Auxiliary graph generation method and device, equipment, medium and product

Abstract

The invention discloses an auxiliary graph generation method and device, equipment, a medium and a product, and relates to the technical field of semiconductor integrated circuits. The method comprises the following steps: acquiring a target pixel image, wherein the target pixel image is a pixel image formed by a graph area where a target mask graph of a to-be-generated auxiliary graph is located; determining a target area corresponding to the target mask pattern according to the target pixel image; extracting pixel points meeting a preset screening condition in the target area as seed points; and generating a target auxiliary pattern of the target mask pattern based on the seed points. According to the auxiliary pattern generation method provided by the invention, the imaging quality of photoetching can be improved.

Description

Technical Field

[0001] This application belongs to the technical field of semiconductor integrated circuits, and particularly relates to a method, apparatus, device, medium, and product for generating assist patterns. Background Art

[0002] With the rapid development of semiconductor technology, the feature size of chips has been continuously reduced, which poses higher requirements for lithography technology. As a result, assist patterns have been widely used in the semiconductor field. By adding assist patterns near the mask pattern, isolated lines and sparse patterns can also have the characteristics of dense patterns, thereby improving the light intensity distribution and the imaging quality of lithography.

[0003] Currently, based on the requirements of manufacturability rules (Mask Rule Check, MRC) and optical proximity correction (Optical Proximity Correction, OPC), the addition rules of assist patterns are determined. Then, based on the addition rules of assist patterns, assist patterns are arranged near the mask pattern.

[0004] However, as the process node continues to shrink, the size of the mask pattern is also continuously reduced, and the placement accuracy requirements for assist patterns are getting higher and higher. However, rule-based methods often fail to meet such high-precision requirements, which may lead to inaccurate positional relationships between assist patterns and mask patterns, resulting in poor imaging quality of lithography. Summary of the Invention

[0005] Embodiments of this application provide a method, apparatus, device, medium, and product for generating assist patterns, which can improve the imaging quality of lithography.

[0006] In the first aspect of the embodiments of this application, a method for generating assist patterns is provided, and the method includes:

[0007] Obtain a target pixel image, where the target pixel image is a pixel image formed by the graphic region where the target mask pattern for which the assist pattern is to be generated is located;

[0008] Determine a target region corresponding to the target mask pattern according to the target pixel image;

[0009] Extract pixel points in the target region that meet a preset screening condition as seed points;

[0010] Generate a target assist pattern for the target mask pattern based on the seed points.

[0011] In the second aspect of the embodiments of this application, an apparatus for generating assist patterns is provided, and the apparatus includes:

[0012] An image acquisition module, configured to acquire a target pixel image, where the target pixel image is a pixel image formed by a graphic region where a target mask graphic to be generated with an auxiliary graphic is located;

[0013] A region determination module, configured to determine a target region corresponding to the target mask graphic according to the target pixel image;

[0014] A seed point extraction module, configured to extract pixel points in the target region that meet a preset screening condition as seed points;

[0015] A graphic generation module, configured to generate a target auxiliary graphic of the target mask graphic based on the seed points.

[0016] In a third aspect of the embodiments of the present application, an electronic device is provided, and the device includes: a memory and a program or instruction stored on the memory and executable on a processor, where when the program or instruction is executed by the processor, it implements the method for generating an auxiliary graphic provided in any aspect of the embodiments of the present application as described above.

[0017] In a fourth aspect of the embodiments of the present application, a readable storage medium is provided, and a program or instruction is stored on the readable storage medium, where when the program or instruction is executed by the processor, it implements the method for generating an auxiliary graphic provided in any aspect of the embodiments of the present application as described above.

[0018] In a fifth aspect of the embodiments of the present application, a computer program product is provided, where when an instruction in the computer program product is executed by a processor of an electronic device, the electronic device is caused to execute the method for generating an auxiliary graphic provided in any aspect of the embodiments of the present application as described above.

[0019] In the method for generating an auxiliary graphic provided by the embodiments of the present application, a target pixel image formed by a graphic region where a target mask graphic is located is acquired, and pixels are used to represent the mask graphic. Then, a target region corresponding to the target mask graphic is determined from the target pixel image; thus, pixel points that can be used to generate an auxiliary graphic are extracted as seed points in the target region, and a target auxiliary graphic of the target mask graphic is generated based on the seed points, realizing pixel-level auxiliary graphic layout for the mask graphic. In this way, by using pixels to represent the mask graphic and performing layout placement of the auxiliary graphic with pixel-level precision, the present application can accurately determine the positional relationship between the auxiliary graphic and the mask graphic, thereby improving the imaging quality of lithography. Description of the Drawings

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0021] Figure 1 It is a schematic flowchart of a method for generating an auxiliary graph provided by an embodiment of the present application;

[0022] Figure 2 It is a schematic diagram of a target area provided by an embodiment of the present application;

[0023] Figure 3 It is a schematic diagram of a first target connection line provided by an embodiment of the present application;

[0024] Figure 4 It is a schematic diagram of Manhattanization processing provided by an embodiment of the present application;

[0025] Figure 5 It is a schematic diagram of a target auxiliary graph provided by an embodiment of the present application;

[0026] Figure 6 It is a schematic diagram of a second target connection line provided by an embodiment of the present application;

[0027] Figure 7 It is a schematic structural diagram of a device for generating an auxiliary graph provided by an embodiment of the present application;

[0028] Figure 8 It is a schematic structural diagram of a device for generating an auxiliary graph provided by an embodiment of the present application. Detailed implementation manners

[0029] The features and exemplary embodiments of various aspects of the present application will be described in detail below. To make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only intended to provide a better understanding of the present application by showing examples of the present application.

[0030] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the said element.

[0031] It should be noted that in the technical solution of this application, the acquisition, storage, use, processing, etc. of data all comply with the relevant provisions of national laws and regulations.

[0032] It should be noted that in the embodiments of this application, certain industry-existing solutions such as certain software, components, models, etc. may be mentioned, and they should be regarded as exemplary. The purpose is only to illustrate the feasibility in the implementation of the technical solution of this application, but it does not mean that the applicant has already or necessarily used this solution.

