Defect processing method and device based on Hash calculation, equipment, medium and product

Abstract

The invention discloses a defect processing method, device and equipment based on Hash calculation, a medium and a product, and relates to the technical field of semiconductor integrated circuits. The method comprises the following steps: acquiring a plurality of target line segments in a design layout, wherein the distances between the target line segments and defect points meet a preset distance condition; respectively determining candidate hash points from a plurality of target line segments with different line segment directions; selecting a target hash point from the candidate hash points based on the position of each candidate hash point; according to the target hash point, calculating a defect hash value of the defect point; and according to the defect information and the defect hash value of the defect point, processing the defect point to obtain a defect point processing result. According to the defect processing method based on Hash calculation provided by the invention, the accuracy of the Hash value of the defect point can be improved.

Description

Technical Field

[0001] The present application belongs to the field of semiconductor integrated circuit technology, and in particular relates to a defect processing method, device, equipment, medium and product based on hash calculation. Background Art

[0002] With the rapid development of semiconductor technology, the size of integrated circuits continues to shrink, placing increasing demands on photolithography techniques. Optical proximity correction, a key pattern correction technology, adjusts the design pattern to overcome optical distortion during the photolithography process. In this process, photolithography rule checking plays a crucial role, identifying and classifying potential defects that may occur during the photolithography process.

[0003] Current lithography rule checking relies on interrupt settings in the design process. After calculating a hash value for each defect point, the importance level of the defect point is determined based on the hash value.

[0004] However, current lithography rule checking cannot effectively calculate the hash value of defect points without interrupting the line segments, which limits the deep mining of defect data and results in low accuracy of the hash value of the defect points. Summary of the Invention

[0005] The embodiments of the present application provide a defect processing method, apparatus, device, medium, and product based on hash calculation, which can improve the accuracy of the hash value of the defect point.

[0006] A first aspect of an embodiment of the present application provides a defect handling method based on hash calculation, the method comprising:

[0007] Acquire multiple target line segments in the design layout whose distances from the defect point meet preset distance conditions;

[0008] Determine candidate hash points from multiple target line segments with different line segment directions;

[0009] Selecting a target hash point from the candidate hash points based on the position of each candidate hash point;

[0010] Calculate the defect hash value of the defect point based on the target hash point;

[0011] According to the defect information and defect hash value of the defect point, the defect point is processed to obtain the defect point processing result.

[0012] According to a second aspect of an embodiment of the present application, a defect processing device based on hash calculation is provided, the device comprising:

[0013] A line segment acquisition module is used to acquire multiple target line segments in the design layout whose distances from the defect points meet preset distance conditions;

[0014] A hash point determination module is used to determine candidate hash points from multiple target line segments with different line segment directions;

[0015] A hash point selection module for selecting a target hash point from among the candidate hash points based on a position of each candidate hash point;

[0016] A hash value calculation module is used to calculate the defect hash value of the defect point based on the target hash point;

[0017] The defect point processing module is used to process the defect point according to the defect information and defect hash value of the defect point to obtain the defect point processing result.

[0018] According to a third aspect of an embodiment of the present application, an electronic device is provided, comprising: a memory and a program or instruction stored in the memory and executable on a processor, wherein when the program or instruction is executed by the processor, a defect handling method based on hash calculation as provided in any one of the above-mentioned embodiments of the present application is implemented.

[0019] In a fourth aspect of an embodiment of the present application, a readable storage medium is provided, on which a program or instruction is stored. When the program or instruction is executed by a processor, a defect processing method based on hash calculation as provided in any one aspect of the above-mentioned embodiment of the present application is implemented.

[0020] In a fifth aspect of the embodiments of the present application, a computer program product is provided. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the defect processing method based on hash calculation provided in any aspect of the above-mentioned embodiments of the present application.

[0021] In the defect processing method based on hash calculation provided by the embodiment of the present application, multiple target line segments whose distances from the defect point meet the preset distance conditions are first obtained in the design layout. In this way, the line segment to which the target hash point may belong can be quickly located. Then, based on the line segment direction of each target line segment, candidate hash points are determined from target line segments with different line segment directions. In this way, by comprehensively considering the different directions of the defect point, the comprehensiveness of the selected candidate hash points can be ensured. Then, based on the position of each candidate hash point, a target hash point is selected from the candidate hash points. In this way, based on the position of each candidate hash point, the target hash point that can be used to calculate the defect hash value is accurately screened out. Finally, based on the target hash point, the defect hash value of the defect point is calculated, thereby effectively processing the defect point. In this way, the present application does not need to rely on the interruption setting in the design process, and can accurately screen out the target hash point for calculating the defect hash value without interrupting the line segment, thereby directly calculating the hash value of the defect point, thereby improving the accuracy of the hash value of the defect point. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0023] Figure 1 This is a flowchart of a defect handling method based on hash calculation provided by an embodiment of the present application;

[0024] Figure 2 This is a schematic diagram of the principle of determining a target hash point provided by an embodiment of the present application;

[0025] Figure 3 This is a schematic diagram of the structure of a defect processing device based on hash calculation provided by an embodiment of the present application;

[0026] Figure 4 This is a structural diagram of a defect processing device based on hash calculation provided by an embodiment of the present application. DETAILED DESCRIPTION

[0027] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, 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 the need for some of these specific details. The following description of the embodiments is merely to provide a better understanding of the present application by illustrating the examples of the present application.

[0028] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, the elements defined by the phrase "comprising..." do not exclude the presence of other identical elements in the process, method, article, or device comprising the elements.

[0029] It should be noted that the acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0030] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned. They should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0031] With the rapid advancement of semiconductor technology, the size of integrated circuits continues to shrink, placing increasing demands on photolithography techniques. Optical proximity correction (OPC), a key pattern correction technology, adjusts the design pattern to overcome optical distortion during the photolithography process. In this process, lithography rule checking plays a crucial role, identifying and classifying potential defects that may occur during the photolithography process. However, current lithography rule checking requires line segment interruption; otherwise, the hash value calculation for the defect point cannot be effectively performed. This severely limits the in-depth analysis of defect data and results in low hash value accuracy for the defect point. This issue directly impacts the effectiveness and accuracy of lithography rule checking, and consequently, affects quality control of the entire photolithography process.

