposure energy within the lithography process window, and the minimum and maximum values ​​of the lithography focal length therein are used as the variation range of the lithography focal length within the lithography process window.

[0131] It should be noted that the variation range of the lithography exposure energy and the lithography focal length of the lithography process window determined according to the lithography process recipe corresponding to each test image in the third target area is more uniform, and the determined lithography process window is more reasonable.

[0132] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above may include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0133] Based on the same inventive concept, embodiments of the present application further provide a device for determining a lithography process window for implementing the aforementioned method for determining a lithography process window. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more embodiments of the device for determining a lithography process window provided below can be found in the aforementioned limitations of the method for determining a lithography process window, and will not be further elaborated here.

[0134] In one embodiment of the present application, Figure 10 As shown, a device for determining a photolithography process window is provided, comprising:

[0135] The acquisition module 100 is used to acquire a focus exposure matrix.

[0136] The analysis module 200 is used to perform cluster analysis on the pixel values ​​of all pixels in each of the test images, determine the first target area in each of the test images, and perform feature statistics on each of the test images based on the first target area in each of the test images to obtain feature data of each of the test images.

[0137] The determination module 300 is configured to determine a photolithography process window according to the feature data of each test image and the photolithography process recipe of each test image.

[0138] Each module in the aforementioned device for determining a lithography process window may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0139] In one embodiment of the present application, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 11As shown. The computer device includes a processor, a memory and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store all relevant data for executing the method for determining the photolithography process window. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for determining the photolithography process window is implemented.

[0140] Those skilled in the art will understand that Figure 11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0141] In one embodiment of the present application, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the steps of the method for determining the lithography process window in the above embodiment are implemented.

[0142] In one embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. The computer program is executed by a processor to implement the steps of the photolithography process window determination method in the above-mentioned method embodiments.

[0143] In one embodiment of the present application, a computer program product is provided, including a computer program, which, when executed by a processor, implements the steps of the photolithography process window determination method in the above-mentioned method embodiments.

[0144] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0145] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The above-mentioned computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0146] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0147] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A method for determining a photolithography process window, characterized in that: The method comprises: Acquire a focus exposure matrix, wherein the focus exposure matrix includes test images corresponding to multiple photolithography process recipes; Performing cluster analysis on the pixel values ​​of all pixels in each of the test images to determine a first target area in each of the test images; performing feature statistics on each of the test images according to the first target area in each of the test images to obtain feature data of each of the test images; A photolithography process window is determined according to the feature data of each test image and the photolithography process recipe of each test image.

2. The method for determining the photolithography process window according to claim 1, wherein: The cluster analysis is performed on the pixel values ​​of all pixels in each of the test images to determine that the first target area in each of the test images includes: the first target area includes a bottom area and an edge area; Based on a clustering algorithm, cluster analysis is performed on the pixel values ​​of all pixels in a target test image to obtain category information of each pixel and the pixel average value corresponding to each category information; the target test image is any one of the multiple test images; Taking one or more pixel points corresponding to the category information with the lowest pixel average value as the bottom area of ​​the target test image; The pixel points corresponding to the one or more category information having the highest pixel average value are used as the edge area of ​​the target test image.

3. The method for determining the photolithography process window according to claim 2, wherein: The feature data includes: the pixel ratio of each pixel point corresponding to the category information, the first standard deviation of the pixel ratio of each pixel point corresponding to the category information, the ranking of the average value of each pixel point corresponding to the category information, the identification result of each category information, the ratio of the largest continuous first target area, and the average and second standard deviation of the ratio of the first few large continuous bottom areas.

4. The method for determining the photolithography process window according to claim 3, wherein: When the feature data includes: a pixel ratio of each pixel corresponding to the category information and a first standard deviation of the pixel ratio of each pixel corresponding to the category information, the feature data of each test image obtained by performing feature statistics on each test image according to the first target area in each test image includes: Obtaining the total number of all pixels in the target test image; Determining the number of pixels corresponding to each category of information based on the category information of each pixel in the target test image; Determining, based on the number of pixels corresponding to each category information and the total number of all pixels, a pixel ratio of each pixel corresponding to the category information; the pixel ratio of each pixel corresponding to the category information includes a pixel ratio of a bottom area and / or a pixel ratio of an edge area; According to the pixel ratio of each pixel point corresponding to the category information, a first standard deviation of the pixel ratio of each pixel point corresponding to the category information is determined.