[0033] In the field of semiconductor manufacturing, as the chip feature size continues to shrink, lithography technology faces increasingly high requirements. In order to improve the light intensity distribution and enhance the lithography imaging quality, assist feature technology has been widely applied. Currently, the addition rules of assist features are determined based on the requirements of manufacturability rules and optical proximity effect correction, and assist features are laid out near the mask pattern accordingly. However, this rule-based method is difficult to meet the increasingly high precision requirements in the case of continuous shrinkage of the process node. Specifically, when the size of the mask pattern continuously decreases, the placement precision requirements of the assist features increase accordingly, but the rule-based method often fails to achieve such high precision requirements. This may lead to an inaccurate positional relationship between the assist features and the mask pattern, resulting in a decline in lithography imaging quality.

[0034] In a typical semiconductor manufacturing scenario, for example, chip design at the 7nm process node is underway. The design team needs to add auxiliary patterns in a complex logic circuit area to ensure the lithography quality of critical lines. Suppose the auxiliary patterns should be placed on both sides of the main pattern with a distance of 25nm. However, in the traditional rule-based method, due to errors in mask making and alignment processes, the actual position of the auxiliary patterns may deviate from the expected position by 2 - 3nm. Although this deviation seems small, it is sufficient to significantly affect the lithography imaging quality at the 7nm process node. Specifically, when the position of the auxiliary patterns shifts, it may cause uneven light intensity distribution in some areas, resulting in defects such as line width variation or bridging.

[0035] The purpose of this application is to provide a method, apparatus, device, medium, and product for generating auxiliary patterns. In the method for generating auxiliary patterns provided by the embodiments of this application, a target pixel image formed by the graphic area where the target mask pattern is located is obtained, and pixels are used to represent the mask pattern. Then, from the target pixel image, a target area corresponding to the target mask pattern is determined; thus, pixel points that can be used to generate auxiliary patterns are extracted as seed points in the target area, and a target auxiliary pattern for the target mask pattern is generated based on the seed points, realizing pixel-level auxiliary pattern layout for the mask pattern. In this way, by using pixels to represent the mask pattern and performing layout placement of the auxiliary pattern at the pixel-level accuracy, this application can accurately determine the positional relationship between the auxiliary pattern and the mask pattern, thereby improving the imaging quality of lithography.

[0036] The following introduces the specific embodiments of the method, apparatus, device, medium, and product for generating auxiliary patterns provided by the embodiments of this application. First, the method for generating auxiliary patterns is introduced below.

[0037] Figure 1 A schematic flowchart of a method for generating auxiliary patterns is provided. This method for generating auxiliary patterns can be applied to a server and may include the following S101 to S104.

[0038] S101, Obtain a target pixel image, where the target pixel image is a pixel image formed by the graphic area where the target mask pattern for which the auxiliary pattern is to be generated is located.

[0039] In this embodiment, the target pixel image refers to a pixel image formed by the target mask pattern for which the auxiliary pattern is to be generated and the surrounding area of the target mask pattern. Specifically, it can be achieved by using image processing technology to convert the target mask pattern and its surrounding area into a pixel-level representation.

[0040] Among them, this step is achieved by converting the target mask pattern and its surrounding area into a pixel-level representation. Specific operations can adopt image scanning or digital processing to discretize the continuous target mask pattern and its surrounding area into a pixel matrix. This step lays the foundation for subsequent precise analysis. Among them, the pixel value corresponding to each pixel point in the target pixel image is the average value of the pixel values of the red, green, and blue channels at that pixel point.

[0041] As an example, assume there is a chip design at the 7nm process node, which contains a complex logic circuit area. To generate appropriate auxiliary patterns, it is first necessary to obtain the target pixel image of this area. Specifically, the server can use a high-resolution scanner to scan the target mask pattern and its surrounding area into an image with a resolution of 1nm / pixel.

[0042] S102. Determine the target area corresponding to the target mask pattern according to the target pixel image.

[0043] In this embodiment, the target area refers to the area that can meet the distance range that should be maintained between the auxiliary pattern and the target mask pattern.

[0044] Among them, this step is achieved by delineating an area in the target pixel image, and this area maintains a specific distance relationship with the target mask pattern. Specific operations can adopt morphological operations such as image dilation or erosion to determine the possible range where the auxiliary pattern can be placed. The purpose of this step is to limit the generation range of the auxiliary pattern and ensure that an appropriate distance is maintained between the auxiliary pattern and the target mask pattern.

[0045] As an example, the server sets the minimum distance between the auxiliary pattern and the target mask pattern to 20nm and the maximum distance to 40nm. Using an image processing algorithm, the original mask pattern is expanded outward by 20nm and 40nm respectively to obtain two new contours. The area between these two contours is the target area.

[0046] S103. Extract the pixel points in the target area that meet the preset screening conditions as seed points.

[0047] In this embodiment, the seed points refer to the pixel points that meet the preset screening conditions, and specifically, a screening algorithm based on pixel values and pixel value gradient directions can be used to select these points.

[0048] Among them, this step can be achieved by analyzing the pixel point information of each pixel point in the target area. Specific operations can adopt calculating features such as pixel values and gradient directions, and select appropriate pixel points as seed points according to the preset screening conditions (such as pixel value thresholds and pixel value gradient direction ranges, etc.). The purpose of this step is to identify the most suitable positions for placing the auxiliary pattern.

[0049] In some embodiments, the preset screening condition is that the pixel value corresponding to the pixel point in the target area is greater than the preset pixel threshold, and the gradient direction satisfies the preset direction condition.

[0050] As an example, the pixel threshold is set to 128, and the gradient direction threshold is ±15° (taking the positive semi-axis of the vertical axis in the plane rectangular coordinate system constructed with the center point of the target mask pattern as the origin as 0°, and the clockwise direction as the positive direction). Each pixel point in the target area is traversed, and its corresponding pixel value and pixel value gradient direction are calculated. If the pixel value of a pixel point is greater than 128 and its pixel value gradient direction is within the range of ±15°, it is marked as a seed point.

[0051] S104, based on the seed points, generate the target auxiliary pattern of the target mask pattern.

[0052] In this embodiment, this step realizes the construction of the auxiliary pattern by connecting or expanding the seed points. Specific operations can adopt algorithms such as curve fitting and shape optimization to ensure that the generated auxiliary pattern meets the lithography requirements.

[0053] As an example, the server performs curve fitting on these seed points to generate a smooth auxiliary pattern contour, and then constructs a rectangular auxiliary pattern based on these auxiliary pattern contours.