[0032] Specifically, in a typical integrated circuit manufacturing process, the design layout usually contains a large number of complex geometric figures and line segments. When performing lithography rule checking, the system needs to calculate a hash value for each potential defect point to determine its importance level. However, without line segment interruption, traditional hash value calculation methods have difficulty accurately capturing the geometric features around the defect point. For example, consider a complex layout area containing multiple intersecting line segments, in which there is a potential defect point. Because the line segments are not interrupted, the system cannot accurately locate the part of the line segment that is most relevant to the defect point, resulting in the hash value calculation result may not accurately reflect the actual importance of the defect point. In this case, the system may incorrectly classify a critical defect as a non-critical defect, or misjudge a non-critical defect as a critical defect. Therefore, developing a method that can accurately calculate the hash value of a defect point without relying on line segment interruption is of great significance to improving the accuracy of lithography rule checking and the overall quality of integrated circuit manufacturing.

[0033] The present application aims to provide a defect handling method, apparatus, device, medium, and product based on hash calculation. In the defect handling method based on hash calculation provided in the embodiments of the present application, multiple target line segments whose distances from the defect point meet preset distance conditions are first obtained from the design layout. This allows for the rapid location of the line segment to which the target hash point may belong. Then, based on the line segment direction of each target line segment, candidate hash points are determined from target line segments with different line segment directions. By comprehensively considering the different directions of the defect point, the comprehensiveness of the selected candidate hash points can be ensured. Then, based on the position of each candidate hash point, a target hash point is selected from the candidate hash points. Based on the position of each candidate hash point, target hash points that can be used to calculate the defect hash value are accurately selected. Finally, based on the target hash points, the defect hash value of the defect point is calculated, thereby effectively handling the defect point. This eliminates the need for interruption settings during the design process, allowing for the accurate selection of target hash points for defect hash value calculation without interrupting the line segments, allowing for direct hash value calculation of the defect point, thereby improving the accuracy of the defect point's hash value.

[0034] The following describes specific embodiments of the defect handling method, apparatus, device, medium, and product based on hash calculation provided by the embodiments of the present application. The following first introduces the defect handling method based on hash calculation.

[0035] Figure 1 A flowchart of a defect handling method based on hash calculation is provided. The defect handling method based on hash calculation can be applied to a server side. The defect handling method based on hash calculation can include the following S101 to S105.

[0036] S101, obtaining a plurality of target line segments in a design layout whose distances from defect points meet a preset distance condition.

[0037] In this embodiment, the design layout is used to represent the graphical representation generated during the integrated circuit design process, which includes various geometric shapes and layout information of the circuit. It is worth noting that the design layout processed in this application may include a layout after optical proximity correction (OPC). Specifically, the design layout can be created and edited using computer-aided design software to meet the sophistication and high performance requirements of integrated circuit design.

[0038] Defect points refer to locations in the design layout that may cause manufacturing problems or performance anomalies, and can be identified and located using lithography rule checking tools.

[0039] The preset distance condition can be that the distance between the target line segment and the defect point is less than a certain threshold. The target line segment can be specifically searched by creating a search area, and the size of the search area can be adjusted according to the specific application scenario. For example, the search area is set to a circular area centered on the defect point, and when determining the radius of the search area, it is necessary to combine the characteristics of the design layout and the size of the defect point. Specifically, the radius of the search area can be set in the range of 1 micron to 10 microns. When the design layout features a high-density wiring area (for example, the line width and spacing are both less than 1 micron), and the defect point is a local line width anomaly (for example, the anomaly width is 0.5 microns), the radius of the circular area can be set to 3 microns to cover the surrounding areas that may be affected; if the design layout features a low-density module (for example, the line width and spacing are both greater than 5 microns), and the defect point is a large area of ​​metal layer missing (for example, the missing area diameter is 2 microns), the radius can be expanded to 8 microns to ensure comprehensive detection of potential related areas.

[0040] As an example, Figure 2 As shown, a schematic diagram of the principle of determining the target hash point is provided. In a typical integrated circuit design layout, assume that there is a potential defect point 210. The server defines a circular area with a radius of 8 microns as the first search area 220, with the defect point 210 as the center.

[0041] Then, all line segments are identified within the first search area 220, such as Figure 2 It can be seen that the first search area 220 includes 4 line segments in total, and these 4 line segments are determined as target line segments.

[0042] S102, determining candidate hash points respectively from a plurality of target line segments having different line segment directions.

[0043] In this embodiment, candidate hash points refer to points selected on the target line segment that may be used for hash calculation. Specifically, a point selection strategy based on line segment direction can be used to determine the candidate hash points. For example, the endpoint or midpoint closest to the defect point can be selected on target line segments with different line segment directions as candidate hash points.

[0044] As an example, Figure 2 As shown, it is assumed that among the four target line segments selected above, the line segment direction of target line segment 230 is horizontally to the right, the line segment direction of target line segment 250 is horizontally to the left, the line segment direction of target line segment 240 is vertically upward, and the line segment direction of target line segment 260 is vertically downward.

[0045] For each target line segment direction, the server selects the point closest to the defect point 210 as a candidate hash point. Figure 2In the figure, perpendicular lines are drawn from the defect point 210 to the target line segment 230 , the target line segment 240 , the target line segment 250 , and the target line segment 260 , respectively, to obtain the candidate hash point 231 , the candidate hash point 241 , the candidate hash point 251 , and the candidate hash point 261 .

[0046] S103: Select a target hash point from the candidate hash points based on the position of each candidate hash point.

[0047] In this embodiment, the target hash point refers to a point that is finally determined to be able to accurately perform hash calculation.