5. The method for determining the photolithography process window according to claim 3, wherein: When the feature data includes: the maximum continuous first target area ratio, the feature statistics of each test image are performed according to the first target area in each test image, and the feature data of each test image obtained includes: the maximum continuous first target area ratio includes: the maximum continuous bottom area ratio and / or the maximum continuous edge area ratio; Obtaining the total number of all pixels in the target test image; Determining a maximum connected domain of the bottom area according to pixel points corresponding to the bottom area in the target test image; Determining a maximum connected domain of the edge region based on pixel points corresponding to the edge region in the target test image; Counting the number of pixels corresponding to the largest connected domain in the bottom area and the number of pixels corresponding to the largest connected domain in the edge area; Determine the maximum continuous bottom area proportion based on the number of pixels corresponding to the largest connected domain in the bottom area and the total number of all pixels; The maximum continuous edge area ratio is determined according to the number of pixels corresponding to the largest connected domain in the edge area and the total number of all pixels.

6. The method for determining the photolithography process window according to claim 3, wherein: When the feature data includes: the average value and the second standard deviation of the proportion of the first large continuous bottom areas, the feature statistics of each test image are performed based on the first target area in each test image, and the feature data of each test image obtained includes: Obtaining the total number of all pixels in the target test image; Performing connected domain detection on the bottom area according to the pixel points corresponding to the bottom area in the target test image to determine a plurality of target bottom areas corresponding to the top largest connected domains; Determining a pixel ratio of each target bottom area according to the number of pixels corresponding to the plurality of target bottom areas and the total number of all pixels; Determine the average of the proportions of the preceding largest consecutive bottom areas according to the pixel proportion of each target bottom area; A second standard deviation of the proportions of the first several large consecutive bottom areas is determined according to the pixel proportion of each target bottom area and the average of the proportions of the first several large consecutive bottom areas.

7. The method for determining the photolithography process window according to claim 3, wherein: The method further comprises: Obtaining a first standard deviation threshold and / or a second standard deviation threshold; Comparing the first standard deviation of each test image with the first standard deviation threshold, and filtering out test images whose first standard deviation is smaller than the first standard deviation threshold; and / or The second standard deviation of each test image is compared with the second standard deviation threshold, and the test images whose second standard deviation is greater than the second standard deviation threshold are screened out.

8. The method for determining the photolithography process window according to claim 3, wherein: Determining the photolithography process window according to the feature data of each test image and the photolithography process recipe of each test image includes: Get the preset data range; Based on the feature data of the plurality of test images, taking the test images whose feature data are within the preset data range as images that meet the requirements; A photolithography process window is determined according to the photolithography process recipes corresponding to the plurality of images meeting the requirements.

9. The method for determining the photolithography process window according to claim 3, wherein: Determining the photolithography process window according to the feature data of each test image and the photolithography process recipe of each test image includes: Obtaining a preset data range, a preset mathematical model, and a range of photolithography focal length and photolithography exposure energy in a photolithography process recipe of a focused exposure matrix; Based on a preset mathematical model, substituting the characteristic data of each test image and the photolithography process recipe corresponding to each test image into the preset mathematical model to determine a target fitting function; Determining a target coordinate system based on the range of the photolithography focal length and the photolithography exposure energy in each photolithography process recipe, wherein the target coordinate system is a coordinate system related to the photolithography focal length and the photolithography exposure energy; Based on the upper and lower limits of the preset data range, and according to the target fitting function, the characteristic data of each test image, and the photolithography process recipe corresponding to each test image, a first contour line and a second contour line are drawn, wherein the characteristic data of the test image corresponding to the point on the first contour line is equal to the upper limit of the preset data range, and the characteristic data of the test image corresponding to the point on the second contour line is equal to the lower limit of the preset data range; Based on the first contour line, the second contour line, and the target coordinate system, an area enclosed by the first contour line, the second contour line, and the target coordinate system is used as a second target area; A photolithography process window is determined according to the photolithography process recipe corresponding to each test image in the second target area.

10. The method for determining the photolithography process window according to claim 9, wherein: Determining the photolithography process window according to the second target area includes: Determine a maximum inscribed rectangle or a maximum inscribed ellipse within the target area according to the second target area; The area corresponding to the maximum inscribed rectangle or the maximum inscribed ellipse is used as the third target area; A photolithography process window is determined according to the photolithography process recipe corresponding to each test image in the third target area.

Images