[0054] In the method for generating the auxiliary pattern provided in this embodiment, the target pixel image formed by the graphic area where the target mask pattern is located is obtained, and pixels are used to represent the mask pattern. Then, in the target pixel image, the target area corresponding to the target mask pattern is determined; thus, the pixel points that can be used to generate the auxiliary pattern are extracted as seed points in the target area, and the target auxiliary pattern of the target mask pattern is generated based on the seed points, realizing the pixel-level auxiliary pattern layout of the mask pattern. In this way, by using pixels to represent the mask pattern and laying out the auxiliary pattern with pixel-level precision, the application can accurately determine the positional relationship between the auxiliary pattern and the mask pattern, thereby improving the imaging quality of lithography.

[0055] As an alternative embodiment, S102 may specifically include:

[0056] In the target pixel image, the outer peripheral annular belt area around the target mask pattern is determined as the target area corresponding to the target mask pattern.

[0057] In this embodiment, the outer peripheral annular belt area describes the specific shape characteristics of the target area in the target pixel image. It surrounds the target mask pattern and is distributed in an annular belt shape. The width of this annular belt depends on the distance range that should be maintained between the auxiliary pattern and the target mask pattern, and its function is to ensure that effective process control and optimization can be carried out in this area to meet the requirements of the lithography process.

[0058] As an example, the server first determines the distance range that should be maintained between the auxiliary pattern and the target mask pattern according to the requirements of the lithography process.

[0059] Then, based on the boundary of the target mask pattern, it is expanded outward based on the above distance range to form an annular region, which is the target region. In the implementation process, this annular region can be generated through an image processing algorithm (such as the dilation operation in morphological operations).

[0060] Through this embodiment, the target region can be accurately determined, and the distance between the auxiliary pattern and the target mask pattern can be better controlled. In the lithography process, the role of the auxiliary pattern is to improve the exposure effect of the photoresist and reduce lithography defects. A suitable distance range can ensure that the auxiliary pattern effectively plays its role, improve the resolution and quality of the lithography pattern, and thus improve the yield of chip manufacturing.

[0061] As an alternative embodiment, the annular region around the outer periphery of the target mask pattern is determined as the target region corresponding to the target mask pattern, which may specifically include:

[0062] The contours of the target mask pattern are respectively expanded outward by a first distance and a second distance to obtain the inner and outer contours of the target region;

[0063] According to the inner and outer contours of the target region, the target region corresponding to the target mask pattern is determined.

[0064] In this embodiment,

[0065] In this application, the first distance and the second distance define the minimum distance and the maximum distance between the mask pattern and its corresponding auxiliary pattern. This definition method provides a flexible range that can be adjusted according to specific manufacturing processes and design requirements. For example, for the 90nm process node, the first distance can be set to 50nm, and the second distance can be set to 200nm; while for the 45nm process node, the first distance can be adjusted to 30nm, and the second distance can be adjusted to 150nm.

[0066] The process of expanding the contours of the target mask pattern outward by the first distance and the second distance can be achieved in various ways. One possible implementation method is to use the dilation algorithm in morphological operations. Specifically, the target mask pattern can be dilated using two different-sized structuring elements to obtain the positions of the inner contour and the outer contour. Another possible implementation method is to use the distance transform algorithm to calculate the distance from each pixel point to the edge of the target mask pattern, and then determine the positions of the inner contour and the outer contour according to the first distance and the second distance thresholds.

[0067] The process of determining the target area based on the positions of the inner contour and the outer contour can be achieved by calculating the difference set between the two contours. Specifically, as Figure 2 shown, a schematic diagram of the target area is provided. Among them, the position of the inner contour 220 is obtained by expanding the contour of the target mask pattern 210 outward by a first distance, and the position of the outer contour 230 is obtained by expanding the contour of the target mask pattern 210 outward by a second distance. Subtracting the area surrounded by the position of the outer contour 230 from the area surrounded by the position of the inner contour 220 results in an annular area, which is the final target area.

[0068] As an example, the first distance and the second distance are dynamically adjusted according to the characteristic dimensions of the target mask pattern. For example, for a larger target mask pattern, a larger distance range can be used; while for a smaller target mask pattern, a smaller distance range can be used. This dynamic adjustment can further improve the accuracy and adaptability of the auxiliary pattern placement.

[0069] Suppose there is a rectangular target mask pattern with dimensions of 1μm × 2μm. The first distance is set to 100nm and the second distance is set to 300nm. First, the server uses the dilation algorithm to expand the target mask pattern outward by 100nm to obtain the inner contour, whose dimensions are approximately 1.2μm × 2.2μm. Then, the server expands the target mask pattern outward by 300nm again to obtain the outer contour, whose dimensions are approximately 1.6μm × 2.6μm. Finally, the server calculates the difference set between the inner contour and the outer contour to obtain an annular area with a width of 200nm, which is the final target area.

[0070] Through this application, the position of the auxiliary pattern can be precisely controlled to ensure that it maintains an appropriate distance from the target mask pattern. This not only improves the effectiveness of the auxiliary pattern but also reduces the risk that the auxiliary pattern may interfere with the target mask pattern. Compared with the traditional fixed-rule-based method, the method of this application is more flexible and precise, can better adapt to target mask patterns of different sizes and shapes, and thus can play a role in various complex chip designs.

[0071] As an optional embodiment, S103 may specifically include:

[0072] Based on the pixel positions of each pixel point in the target pixel image, candidate points located within the target area are screened out;

[0073] From each candidate point, seed points whose pixel values meet the preset pixel value threshold condition and whose pixel value gradient directions meet the preset direction condition are screened out.

[0074] In this embodiment, the preset screening conditions can be set based on three aspects: pixel position, pixel value, and pixel value gradient direction. These three types of information provide a multi-dimensional basis for the screening of seed points. The pixel position is used to determine whether a pixel is within the target area, while the pixel value and pixel value gradient direction are used to further screen seed points that meet specific conditions.

[0075] Specifically, based on the pixel positions of each pixel, candidate points located within the target area are screened. This step can be achieved by comparing the coordinates of the pixel with the boundary coordinates of the target area. For example, an annular area can be set as the target area, and then it is determined whether the x and y coordinates of each pixel are within this annular area. This can quickly exclude pixels that are not within the target area, greatly reducing the amount of data that needs to be processed subsequently.