[0048] As an example, the server selects the most representative point as the target hash point based on the positions of the candidate hash point 231 , the candidate hash point 241 , the candidate hash point 251 , and the candidate hash point 261 selected above.

[0049] For example, the distance between each pair of candidate hash points in candidate hash point 231, candidate hash point 241, candidate hash point 251, and candidate hash point 261 can be calculated, that is, the distance between candidate hash point 231 and candidate hash point 241, the distance between candidate hash point 231 and candidate hash point 251, the distance between candidate hash point 231 and candidate hash point 261, the distance between candidate hash point 241 and candidate hash point 251, the distance between candidate hash point 241 and candidate hash point 261, and the distance between candidate hash point 251 and candidate hash point 261 are calculated, and the two candidate hash points with the closest distance are selected as the target hash points. For example, if the calculation result shows that the distance between candidate hash point 251 and candidate hash point 261 is the smallest among all the calculation results, then these two points will be selected as the target hash points. Therefore, candidate hash point 251 and candidate hash point 261 are finally selected as the target hash points.

[0050] S104, calculating the defect hash value of the defect point according to the target hash point.

[0051] In this embodiment, the defect hash value refers to a numerical value used to characterize the characteristics of the defect point, and can be generated using a calculation method based on the target hash point.

[0052] As an example, the server calculates the target hash value of each target hash point based on the target hash points determined above (ie, the target hash points 251 and 261 selected by calculating the distance) and the line segment information around these target hash points.

[0053] For example, for target hash point 251, the server examines the surrounding line segment information, which can include features such as the segment's length, direction, and connections with other segments. These line segment features are quantified (for example, by normalizing the segment length and encoding the direction as an angle), resulting in a set of numerical values. These values ​​are then processed using a specific hash algorithm to generate the target hash value for target hash point 251. Similarly, for target hash point 261, the same approach is employed: quantizing the surrounding line segment information and then using a hash algorithm to calculate its target hash value.

[0054] Finally, the target hash value of the target hash point 251 and the target hash value of the target hash point 261 are accumulated to obtain the defect hash value of the defect point.

[0055] S105, processing the defect point according to the defect information and the defect hash value of the defect point to obtain a defect point processing result.

[0056] In this embodiment, the defect information is used to characterize the basic attributes of the defect point. For example, the defect information may include defect type, defect size, etc.

[0057] As an example, the server classifies and stores the defect points according to their importance based on their defect information and defect hash values, thereby facilitating subsequent corresponding processing based on the importance of the defect points.

[0058] In the defect processing method based on hash calculation provided by this embodiment, multiple target line segments whose distances from the defect point meet preset distance conditions are first obtained in the design layout. In this way, the line segment to which the target hash point may belong can be quickly located. Then, based on the line segment direction of each target line segment, candidate hash points are determined from target line segments with different line segment directions. In this way, by comprehensively considering the different directions of the defect point, the comprehensiveness of the selected candidate hash points can be ensured. Then, based on the position of each candidate hash point, a target hash point is selected from the candidate hash points. In this way, based on the position of each candidate hash point, the target hash point that can be used to calculate the defect hash value is accurately screened out. Finally, based on the target hash point, the defect hash value of the defect point is calculated, thereby effectively processing the defect point. In this way, the present application does not need to rely on the interruption setting in the design process, and can accurately screen out the target hash point for calculating the defect hash value without interrupting the line segment, thereby directly calculating the hash value of the defect point, thereby improving the accuracy of the hash value of the defect point.

[0059] As an optional embodiment, S102 may specifically include:

[0060] According to the segment direction of each target segment, target segments in opposite directions are divided into one group to obtain multiple segment groups;

[0061] Draw a perpendicular line from the defect point to the straight line where each target line segment of each line segment group is located, and take the intersection of the straight line where each target line segment is located and the perpendicular line as the line feature point of the target line segment;

[0062] According to the positional relationship between each line segment feature point and the target line segment, the candidate hash points of the target line segment are determined.

[0063] In this embodiment, the line segment group includes two target line segments, and the line segment directions of the two target line segments are opposite.

[0064] The positional relationship between the line segment feature point and the target line segment can be of two types: type 1 and type 2. If the positional relationship between the line segment feature point and the target line segment is of type 1, it means that the intersection point of the perpendicular line from the defect point to the line where the target line segment is located is on the target line segment. If the positional relationship between the line segment feature point and the target line segment is of type 2, it means that the intersection point of the perpendicular line from the defect point to the line where the target line segment is located is not on the target line segment.

[0065] As an example, the server first groups target segments in opposite directions into a group based on their direction, resulting in multiple segment groups. This grouping method helps to classify segments in different directions and facilitates the subsequent targeted determination of candidate hash points.

[0066] For example, the target line segments going horizontally to the right and horizontally to the left can be divided into one group, and the line segments going vertically upward and vertically downward can be divided into another group. Figure 2 As shown, the target line segment 230 and the target line segment 250 belong to the same line segment group, and the target line segment 240 and the target line segment 260 belong to the same line segment group.

[0067] Next, for each target segment in the segment group, a perpendicular line is drawn from the defect point to the line containing the target segment to obtain the target segment's feature point. A feature point is the intersection of the target segment's line and the perpendicular line. This step determines the shortest distance between the defect point and the target segment, which helps determine the location of the feature point later.

[0068] Furthermore, based on the positional relationship between the segment feature point and the target segment, a candidate hash point is determined on each target segment in the segment group. The positional relationship between the segment feature point and the target segment includes a first type and a second type, where the first type indicates that the segment feature point is on the target segment, and the second type indicates that the segment feature point is not on the target segment. By distinguishing whether the segment feature point is on the target segment, different strategies can be used to determine candidate hash points.

[0069] For example, if the positional relationship between the line segment feature point and the target line segment is of the second type, the line segment feature point is directly determined as a candidate hash point; if the positional relationship between the line segment feature point and the target line segment is of the first type, the endpoint on the target line segment that is closer to the defect point is determined as a candidate hash point.