[0076] From each candidate point, seed points are screened whose pixel values meet the preset pixel value threshold conditions and whose pixel value gradient directions meet the preset direction conditions. This step involves the judgment of two conditions:

[0077] First, the pixel value threshold condition: One or more pixel value ranges can be set, and only candidate points whose pixel values fall within these ranges can be selected as seed points. For example, candidate points with pixel values greater than 128 can be set as potential seed points.

[0078] Second, the pixel value gradient direction condition: The pixel value gradient direction reflects the trend of pixel value change. One or more direction ranges can be set, and only candidate points whose gradient directions fall within these ranges can be selected as seed points. For example, candidate points with gradient directions within the range of ±15° can be set as potential seed points.

[0079] Among them, as Figure 2 shown, the target area can be divided into various types of extraction frames, and the preset pixel value threshold conditions and preset direction conditions corresponding to different types of extraction frames can be different. Among them, the vertex expansion extraction frame 240 is obtained by expanding the minimum distance and the maximum distance along the diagonal direction outward from the vertex of the target mask pattern 210, and the edge expansion extraction frame 250 is obtained by expanding the minimum distance and the maximum distance perpendicular to the edge of the target mask pattern 210 outward.

[0080] Through this embodiment, seed points with specific brightness and edge features can be effectively screened out from a large number of pixels. These seed points represent important features or boundaries in the image. This screening method not only improves the quality of the seed points but also provides a better basis for the subsequent generation of auxiliary graphics.

[0081] As an alternative embodiment, S104 can specifically include:

[0082] According to the interval distances between different seed points, two seed points with an interval distance less than a first distance threshold are classified into the same seed point class, obtaining at least one seed point class;

[0083] From each seed point class, multiple characteristic seed points are selected;

[0084] According to the distribution positions of the characteristic seed points in the target mask pattern, adjacent characteristic seed points are sequentially connected to form a first target connection line;

[0085] Perform Manhattanization processing on the first target connection line to obtain a target processed line segment;

[0086] Based on the target processed line segment, a target auxiliary pattern of the target mask pattern is constructed.

[0087] In this embodiment, two seed points with an interval distance less than the first distance threshold belong to the same seed point class. For example, if the interval distance between seed point A and seed point B is less than the first distance threshold, then seed point A and seed point B are classified into the same seed point class; if the interval distance between seed point B and seed point C is also less than the first distance threshold, at this time, regardless of whether the interval distance between seed point A and seed point C is less than the first distance threshold, seed point C is classified into the seed point class of seed point A and seed point B.

[0088] Next, multiple characteristic seed points are selected from each seed point class. This step can be implemented using various strategies, such as selecting the centroid, boundary points of the seed point class, or selecting representative points at a certain sampling interval. The purpose of selecting characteristic seed points is to simplify subsequent processing and retain key information. For example, 3 to 10 characteristic seed points can be selected from each seed point class, and the specific number can be adjusted according to the size and shape of the seed point class.

[0089] Then, according to the distribution positions of the characteristic seed points in the target mask pattern, adjacent characteristic seed points are sequentially connected to form a first target connection line. This step converts discrete points into continuous lines, providing a framework for the generation of the auxiliary pattern. The connection process can use methods such as straight line connection or curve fitting.

[0090] As Figure 3 shown, a schematic diagram of a first target connection line is provided. Among them, if characteristic seed point 301 is adjacent to characteristic seed point 302, then characteristic seed point 301 and characteristic seed point 302 are connected; at the same time, if characteristic seed point 302 is also adjacent to characteristic seed point 303, then characteristic seed point 302 and characteristic seed point 303 are also connected; characteristic seed point 303 is also sequentially connected to adjacent characteristic seed point 304, and characteristic seed point 304 is sequentially connected to adjacent characteristic seed point 305, thereby forming a first target connection line.

[0091] Finally, perform Manhattanization on the first target connection line to obtain a target processed line segment; then, based on the target processed line segment, construct a target auxiliary pattern of the target mask pattern. Manhattanization can make the finally generated auxiliary pattern more regular, facilitating manufacturing and application.

[0092] Exemplarily, as Figure 4 shown, a schematic diagram of Manhattanization is provided. Among them, the specific operation of Manhattanization is to convert an oblique line segment into a combined line segment composed of horizontal and vertical line segments, that is, to obtain the target processed line segment. Specifically, the line segments formed between feature seed point 301 and feature seed point 302, between feature seed point 302 and feature seed point 303, between feature seed point 303 and feature seed point 304, and between feature seed point 304 and feature seed point 305 are all converted into horizontal or vertical line segments, thereby obtaining the target processed line segment 401 composed of horizontal and vertical line segments. In addition, the right-angle positions in Manhattanization are determined according to a preset minimum line segment length, that is, the finally obtained horizontal and vertical line segments are both greater than or equal to the preset minimum side length.

[0093] Furthermore, as Figure 5 shown, a schematic diagram of the target auxiliary pattern is provided. After performing Manhattanization on the first target connection line to obtain the target processed line segment 401 composed of horizontal and vertical line segments, then translate the target processed line segment 401 in opposite directions by a preset length respectively to obtain a translated line segment 501 and a translated line segment 502. Finally, connect the translated line segment 501 and the translated line segment 502 with horizontal and vertical line segments to form a closed pattern, that is, to obtain the target auxiliary pattern.

[0094] As an example, assume that in the chip design of a 90nm process node, it is necessary to generate an auxiliary pattern for a complex L-shaped mask pattern. First, the server extracts seed points from the target area around the mask pattern through image processing technology. The coordinate information of these seed points is recorded, and there are a total of 100 seed points.

[0095] Next, set the first distance threshold to 50nm, and the server classifies these 100 seed points to obtain 3 seed point classes. Then, feature seed points are respectively selected for each seed point class. The method adopted is to select a seed point as a feature seed point every 30nm along the distribution direction of the seed points in each seed point class. In this way, 5, 7, and 6 feature seed points are respectively selected in the three seed point classes.

[0096] Then, connect these characteristic seed points in sequence according to their distribution positions in the mask pattern, and use the cubic spline interpolation method to generate a smooth curve to form the first target connection line.

[0097] Finally, perform Manhattanization processing on each first target connection line. Specifically, the points on the first target connection line can be projected onto the nearest horizontal or vertical grid lines, and then these points are connected by horizontal line segments and vertical line segments. Finally, the target auxiliary pattern is formed based on the combined line segments of the horizontal line segments and vertical line segments. In this process, the minimum line segment length is set to 20 nm to avoid generating overly short line segments.

[0098] Through this embodiment, through the clustering, screening, connection, and regularization processing of the seed points, the conversion from discrete seed points to continuous and regular auxiliary patterns is achieved. This method can effectively generate auxiliary patterns adapted to the target mask pattern based on the seed points, improving the accuracy and practicality of the auxiliary patterns. The auxiliary patterns generated in this way can better meet the requirements of high-precision lithography technology, help improve the light intensity distribution, and improve the imaging quality of lithography.

[0099] As an alternative embodiment, multiple characteristic seed points are selected from each seed point class, specifically including:

[0100] For each seed point class, the following steps are respectively executed:

[0101] Based on the various seed points in the seed point class, determine the second target connection line;

[0102] On the second target connection line, sample the seed points to obtain characteristic seed points.

[0103] In this embodiment, first, determine the second target connection line based on the various seed points in the seed point class. This step can connect the scattered seed points into a continuous line, providing a basis for subsequent sampling. Specifically, various methods can be used to determine the second target connection line. For example, the minimum spanning tree algorithm can be used to connect all the seed points in the seed point class to form a shortest continuous path. Another method is to use the principal component analysis algorithm to find the main direction of the seed point distribution, and then connect the seed points along this direction.

[0104] As Figure 6 shown, a schematic diagram of the second target connection line is provided. Among them, the seed points 601, 602, 603, 604, 605, and 606 are connected in sequence, and the smooth curve formed is the second target connection line.

[0105] Then, sample the seed points on the second target connection line to obtain feature seed points. This step can effectively reduce the number of seed points while maintaining the distribution characteristics of the seed points, improving the efficiency of subsequent processing. There are various sampling methods available. For example, equidistant sampling can be used, that is, select a seed point as a feature seed point at a certain distance interval on the second target connection line. Adaptive sampling can also be used, dynamically adjusting the sampling interval according to the distribution density of the seed points on the connection line.

[0106] As an example, assume there is a seed point class containing 100 seed points distributed in a 100×100 pixel area. First, use the minimum spanning tree algorithm to determine the second target connection line, obtaining a continuous line segment with a total length of 500 pixels. Then, set the sampling interval to 50 pixels and perform equidistant sampling on this line segment. In this way, 10 feature seed points are finally obtained. These feature seed points can better represent the distribution characteristics of the original 100 seed points, while greatly reducing the data volume, facilitating subsequent processing.

[0107] Through this embodiment, representative feature seed points can be selected from a large number of seed points, retaining the distribution characteristics of the original seed points and reducing the complexity of data processing. This method can effectively solve the technical problem of how to select multiple feature seed points from the seed point class, providing a reliable basis for subsequent generation of the target auxiliary graph.

[0108] As an alternative embodiment, based on various sub-points in the seed point class, determine the second target connection line, which may specifically include:

[0109] Connect adjacent various sub-points in the seed point class in sequence according to the distribution positions of the seed points in the target mask pattern to form a candidate connection line, where the candidate connection line includes a main trunk line segment and branch line segments;

[0110] Break the branch line segments in the candidate connection line and determine the main trunk line segment in the candidate connection line as the second target connection line.

[0111] In this embodiment, first, connect adjacent various sub-points in the seed point class in sequence according to the distribution positions of the seed points in the target mask pattern to form a candidate connection line. This step can initially form the connection relationship between the seed points, but may include a main trunk line segment and branch line segments. Multiple methods can be used to generate the candidate connection line, such as the minimum spanning tree algorithm or the greedy algorithm.

[0112] Furthermore, the present application breaks the branch segments in the candidate connection lines. This step can effectively remove unnecessary branches and retain the main connection structure. The identification of branch segments can be based on various criteria, such as line segment length, connectivity, or angle, etc. As a preferred implementation, a threshold can be set, and line segments with a length less than the threshold are regarded as branch segments.

[0113] Finally, the present application determines the main trunk segments in the candidate connection lines as the second target connection lines. This step ensures that the finally obtained second target connection lines are the main trends of the seed point distribution, avoiding the possible interference caused by branch segments. The determination of the main trunk segments can be based on connectivity analysis, and the line segment connecting the most seed points is selected as the main trunk segment.

[0114] Through this embodiment, by using the method of identifying and removing branch segments and retaining the main trunk segments, the main distribution trend of the seed points can be more accurately reflected, thereby generating a more precise auxiliary graph. This improvement not only improves the quality of the auxiliary graph but also enhances the imaging quality in the subsequent lithography process.

[0115] As an alternative embodiment, on the second target connection lines, sampling is performed on the seed points to obtain characteristic seed points, including:

[0116] Among various seed points on the second target connection lines, any one seed point is selected as the sampling starting point;

[0117] When the sampling starting point is an endpoint of the second target connection line, starting from the sampling starting point, various seed points are sequentially traversed along the extension direction of the second target connection line to obtain each characteristic seed point that meets the preset interval condition;

[0118] When the sampling starting point is a non - endpoint of the second target connection line, starting from the sampling starting point, various seed points are sequentially traversed to both sides of the second target connection line to obtain each characteristic seed point that meets the preset interval condition.

[0119] In this embodiment, selecting any one seed point on the second target connection line as the sampling starting point is a key feature of the present application. This step provides a clear starting point for subsequent sampling, making the sampling process have a clear starting position. Selecting any one seed point as the starting point can increase the flexibility of sampling and adapt to different forms of the second target connection lines. For example, the endpoint, midpoint, or any other position of the seed point on the second target connection line can be selected as the starting point.

[0120] According to whether the sampling starting point is an endpoint of the second target connection line, the present application adopts different traversal methods. This feature takes into account the sampling requirements in different situations and improves the flexibility and applicability of sampling.

[0121] Such as Figure 6As shown, assume that the seed point 601 is determined as the sampling starting point. At this time, the sampling starting point is an endpoint, and then traverse sequentially along the extension direction of the second target connection line. This method is suitable for sampling starting from an endpoint, ensuring the continuity and integrity of sampling. Traversing from the endpoint can cover the entire second target connection line without missing any possible feature seed points.