[0070] This embodiment groups target line segments by direction, uses the perpendicular line from the defect point to the target line segment to determine the line segment feature points, and then determines candidate hash points based on the position type of the line segment feature points. This takes into account the direction of the target line segment and the relative position of the line segment feature points, more accurately reflecting the distribution of line segments around the defect point, thus providing a more reliable basis for subsequent hash value calculation.

[0071] As an optional embodiment, determining the candidate hash points of the target line segment based on the positional relationship between each line segment feature point and the target line segment may specifically include:

[0072] When each line segment feature point in the line segment group is on the target line segment, each line segment feature point is offset according to its position on the corresponding target line segment to obtain a candidate hash point;

[0073] In the case that at least one line segment feature point in the line segment group is not on the target line segment, each line segment feature point is determined as a candidate hash point.

[0074] In this embodiment, as an example, when the positional relationship between each line segment feature point in the line segment group and the target line segment belongs to the first type, the server side selects the line segment endpoint corresponding to the smallest endpoint distance for each target line segment as the reference point, and then offsets the reference point according to the preset offset distance to obtain the position of the candidate hash point. The endpoint distance is used to represent the distance between the line segment feature point and the line segment endpoint on the target line segment where the line segment feature point is located. When the positional relationship between at least one line segment feature point in the line segment group and the target line segment belongs to the second type, the server side directly determines each line segment feature point in the line segment group as a candidate hash point.

[0075] This embodiment distinguishes different situations when candidate hash points are determined, improving the accuracy and flexibility of hash value calculation. When all line segment feature points are on the target line segment, the position of the candidate hash points is optimized through offset calculation, improving the accuracy of the candidate hash points. When a line segment feature point is not on the target line segment, the line segment feature point is directly used as the candidate hash point, simplifying the processing and better reflecting the actual relationship between the defect point and the target line segment.

[0076] As an optional embodiment, each line segment feature point is offset according to its position on the corresponding target line segment to obtain a candidate hash point, which may specifically include:

[0077] Obtain the projected overlapping line segments between each target line segment in the line segment group along the direction perpendicular to the target line segment;

[0078] The endpoint closest to the feature point of each projected overlapping line segment is determined as the target endpoint;

[0079] The offset is determined based on each target endpoint and the preset tolerance, and the line segment feature points are offset according to the offset to obtain candidate hash points.

[0080] In this embodiment, the target endpoint is the reference point when offsetting the line segment feature point, and the preset tolerance is used to control the offset range when offsetting the line segment feature point. For example, the preset tolerance can be set to 5% of the line segment length or 10% of the line segment length.

[0081] As an example, for a given line segment group, the server first determines whether all line segment feature points are on the target line segment.

[0082] If all segment feature points in a segment group lie on the target segment, a projection operation is performed on the target segments in the segment group to determine whether they overlap on the projection plane. Specifically, the projection intervals of the two target segments along a direction perpendicular to the target segments are calculated and checked for intersection. If the projection intervals of the two segments intersect, they overlap in that projection direction. These overlapping segments are recorded as projected overlapping segments.

[0083] Then, for each projected overlapping line segment, the distances between its two endpoints and the line segment feature point are calculated, and the endpoint closer to the line segment feature point is selected as the target endpoint.

[0084] Then, based on the target endpoint and a preset tolerance, the line segment feature point is offset. Specifically, the vector from the line segment feature point to the target endpoint is calculated. This vector is then normalized and multiplied by the preset tolerance to obtain the offset vector. Finally, the line segment feature point is shifted along the offset vector to obtain a candidate hash point.

[0085] This embodiment introduces a target endpoint and a preset tolerance, providing a more flexible and adaptive offset strategy. This offset strategy can be adjusted based on the actual situation of the line segment, thereby generating more reasonable and consistent candidate hash points.

[0086] As an optional embodiment, S103 may specifically include:

[0087] Get the hash distance between each candidate hash point in each segment group;

[0088] The two candidate hash points with the smallest hash distance are determined as the target hash points.

[0089] In this embodiment, the hash distance is used to represent the distance between two candidate hash points in a line segment group.

[0090] As an example, first, for each segment group, based on the position of each candidate hash point in the segment group, the hash distance between the candidate hash points in the segment group is determined. Specifically, this step can be implemented in a variety of ways, such as: 1. Using Euclidean distance to calculate the straight-line distance between two points. 2. Using Manhattan distance to calculate the sum of the distances between two points on the x-axis and y-axis. 3. Taking into account the characteristics of the layout, using weighted distance calculation to assign different weights to the distances in the x-axis and y-axis directions.

[0091] Then, the hash distances corresponding to each line segment group are sorted, and the two candidate hash points corresponding to the smallest hash distances are selected as target hash points.

[0092] This embodiment effectively solves the problem of target hash point selection by calculating the distance between candidate hash points and selecting the two closest candidate hash points as the target hash points. Selecting the two closest candidate hash points as the target hash points ensures that the target hash points best represent the graphical features surrounding the defect, thereby improving the accuracy and reliability of subsequent hash value calculations.

[0093] As an optional embodiment, S104 may specifically include:

[0094] For each target hash point, determining a target hash value of the target hash point based on line segments in the design layout that are within a first search area of ​​the target hash point;

[0095] The target hash value of each target hash point is accumulated to obtain the defect hash value of the defect point.

[0096] In this embodiment, the target hash value is used to represent a numerical value representing a local feature of a defect point calculated based on a single target hash point.

[0097] The defect hash value is used to represent the numerical value representing the overall characteristics of the defect point calculated based on all target hash points.

[0098] The size of the first search area can be adjusted according to the specific application scenario. For example, the first search area can be set to a circular area centered on the target hash point, and the radius can be determined according to the characteristics of the design layout and the size of the defect point.

[0099] When determining the target hash value, various factors can be considered. For example, the total length, directional distribution, and density of line segments within the first search area can be statistically analyzed. Specifically, the search area can be divided into multiple sectors, and the sum of the lengths of the line segments within each sector can be calculated. These values ​​can then be combined into a vector as the target hash value. This method can effectively capture the geometric distribution characteristics around the target hash point.

[0100] When accumulating target hash values, you can use simple numerical addition or more complex combinations. For example, you can assign different weights to the target hash values ​​of different target hash points. The weights can be determined based on the distance between the target hash point and the defect point, with closer target hash points receiving larger weights. This can better reflect the geometric characteristics near the defect point.

[0101] As an example, for each target hash point, we define a circular first search area with a radius of 30 units. Within this search area, we divide 360 ​​degrees into 8 sectors, each sector covering 45 degrees.

[0102] For one target hash point, we count the total length of each sector within its first search area. Assume the result is [100, 150, 80, 200, 120, 90, 180, 110]. This 8-dimensional vector serves as the target hash value for that target hash point. Repeat the same process for the other target hash point, assuming the resulting target hash values ​​are [120, 130, 100, 180, 140, 110, 160, 130].

[0103] Then, we add these two 8-dimensional vectors at the element level to obtain the final defect hash value of the defect point.

[0104] This embodiment does not rely on line segment interruption. Instead, it selects multiple target hash points and considers the line segment characteristics surrounding each point, ultimately accumulating a comprehensive defect hash value. This can better adapt to design layouts without line segment interruption, improving the flexibility and applicability of defect point hash value calculation.

[0105] As an optional embodiment, for each target hash point, before determining a target hash value of the target hash point based on line segments within a first search area of ​​the target hash point in the design layout, the defect handling method based on hash calculation further includes:

[0106] Marking the line segments in the first search area of ​​the target hash point in the design layout as relevant line segments, and marking the line segments outside the first search area of ​​the target hash point in the design layout as non-relevant line segments, to obtain a marked layout;

[0107] For each target hash point, determining a target hash value of the target hash point based on line segments in the design layout that are within a first search region of the target hash point may specifically include:

[0108] Based on the correlation line segment of each target hash point in the marked layout, the target hash value of each target hash point is determined respectively.

[0109] In this embodiment, the correlation line segment is used to represent the line segment located within the first search area of ​​the target hash point, and the non-correlation line segment is used to represent the line segment located outside the first search area of ​​the target hash point.

[0110] As an example, first, the line segments in the design layout that are within the first search area of ​​the target hash point are marked as relevant line segments. Specifically, this step can be implemented in a variety of ways, such as using Boolean operations to determine whether the line segment is within the first search area, or using a spatial index structure to quickly locate relevant line segments.

[0111] Secondly, the line segments in the design layout that are outside the first search area of ​​the target hash point are marked as non-relevant line segments. This step can be achieved by elimination, that is, all line segments that are not marked as relevant line segments are marked as non-relevant line segments.

[0112] The marked design layout is then determined as a marked layout. Specifically, the marked layout can be implemented by adding an identifier to each line segment, such as using 1 to represent a relevant line segment and 0 to represent an irrelevant line segment; or the relevant line segments can be left unprocessed and the irrelevant line segments can be directly truncated.

[0113] Finally, for each target hash point, the target hash value of the target hash point is determined based on the relevant line segments in the marked layout. This step can be achieved by only processing the line segments marked as relevant, which greatly reduces the amount of calculation.

[0114] This embodiment marks line segments as relevant or irrelevant, further refining the data screening process. This marking method not only improves data organization efficiency, but also provides clear processing objects for subsequent hash value calculations, thereby greatly improving calculation efficiency.

[0115] As an optional embodiment, determining the target hash value of each target hash point based on the correlation line segment of each target hash point in the marked layout may specifically include:

[0116] Based on the reference coordinate system, coordinate transformation is performed on the first endpoint coordinates of each correlation line segment of the target hash point to obtain the second endpoint coordinates of each correlation line segment, where the first endpoint coordinates are the coordinates of the segment endpoint of the correlation line segment in the layout coordinate system of the marked layout, and the reference coordinate system is a plane rectangular coordinate system constructed with the target hash point as the origin and the segment direction of the target line segment of the target hash point as the positive direction of the longitudinal axis;

[0117] Based on the vertical axis of the reference coordinate system, mirror transformation is performed on each second endpoint coordinate to obtain each third endpoint coordinate;

[0118] Based on each second endpoint coordinate and each third endpoint coordinate, a target hash value of the target hash point is determined.

[0119] In this embodiment, the first endpoint coordinates are used to represent the coordinates of the endpoint of the correlation line segment in the layout coordinate system of the marked layout, the second endpoint coordinates are used to represent the coordinates of the endpoint of the correlation line segment in the reference coordinate system, and the third endpoint coordinates are used to represent the endpoint coordinates obtained after mirroring the second endpoint coordinates of the correlation line segment.

[0120] The introduction of the reference coordinate system provides the foundation for subsequent coordinate transformations and mirroring. Coordinate transformation transforms the first endpoint coordinates of the dependency line segment in the layout coordinate system to the second endpoint coordinates in the reference coordinate system. This step unifies all dependency line segments into a coordinate system centered on the target hash point, facilitating subsequent processing.

[0121] The mirror transformation mirrors the coordinates of the second endpoint based on the vertical axis of the reference coordinate system to obtain the coordinates of the third endpoint. This step can capture the symmetry information of the line segment on both sides of the target hash point.

[0122] Finally, the target hash value is calculated based on the second endpoint coordinates and the third endpoint coordinates, combining the previously obtained coordinate information to generate a hash value that can characterize the distribution characteristics of the line segments around the target hash point.

[0123] As an example, Figure 2 As shown, assuming that candidate hash points 231 and candidate hash points 251 are ultimately determined as target hash points, target hash points 231 and 251 are used as origins (0, 0), respectively. A reference coordinate system is constructed with the direction of target line segment 230 where target hash point 231 is located as the positive y-axis direction; another reference coordinate system is constructed with the direction of target line segment 250 where target hash point 251 is located as the positive y-axis direction.

[0124] Then, for each target hash point's related line segment, its endpoint coordinates are transformed from the layout coordinate system to the reference coordinate system using a matrix transformation, where the transformation matrix is ​​determined based on the rotation angle and translation between the two coordinate systems.