[0122] Assume that the seed point 603 is determined as the sampling starting point. At this time, the sampling starting point is not an endpoint, and then traverse sequentially to both sides of the second target connection line. This method is suitable for sampling starting from a middle point, ensuring the comprehensiveness of sampling. Traversing to both sides can ensure that the seed points on both sides of the starting point are taken into account, avoiding one-sidedness of sampling.

[0123] By setting a preset interval condition, the density and uniformity of sampling can be controlled, so as to obtain more suitable feature seed points. The preset interval condition can be adjusted according to specific requirements. For example, it can be set as a fixed pixel distance or dynamically adjusted according to the length of the second target connection line. This flexible setting of the interval condition can adapt to second target connection lines of different sizes and shapes, ensuring the adaptability of sampling.

[0124] As an example, first, obtain all the seed points on the second target connection line and sort these seed points according to their positions on the second target connection line. For example, the seed points can be sorted in ascending order according to their horizontal coordinates or vertical coordinates (depending on the main direction of the second target connection line).

[0125] Secondly, select a seed point from the sorted list of seed points as the sampling starting point, and this sampling starting point belongs to the feature seed points. Here, the first seed point (endpoint), the last seed point (the other endpoint) or any middle seed point can be selected.

[0126] Next, determine whether the selected sampling starting point is an endpoint of the second target connection line. If it is an endpoint, start from this point and traverse all the seed points along the extension direction of the second target connection line. If it is not an endpoint, start from this point and traverse all the seed points to both sides of the second target connection line.

[0127] During the traversal process, whenever a seed point that meets the preset interval condition is encountered, mark it as a feature seed point. The preset interval condition can be set as: the distance between adjacent feature seed points is not less than 10 pixels. This distance can be adjusted according to actual needs.

[0128] Finally, collect all the points marked as feature seed points as the final sampling result.

[0129] Through this embodiment, by adopting a flexible sampling strategy and controllable interval conditions, the technical problem of sampling seed points on the second target connection line to obtain characteristic seed points is effectively solved, providing more reliable and high-quality basic data for subsequent auxiliary pattern generation.

[0130] As an alternative embodiment, the preset interval condition is that the distance between adjacent characteristic seed points satisfies any one of the following:

[0131] Both the first distance and the second distance between the characteristic seed points are not less than the second distance threshold. The first distance is the distance between the characteristic seed points in the first direction, and the second distance is the distance between the characteristic seed points in the second direction. The first direction is perpendicular to the second direction;

[0132] The maximum value of the first distance and the second distance between the characteristic seed points is not less than the second distance threshold, and the minimum value of the first distance and the second distance between the characteristic seed points is zero.

[0133] In this embodiment, the preset interval condition provides two options: one is to require that the distances of the characteristic seed points in two perpendicular directions are not less than the second distance threshold; the other is to allow the distance of the characteristic seed points in one direction to be zero, but the distance in the other direction is not less than the second distance threshold. This flexible condition setting can adapt to different auxiliary pattern layout requirements.

[0134] The preset interval condition proposed in this application can be implemented in various ways. For example, the second distance threshold can be dynamically adjusted according to the feature size of the mask pattern. In some embodiments, the second distance threshold can be set to be between 1 / 4 and 1 / 2 of the minimum feature size of the mask pattern. This can ensure that the generated auxiliary pattern will neither be too dense to interfere with the main pattern nor too sparse to lose the auxiliary effect.

[0135] As an example, first, a sampling starting point is selected on the second target connection line. This starting point can be the endpoint of the connection line or any point on the connection line. Selecting different starting points may result in slightly different sets of characteristic seed points finally obtained, but all can meet the preset interval condition.

[0136] Then, starting from the sampling starting point, all seed points are traversed along the second target connection line. The traversing direction depends on the position of the sampling starting point. If the starting point is the endpoint of the connection line, it is traversed along the extension direction of the connection line; if the starting point is a non-endpoint of the connection line, it is traversed to both sides of the connection line.

[0137] During the traversing process, each seed point is evaluated to check whether it meets the preset interval condition. Specifically, it is necessary to calculate the first distance and the second distance between the current seed point and the nearest selected characteristic seed point.

[0138] If both the first distance and the second distance are not less than the second distance threshold, or the maximum value of the first distance and the second distance is not less than the second distance threshold and the minimum value is zero, then the current seed point meets the preset interval condition and can be selected as a feature seed point.

[0139] If the current seed point does not meet the preset interval condition, then continue to traverse the next seed point until a seed point that meets the condition is found or all seed points have been traversed.

[0140] Through this embodiment, the distribution of feature seed points is precisely controlled, providing strong support for generating high-quality and high-precision auxiliary patterns. This method not only improves the quality of the auxiliary patterns, but also enhances the adaptability and robustness of the entire auxiliary pattern generation process, thus effectively solving the problem of the increasing requirement for the placement accuracy of auxiliary patterns with the reduction of process nodes.

[0141] As an alternative embodiment, S101 may specifically include:

[0142] Obtain a target imaging pattern, where the target imaging pattern is the chip pattern expected to be formed after lithography;

[0143] Perform inverse lithography operation on the target imaging pattern to obtain a target mask pattern;

[0144] Generate a target pixel image based on the target mask pattern.

[0145] In this embodiment, first, obtain the target imaging pattern. This step can be achieved in various ways, such as exporting the expected chip pattern from design software or obtaining an image by scanning an actual chip sample. The target imaging pattern represents the chip pattern expected to be formed after lithography and is the starting point of the entire process, laying the foundation for subsequent steps.

[0146] Secondly, perform an inverse lithography operation on the target imaging pattern. This is the core step in solving the problem and involves complex optical models and algorithms. The inverse lithography operation can be implemented by various algorithms, such as physical model-based methods or machine learning methods. The purpose of this step is to reverse-engineer the mask pattern that produced the imaging result.

[0147] Finally, generate a target pixel image based on the target mask pattern. This step converts the continuous mask pattern into a discrete pixel representation. Various interpolation algorithms, such as nearest neighbor interpolation, bilinear interpolation, or bicubic interpolation, can be used to ensure that the generated pixel image can accurately represent the characteristics of the mask pattern.

[0148] As an example, first, the server obtains a target imaging pattern: Use electronic design automation software to design the chip layout and export the expected chip pattern as the target imaging pattern. This pattern is usually a high-resolution bitmap file with a resolution of up to 1 nm / pixel.