[0125] Then, after the coordinate transformation, a mirror transformation is performed. Specifically, this can be achieved by simply inverting the x coordinate, that is, (x, y) becomes (-x, y). In this way, the coordinates of the third endpoint of each correlation line segment can be obtained.

[0126] Finally, the target hash value is calculated using these transformed coordinates. For example, the x and y coordinates of all second and third endpoints can be summed up, and then these sums can be converted into a unique hash value using a target algorithm (such as a hash function).

[0127] By introducing a reference coordinate system and performing coordinate transformation in this embodiment, the information of all relevant line segments can be unified into a standardized coordinate system. In this way, regardless of the orientation of the line segments in the marked layout, they can be compared and analyzed in this unified coordinate system. At the same time, the introduction of mirror transformation can take into account the symmetry of the line segments on both sides of the target hash point, thereby improving the accuracy of the hash value calculation.

[0128] As an optional embodiment, the defect information includes defect type and defect size;

[0129] S105 may specifically include:

[0130] Determine the key defect conditions of the defect point based on the defect type of the defect point;

[0131] If the defect size and defect hash value of the defect point meet the critical defect conditions, the defect level of the defect point is determined to be a critical defect;

[0132] If the defect size and defect hash value of the defect point do not meet the critical defect condition, the defect level of the defect point is determined to be a non-critical defect.

[0133] In this embodiment, defect types may include short circuits, open circuits, and bridges. For each defect type, corresponding critical defect conditions may be defined based on its characteristics and potential impact. For example, for a short circuit defect, the critical defect conditions may include the defect size exceeding a certain threshold and the defect hash value being less than a specific value.

[0134] Categorizing defects into critical and non-critical levels helps prioritize important defects and improves processing efficiency. Critical defects can be repaired immediately or analyzed further, while non-critical defects can be addressed during subsequent optimization or, in some cases, tolerated.

[0135] As an example, the server first determines the critical defect condition for the defect point based on the defect type. Then, if the defect size and defect hash value of the defect point meet the critical defect condition, the defect point is classified as a critical defect. Conversely, if the defect size and defect hash value of the defect point do not meet the critical defect condition, the defect point is classified as a non-critical defect.

[0136] For example, suppose there's a short-circuit defect with a size of 15nm and a calculated defect hash value of 80. The default critical defect criteria are: a short-circuit defect with a size greater than 10nm and a hash value less than 100 is considered a critical defect. In this case, the system identifies the defect as critical and prioritizes its handling.

[0137] This embodiment comprehensively considers the defect type, size, and surrounding environmental characteristics, establishing a more comprehensive and accurate defect assessment mechanism. By setting key defect conditions and comparing the defect size and hash value against them, the importance of defects can be more objectively determined. This approach considers not only the characteristics of the defect itself but also the environment in which the defect occurs, enabling more accurate identification and classification of defect points, improving the accuracy and efficiency of defect handling.

[0138] As an optional embodiment, S101 may specifically include:

[0139] Determine each line segment in the second search area located at the defect point in the design layout as a candidate line segment;

[0140] Classify each candidate line segment according to its line segment direction to obtain multiple candidate line segment sets;

[0141] The candidate line segment with the smallest distance to the defect point in each candidate line segment set is determined as the target line segment.

[0142] In this embodiment, the candidate line segment set includes multiple candidate line segments belonging to the same line segment direction.

[0143] The size of the second search area may be the same as or different from the size of the first search area.

[0144] As an example, the server first identifies each line segment in the design layout that falls within the second search area as a candidate line segment, effectively narrowing the search scope. This step avoids a comprehensive search of the entire design layout, significantly improving processing efficiency. For example, if the design layout contains hundreds of line segments, but the second search area only contains dozens of line segments, this step can reduce the number of line segments to be processed by several orders of magnitude.

[0145] Next, each candidate line segment is classified according to its direction, resulting in multiple candidate line segment sets. This step is important because it provides structured data for subsequent target line segment selection. Direction classification enables better processing of line segments with different directions. For example, in a typical integrated circuit design, there may be horizontal, vertical, and diagonal line segments. Classification allows for separate processing of these different direction segments, improving processing accuracy.

[0146] Finally, for each candidate segment set, the candidate segment with the smallest distance to the defect point is selected as the target segment. This ensures that the selected target segment is the one most relevant to the defect point in all directions, providing the most valuable information for subsequent defect analysis.

[0147] This embodiment firstly identifies candidate line segments and limits the search range to the second search area, improving search efficiency. Secondly, by classifying candidate line segments by direction, it facilitates the subsequent processing of line segments with different directions. Finally, by selecting the line segment with the shortest distance to the defect point in each direction as the target line segment, it ensures that the selected target line segment is most relevant to the defect point, providing accurate basic data for subsequent defect processing.

[0148] According to the defect processing method based on hash calculation provided by this application, correspondingly, this application also provides a specific embodiment of a defect processing device based on hash calculation.

[0149] like Figure 3 As shown, the defect processing device 300 based on hash calculation provided by the embodiment of the present application includes a line segment acquisition module 310, a hash point determination module 320, a hash point selection module 330, a hash value calculation module 340 and a defect point processing module 350.

[0150] The line segment acquisition module 310 is used to acquire a plurality of target line segments in the design layout whose distances from the defect point meet a preset distance condition;

[0151] A hash point determination module 320 is configured to determine candidate hash points on each of a plurality of target line segments having different line segment directions;

[0152] A hash point selection module 330 for selecting a target hash point from the candidate hash points based on the position of each candidate hash point;

[0153] A hash value calculation module 340 is used to calculate the defect hash value of the defect point based on the target hash point;

[0154] The defect point processing module 350 is used to process the defect point according to the defect information and defect hash value of the defect point to obtain a defect point processing result.

[0155] As an optional embodiment, the hash point determination module 320 includes the following units:

[0156] a line segment grouping unit, configured to group target line segments in opposite directions into a group according to the line segment direction of each target line segment, thereby obtaining a plurality of line segment groups;

[0157] A feature point determination unit is used to draw a perpendicular line from the defect point to the straight line where each target line segment of each line segment group is located, and use the intersection of the straight line where each target line segment is located and the perpendicular line as the line feature point of the target line segment;

[0158] The hash point determination unit is used to determine the candidate hash points of the target line segment according to the positional relationship between each line segment feature point and the target line segment.

[0159] As an optional embodiment, the hash point determination unit is configured to:

[0160] When each line segment feature point in the line segment group is on the target line segment, each line segment feature point is offset according to its position on the corresponding target line segment to obtain a candidate hash point;

[0161] In the case that at least one line segment feature point in the line segment group is not on the target line segment, each line segment feature point is determined as a candidate hash point.

[0162] As an optional embodiment, the hash point determination unit is specifically configured to:

[0163] Obtain the projected overlapping line segments between each target line segment in the line segment group along the direction perpendicular to the target line segment;

[0164] The endpoint closest to the feature point of each projected overlapping line segment is determined as the target endpoint;

[0165] The offset is determined based on each target endpoint and the preset tolerance, and the line segment feature points are offset according to the offset to obtain candidate hash points.

[0166] As an optional embodiment, the hash point selection module 330 includes the following units:

[0167] A distance acquisition unit, used to obtain the hash distance between each candidate hash point in each line segment group;

[0168] The hash point selection unit is used to determine the two candidate hash points with the smallest hash distance as target hash points.

[0169] As an optional embodiment, the hash value calculation module 340 includes the following units:

[0170] a hash value calculation unit, configured to determine, for each target hash point, a target hash value of the target hash point based on line segments in the design layout that are located within a first search area of ​​the target hash point;

[0171] The hash value accumulation unit is used to accumulate the target hash value of each target hash point to obtain the defect hash value of the defect point.

[0172] As an optional embodiment, before determining the target hash value of each target hash point based on the line segments in the design layout that are located in the first search area of ​​the target hash point, the hash value calculation module 340 further includes the following units:

[0173] a line segment marking unit, configured to mark line segments in a first search area of ​​a target hash point in a design layout as relevant line segments, and mark line segments outside the first search area as non-relevant line segments, to obtain a marked layout;

[0174] Hash value calculation unit, specifically used for:

[0175] Based on the correlation line segment of each target hash point in the marked layout, the target hash value of each target hash point is determined respectively.

[0176] As an optional embodiment, the hash value calculation unit is specifically configured to:

[0177] Based on the reference coordinate system, coordinate transformation is performed on the first endpoint coordinates of each correlation line segment of the target hash point to obtain the second endpoint coordinates of each correlation line segment, where the first endpoint coordinates are the coordinates of the line segment endpoint in the layout coordinate system of the marked layout, and the reference coordinate system is a plane rectangular coordinate system constructed with the target hash point as the origin and the line segment direction of the target line segment of the target hash point as the positive direction of the vertical axis;

[0178] Based on the vertical axis of the reference coordinate system, mirror transformation is performed on each second endpoint coordinate to obtain each third endpoint coordinate;

[0179] Based on each second endpoint coordinate and each third endpoint coordinate, a target hash value of the target hash point is determined.

[0180] As an optional embodiment, the defect information includes defect type and defect size;

[0181] The defect point processing module 350 is specifically used to:

[0182] Determine the key defect conditions of the defect point based on the defect type of the defect point;

[0183] If the defect size and defect hash value of the defect point meet the critical defect conditions, the defect level of the defect point is determined to be a critical defect;

[0184] If the defect size and defect hash value of the defect point do not meet the critical defect condition, the defect level of the defect point is determined to be a non-critical defect.

[0185] As an optional embodiment, the line segment acquisition module 310 is specifically configured to:

[0186] Determine each line segment in the second search area located at the defect point in the design layout as a candidate line segment;

[0187] Classify each candidate line segment according to its line segment direction to obtain multiple candidate line segment sets;

[0188] The candidate line segment with the smallest distance to the defect point in each candidate line segment set is determined as the target line segment.

[0189] According to the defect processing method based on hash calculation provided by this application, correspondingly, this application also provides a specific embodiment of a defect processing device based on hash calculation.

[0190] Figure 4 A schematic diagram of the hardware structure of a defect handling device based on hash calculation provided in an embodiment of the present application is shown.

[0191] The defect handling device based on hash calculation may include a processor 401 and a memory 402 storing computer program instructions.

[0192] Specifically, the processor 401 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0193] Memory 402 may include a large capacity memory for data or instructions. By way of example and not limitation, memory 402 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. Where appropriate, memory 402 may include removable or non-removable (or fixed) media. Where appropriate, memory 402 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, memory 402 is a non-volatile solid-state memory.

[0194] The processor 401 reads and executes computer program instructions stored in the memory 402 to implement any one of the defect handling methods based on hash calculation in the above embodiments.

[0195] In one example, the defect handling device based on hash calculation may further include a communication interface 403 and a bus 410. Figure 4 As shown, the processor 401 , the memory 402 , and the communication interface 403 are connected via a bus 410 and communicate with each other.

[0196] The communication interface 403 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0197] Bus 410 includes hardware, software or both, and the parts of the defect processing equipment based on hash calculation are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industry standard architecture (EISA) bus, front side bus (FSB), hypertransport (HT) interconnection, industry standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 410 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the application considers any suitable bus or interconnection.

[0198] In addition, in conjunction with the above-mentioned defect handling methods based on hash calculation, embodiments of the present application may provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by a processor, any of the above-mentioned defect handling methods based on hash calculation is implemented.

[0199] In addition, in combination with the defect processing method based on hash calculation in the above-mentioned embodiments, the embodiments of the present application may provide a computer program product for implementation. When the instructions in the computer program product are executed by the processor of an electronic device, the electronic device executes the defect processing method based on hash calculation provided by any aspect of the above-mentioned embodiments of the present application.