[0149] Then, the server performs inverse lithography operations using a physical model-based inverse lithography algorithm. Specifically, first, an optical model of the lithography system is established, including parameters such as the light source wavelength, numerical aperture, and partial coherence factor. Then, through an iterative optimization algorithm, the mask pattern is gradually adjusted until the difference between the simulated imaging result and the target imaging pattern is less than a preset threshold.

[0150] Finally, the server generates a target pixel image: Convert the obtained mask pattern into a pixel image. Use the bicubic interpolation algorithm to sample the mask pattern into a raster image with a resolution of 10 nm / pixel. To ensure an accurate representation of the edges, sub-pixel gray values are used in the edge regions.

[0151] Through this embodiment, by reverse derivation, starting directly from the expected chip pattern, the required mask pattern and pixel image can be obtained more quickly and accurately. This not only improves the design efficiency but also better controls the final imaging quality, which is of great significance for improving the yield of chip manufacturing.

[0152] Based on the method for generating auxiliary patterns provided in this application. Correspondingly, this application also provides a specific embodiment of an apparatus for generating auxiliary patterns.

[0153] As Figure 7 shown, the apparatus 700 for generating auxiliary patterns provided in the embodiment of this application includes an image acquisition module 710, a region determination module 720, a seed point extraction module 730, and a pattern generation module 740.

[0154] The image acquisition module 710 is configured to acquire a target pixel image, where the target pixel image is a pixel image formed by the graphic region where the target mask pattern for generating the auxiliary pattern is located;

[0155] The region determination module 720 is configured to determine a target region corresponding to the target mask pattern according to the target pixel image;

[0156] The seed point extraction module 730 is configured to extract pixel points in the target region that meet a preset screening condition as seed points;

[0157] The pattern generation module 740 is configured to generate a target auxiliary pattern for the target mask pattern based on the seed points.

[0158] As an optional embodiment, the region determination module 720 is specifically configured to:

[0159] In the target pixel image, an outer peripheral annular region around the target mask pattern is determined as the target region corresponding to the target mask pattern.

[0160] As an alternative embodiment, the region determination module 720 is specifically configured to:

[0161] Expand the contours of the target mask pattern outward by a first distance and a second distance respectively to obtain the inner and outer contours of the target region;

[0162] Determine the target region corresponding to the target mask pattern according to the inner and outer contours of the target region.

[0163] As an alternative embodiment, the seed point extraction module 730 is specifically configured to:

[0164] Based on the pixel positions of the pixel points in the target pixel image, candidate points located within the target region are filtered out;

[0165] From each candidate point, seed points whose pixel values meet the preset pixel value threshold condition and whose pixel value gradient directions meet the preset direction condition are filtered out.

[0166] As an alternative embodiment, the graphic generation module 740 specifically includes the following units:

[0167] The seed point division unit is configured to divide two seed points with an interval distance less than a first distance threshold into the same seed point class according to the interval distance between different seed points, to obtain at least one seed point class;

[0168] The feature point selection unit is configured to select multiple feature seed points from each seed point class respectively;

[0169] The feature point connection unit is configured to connect adjacent feature seed points in sequence according to the distribution positions of the feature seed points in the target mask pattern to form a first target connection line;

[0170] The line segment processing unit is configured to perform Manhattanization processing on the first target connection line to obtain a target processed line segment;

[0171] The graphic generation unit is configured to construct a target auxiliary graphic of the target mask pattern based on the target processed line segment.

[0172] As an alternative embodiment, the feature point selection unit specifically includes the following sub-units:

[0173] The connection line determination sub-unit is configured to determine a second target connection line based on the sub-points in the seed point class;

[0174] The feature point sampling sub-unit is configured to sample seed points on the second target connection line to obtain feature seed points.

[0175] As an alternative embodiment, the connection determination subunit is specifically configured to:

[0176] According to the distribution positions of the seed points in the target mask pattern, various adjacent sub-points in the seed point class are sequentially connected to form a candidate connection line, and the candidate connection line includes a main line segment and branch line segments;

[0177] Break the branch line segments in the candidate connection line, and determine the main line segment in the candidate connection line as the second target connection line.

[0178] As an alternative embodiment, the feature point sampling subunit is specifically configured to:

[0179] Among various sub-points on the second target connection line, select any one seed point as the sampling starting point;

[0180] When the sampling starting point is an end point of the second target connection line, starting from the sampling starting point, sequentially traverse various sub-points along the extension direction of the second target connection line to obtain each feature seed point that meets the preset interval condition;

[0181] When the sampling starting point is a non-end point of the second target connection line, starting from the sampling starting point, sequentially traverse various sub-points on both sides of the second target connection line to obtain each feature seed point that meets the preset interval condition.

[0182] As an alternative embodiment, the image acquisition module 710 is specifically configured to:

[0183] Acquire a target imaging pattern, where the target imaging pattern is a chip pattern expected to be formed after lithography;

[0184] Perform reverse lithography operation on the target imaging pattern to obtain a target mask pattern;

[0185] Generate a target pixel image based on the target mask pattern.

[0186] Based on the method for generating the auxiliary pattern provided in this application. Correspondingly, this application also provides a specific embodiment of the device for generating the auxiliary pattern.

[0187] Figure 8 Shows a schematic hardware structure diagram of the device for generating the auxiliary pattern provided in the embodiment of this application.

[0188] The device for generating the auxiliary pattern may include a processor 801 and a memory 802 storing computer program instructions.

[0189] Specifically, the above-mentioned processor 801 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits for implementing the embodiments of the present application.

[0190] The memory 802 may include a mass storage for data or instructions. By way of example and not limitation, the memory 802 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. In a suitable case, the memory 802 may include a removable or non-removable (or fixed) medium. In a suitable case, the memory 802 may be internal or external to the integrated gateway disaster recovery device. In a specific embodiment, the memory 802 is a non-volatile solid state memory.

[0191] The processor 801 reads and executes computer program instructions stored in the memory 802 to implement any one of the auxiliary graph generation methods in the above embodiments.

[0192] In one example, the auxiliary graph generation device may further include a communication interface 803 and a bus 810. As shown, Figure 8 the processor 801, the memory 802, and the communication interface 803 are connected through the bus 810 and complete communication with each other.