[0200] It should be understood that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, a detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated. Those skilled in the art can make various changes, modifications, and additions, or change the order of the steps after understanding the spirit of the present application.

[0201] The functional blocks shown in the above-described block diagram can be implemented as hardware, software, firmware or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "Machine-readable medium" can include any medium that can store or transmit information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROMs, flash memories, erasable ROMs (EROMs), floppy disks, CD-ROMs, optical disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0202] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or systems based on a series of steps or devices. However, this application is not limited to the order of the above steps. In other words, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0203] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box 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 produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the function / action specified in one or more boxes 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 is also understood that each box in the block diagram and / or flowchart and the combination of the boxes in the block diagram and / or flowchart can also be implemented by dedicated hardware that performs the specified function or action, or can be implemented by a combination of dedicated hardware and computer instructions.

[0204] The above description is only a specific embodiment of the present application. Those skilled in the art will 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 aforementioned method embodiments, and will not be repeated here. It should be understood that the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the scope of protection of the present application.

Claims

1. A defect handling method based on hash calculation, characterized in that: include: Acquire multiple target line segments in the design layout whose distances from the defect point meet preset distance conditions; Determining candidate hash points respectively from the plurality of target line segments having different line segment directions; selecting a target hash point from among the candidate hash points based on a position of each of the candidate hash points; Calculating a defect hash value of the defect point according to the target hash point; The defect point is processed according to the defect information of the defect point and the defect hash value to obtain a defect point processing result.

2. The method according to claim 1, characterized in that Determining candidate hash points respectively on the plurality of target line segments having different line segment directions includes: According to the line segment direction of each target line segment, the target line segments belonging to opposite directions are divided into a group to obtain a plurality of line segment groups; Draw a perpendicular line from the defect point to the straight line where each target line segment of each line segment group is located, and use the intersection of the straight line where each target line segment is located and the perpendicular line as the line segment feature point of the target line segment; The candidate hash points of the target line segment are determined according to the positional relationship between each of the line segment feature points and the target line segment.

3. The method according to claim 2, characterized in that The determining the candidate hash point of the target line segment according to the positional relationship between each line segment feature point and the target line segment includes: When each of the line segment feature points in the line segment group is on the target line segment, offset each of the line segment feature points according to the position of each of the line segment feature points on the corresponding target line segment to obtain the candidate hash point; In a case where at least one of the line segment feature points in the line segment group is not on the target line segment, each of the line segment feature points is determined as the candidate hash point.

4. The method according to claim 3, characterized in that The step of offsetting each of the line segment feature points according to a position of each of the line segment feature points on the corresponding target line segment to obtain the candidate hash point includes: Obtaining the projected overlapping line segments between the target line segments in the line segment group along a direction perpendicular to the target line segments; Determine the endpoint on each of the projected overlapping line segments that is closest to the line segment feature point as the target endpoint; An offset is determined based on each of the target endpoints and a preset tolerance, and the line segment feature points are offset according to the offset to obtain the candidate hash points.

5. The method according to claim 2, characterized in that The selecting a target hash point from the candidate hash points based on the position of each candidate hash point includes: Obtaining the hash distance between each candidate hash point in each line segment group; The two candidate hash points with the smallest hash distance are determined as the target hash points.

6. The method according to claim 1, wherein The step of calculating the defect hash value of the defect point according to the target hash point includes: For each of the target hash points, determining a target hash value of the target hash point based on line segments in the design layout that are located within a first search area of ​​the target hash point; The target hash value of each target hash point is accumulated to obtain the defect hash value of the defect point.

7. The method according to claim 6, characterized in that Before determining, for each target hash point, a target hash value of the target hash point based on line segments in the design layout that are located within a first search area of ​​the target hash point, the method further includes: Marking the line segments in the first search area of ​​the target hash point in the design layout as correlation line segments, and marking the line segments outside the first search area as non-correlation line segments, to obtain a marked layout; For each target hash point, determining a target hash value of the target hash point based on line segments in the design layout that are located within a first search area of ​​the target hash point includes: Based on the correlation line segment of each target hash point in the marked layout, a target hash value of each target hash point is determined respectively.

8. The method according to claim 7, characterized in that Determining the target hash value of each target hash point based on the correlation line segment of each target hash point in the marked layout includes: Based on the reference coordinate system, coordinate transformation is performed on the first endpoint coordinates of each of the correlation line segments of the target hash point to obtain the second endpoint coordinates of each of the correlation line segments, where the first endpoint coordinates are the coordinates of the line segment endpoints in the layout coordinate system of the marked layout, and the reference coordinate system is a plane rectangular coordinate system constructed with the target hash point as the origin and the line segment direction of the target line segment of the target hash point as the positive direction of the longitudinal axis; Based on the longitudinal axis of the reference coordinate system, mirror transform each of the second endpoint coordinates to obtain each of the third endpoint coordinates; Based on each of the second endpoint coordinates and each of the third endpoint coordinates, a target hash value of the target hash point is determined.

9. The method according to claim 1, characterized in that The defect information includes defect type and defect size; The processing of the defect point according to the defect information of the defect point and the defect hash value to obtain a defect point processing result includes: Determining a key defect condition of the defect point according to the defect type of the defect point; If the defect size of the defect point and the defect hash value meet the critical defect condition, determining the defect level of the defect point as a critical defect; When the defect size of the defect point and the defect hash value do not satisfy the critical defect condition, the defect level of the defect point is determined to be a non-critical defect.

10. The method according to claim 1, characterized in that The step of obtaining a plurality of target line segments in the design layout whose distances from the defect point meet a preset distance condition comprises: determining each line segment in the design layout that is located in the second search area of ​​the defect point as a candidate line segment; Classifying each of the candidate line segments according to the line segment directions to obtain multiple candidate line segment sets; The candidate line segment with the smallest distance from the defect point in each candidate line segment set is determined as the target line segment.

11. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the defect processing method based on hash calculation as described in any one of claims 1 to 10.

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