[0193] The communication interface 803 is mainly used to implement communication between various modules, devices, units, and / or devices in the embodiments of the present application.

[0194] The bus 810 includes hardware, software, or both, and couples the components of the auxiliary graph generation device to each other. By way of example and not limitation, the bus may include an accelerated graphics port (AGP) or other graphics bus, an enhanced industry standard architecture (EISA) bus, a front side bus (FSB), a hypertransport (HT) interconnect, an industry standard architecture (ISA) bus, an infinite bandwidth interconnect, a low pin count (LPC) bus, a memory bus, a microchannel architecture (MCA) bus, a peripheral component interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a video electronics standards association local (VLB) bus, or other suitable buses, or a combination of two or more of these. In a suitable case, the bus 810 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0195] In addition, in combination with the method for generating auxiliary graphics in the above embodiments, an embodiment of the present application can be implemented by providing a computer storage medium. Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by a processor, any one of the methods for generating auxiliary graphics in the above embodiments is implemented.

[0196] In addition, in combination with the method for generating auxiliary graphics in the above embodiments, an embodiment of the present application can be implemented by providing a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is caused to execute the method for generating auxiliary graphics provided in any aspect of the above embodiments of the present application.

[0197] It should be clear that the present application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order between steps after understanding the spirit of the present application.

[0198] The functional blocks shown in the structural block diagrams described above can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, and so on. When implemented in software, the elements of the present application are programs or code segments for performing the required tasks. The program or code segment can be stored in a machine-readable medium or transmitted via a data signal carried in a carrier wave on a transmission medium or a communication link. A "machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical discs, hard disks, fiber optic media, radio frequency (RF) links, and so on. The code segment can be downloaded via a computer network such as the Internet, an intranet, and so on.

[0199] It should also be noted that the exemplary embodiments mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0200] As described above with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block in the flowchart and / or block diagram, and the combination of blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more blocks of the flowchart and / or block diagram. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field programmable logic circuit. It should also be understood that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can also be implemented by dedicated hardware that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0201] As described above, the foregoing is only a specific implementation manner of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein. It should be understood that the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present application.

Claims

1. A method for generating an auxiliary graphic, characterized in that: include: Acquire a target pixel image, where the target pixel image is a pixel image formed by a graphic region where a target mask graphic to be generated as an auxiliary graphic is located; Determining a target area corresponding to the target mask pattern according to the target pixel image; Extracting pixel points in the target area that meet preset screening conditions as seed points; Based on the seed point, a target auxiliary pattern of the target mask pattern is generated.

2. The method according to claim 1, characterized in that The step of determining the target area corresponding to the target mask pattern according to the target pixel image includes: In the target pixel image, an outer ring-shaped area surrounding the target mask pattern is determined as a target area corresponding to the target mask pattern.

3. The method according to claim 2, characterized in that The step of determining the outer peripheral annular zone surrounding the target mask pattern as the target area corresponding to the target mask pattern includes: Extending the contour of the target mask pattern outward by a first distance and a second distance respectively to obtain inner and outer contours of the target area; The target area corresponding to the target mask pattern is determined according to the inner and outer contours of the target area.

4. The method according to claim 1, characterized in that: The step of extracting pixel points in the target area that meet a preset screening condition as seed points includes: Based on the pixel point position of each pixel point in the target pixel image, screening out candidate points located in the target area; From the candidate points, the seed points whose pixel values ​​satisfy a preset pixel value threshold condition and whose pixel value gradient directions satisfy a preset direction condition are screened out.

5. The method according to any one of claims 1 to 4, characterized in that: The step of generating a target auxiliary pattern of the target mask pattern based on the seed point comprises: According to the interval distances between different seed points, two seed points whose interval distance is less than a first distance threshold are classified into the same seed point class to obtain at least one seed point class; Selecting a plurality of characteristic seed points from each of the seed point classes; According to the distribution positions of the feature seed points in the target mask pattern, adjacent feature seed points are sequentially connected to form a first target line; Performing Manhattanization processing on the first target line to obtain a target processing line segment; Based on the target processing line segment, a target auxiliary pattern for obtaining the target mask pattern is constructed.

6. The method according to claim 5, characterized in that The step of selecting a plurality of characteristic seed points from each of the seed point classes comprises: For each of the seed point classes, perform the following steps respectively: Determine a second target line based on each of the seed points in the seed point class; On the second target line, the seed points are sampled to obtain the feature seed points.

7. The method according to claim 6, characterized in that The determining of the second target line based on each of the seed points in the seed point class includes: According to the distribution positions of the seed points in the target mask pattern, the adjacent seed points in the seed point class are sequentially connected to form candidate links, wherein the candidate links include trunk segments and branch segments; The branch line segment in the candidate line is interrupted, and the trunk line segment in the candidate line is determined as the second target line.

8. The method according to claim 6, characterized in that The step of sampling the seed points on the second target line to obtain the feature seed points includes: Among the seed points on the second target line, select any one of the seed points as a sampling starting point; In the case where the sampling starting point is the endpoint of the second target line, taking the sampling starting point as the starting point, traversing the seed points in sequence along the extension direction of the second target line to obtain the feature seed points that meet the preset interval condition; In the case where the sampling starting point is a non-end point of the second target line, the sampling starting point is used as the starting point, and the seed points are traversed in sequence on both sides of the second target line to obtain the feature seed points that meet the preset interval conditions.

9. The method according to claim 8, characterized in that The preset spacing condition is that the distance between adjacent feature seed points satisfies any one of the following: The first distance and the second distance between the feature seed points are not less than a second distance threshold, the first distance is the distance between the feature seed points in a first direction, the second distance is the distance between the feature seed points in a second direction, and the first direction is perpendicular to the second direction; The maximum value of the first distance and the second distance between the feature seed points is not less than the second distance threshold, and the minimum value of the first distance and the second distance between the feature seed points is zero.

10. The method according to any one of claims 1 to 4, characterized in that: The step of obtaining a target pixel image comprises: Acquiring a target imaging pattern, wherein the target imaging pattern is a chip pattern expected to be formed after photolithography; Performing a reverse photolithography operation on the target imaging pattern to obtain the target mask pattern; Based on the target mask pattern, the target pixel image is generated.

11. An electronic device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for generating auxiliary graphics as described in any one of claims 1 to 10 is implemented.

Images