Sem graphical profile point analysis method, device, computer equipment and program product

Abstract

The application relates to the field of computing lithography technology, in particular to a SEM (Scanning Electron Microscope) pattern contour point analysis method and device, computer equipment and program product, which comprises the following steps: acquiring a SEM image to be analyzed, and calculating the directional derivative of each pixel in the SEM image; selecting a pixel point as a main point, marking a main point visual field in a preset range with the main point as the center, and marking the pixel points in the main point visual field except the main point as slave points; calculating the similarity of the main point and the slave points according to the directional derivatives of the main point and the slave points, and judging whether the main point is a contour point according to the calculation result. The application further provides a SEM pattern contour analysis device, computer equipment and program product, unnecessary time cost in a layout design process is saved, and the efficiency of the design process is improved.

Description

[Technical Field]

[0001] This invention relates to the field of computational lithography, and particularly to a method, apparatus, computer equipment, and program product for analyzing SEM graphic contour points. [Background Technology]

[0002] In modern VLSI manufacturing processes, the patterns on the silicon wafer surface after exposure and development are at the nanometer scale, making them impossible to image with ordinary optical microscopes. Scanning electron microscopy (SEM) is applied to this field to improve resolution and image these microscale patterns. SEM emits electrons onto the surface of the silicon wafer. These electrons, upon contacting the uneven surface, reflect varying numbers of electrons at different locations. These reflected electrons are called secondary electrons, and SEM obtains images of the microscale patterns on the silicon wafer surface by detecting these secondary electrons.

[0003] Current SEM image contour algorithms generally use gradient operators, which have several implementation methods. The most common one is the first-order differential operator in Equation 1.

[0004]

[0005] Similarly, there are the Sobel operator and the Laplace operator, both belonging to first-order differential operators, which are single-kernel convolution algorithms. Among comprehensive edge detection algorithms, the Canny algorithm performs relatively well. It first filters out noise from the original image using a low-pass filter, then applies NMS (Non-maximum suppression) for further noise control, and finally uses double threshold detection to obtain the contour edges. The Canny algorithm does not have an advantage in terms of computational speed. The Canny algorithm performs poorly in obtaining contours for SEM images, mainly because the quality of SEM images is generally low, such as... Figure 3 As shown, when the SEM image is magnified, the outline of the microscopic graphic structure is not visually obvious.

[0006] Even after applying traditional gradient operators or the Canny algorithm commonly used in image processing, it is impossible to obtain a satisfactory image contour.

[0007] It is evident that traditional algorithms lose image contour information that is still discernible to the naked eye. This is because SEM images have poor quality, and the signals of edges and contours are weak in numerical representation. After applying gradient operators, their values ​​are not much different from the background environment and are not easily distinguishable. The method for distinguishing contours usually uses the threshold method, as shown in Equation 2.

[0008] Contour i,j =f(I i,j ) = max(Ii,j -threshold,0) Equation 2

[0009] This method uses a floating-point value as a threshold to determine the contour: pixels above the threshold are identified as contours and edges, while pixels below it are excluded. This directly leads to some contour areas with weak numerical signals in the SEM image being identified as non-contour areas. [Summary of the Invention]

[0010] To address the problem that traditional methods cannot accurately determine whether pixels with weak numerical signals are contour points, this invention provides a method, apparatus, computer equipment, and program product for SEM graphic contour point analysis.

[0011] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a SEM image contour point analysis method, comprising the following steps: acquiring the SEM image to be analyzed and calculating the directional derivative of each pixel in the SEM image; selecting a pixel as the master point, marking a preset range of master point field of view centered on the master point, and marking pixels other than the master point within the master point field of view as slave points; calculating the similarity between the master point and the slave points based on the directional derivatives of the master point and the slave points, and determining whether the master point is a contour point based on the calculation result.

[0012] Preferably, after obtaining the directional derivative of each pixel in the SEM image, multiple pixels are simultaneously marked as principal points, and it is determined in parallel whether each principal point is a contour point.

[0013] Preferably, when the similarity is greater than a preset first threshold, the principal point is determined to be a contour point.

[0014] Preferably, the field of view of the main point does not exceed a circle with a radius of 10 nm centered on the main point.

[0015] Preferably, the similarity between the principal point and the secondary point is calculated using the following formula:

[0016]

[0017] Where Nrm represents the similarity score, and N represents the number of secondary points (excluding the main point) within the main point's field of view. is a vector, representing the directional derivative, i and j represent the coordinates of the principal point in the Cartesian coordinate system, and p and q represent the coordinates of the slave point relative to the principal point.

[0018] Preferably, when calculating the similarity between the principal point and the secondary points, a partially normalized or non-normalized method is used, as shown below:

[0019] Partial normalization:

[0020]

[0021] or

[0022]

[0023] Non-normalization:

[0024]

[0025] Preferably, the directional derivatives include derivatives in the mutually perpendicular X and Y directions.

[0026] To solve the above-mentioned technical problems, the present invention provides another technical solution as follows: a SEM image contour analysis device, which can implement the steps of the above-mentioned SEM image contour point analysis method, including: a receiving module for acquiring the SEM image to be analyzed; a marking module for marking the principal point, the principal point field of view, and the secondary points; and a calculation module for calculating the directional derivative of the pixel points and the similarity between the principal point and the secondary points, and determining whether the principal point is a contour point.

[0027] To solve the above-mentioned technical problems, the present invention provides another technical solution as follows: a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned SEM graphic contour point analysis method.

[0028] To solve the above-mentioned technical problems, the present invention provides another technical solution as follows: a program product, the program product including computer program instructions, which, when executed, implement the steps of the SEM graphic contour point analysis method described above.

[0029] Compared with existing technologies, the SEM graphic contour point analysis method provided by this invention has the following beneficial effects:

[0030] 1. The SEM image contour point analysis method provided in the first embodiment of the present invention includes the following steps: acquiring the SEM image to be analyzed and calculating the directional derivative of each pixel in the SEM image; selecting a pixel as the master point, marking a preset range of master point field of view centered on the master point, and marking pixels other than the master point within the master point field of view as slave points; calculating the similarity between the master point and slave points based on the directional derivatives of the master point and slave points, and determining whether the master point is a contour point based on the calculation result. It can be understood that the scheme provided in the first embodiment of the present invention differs from the traditional thresholding method. For some contour regions with relatively weak numerical signals, the traditional thresholding method easily determines them as non-contour regions. However, this scheme uses the similarity between a pixel and its neighboring pixels as the basis for determining whether it is a contour point. Even if the numerical signal of the current pixel is weak, it does not affect the determination of whether it is a contour point. Therefore, this scheme can obtain richer contour points for drawing clear image contours compared to the traditional thresholding method.

[0031] 2. In the SEM image contour point analysis method provided in the first embodiment of the present invention, after obtaining the directional derivative of each pixel in the SEM image, multiple pixels are simultaneously marked as principal points, and each principal point is judged in parallel to determine whether it is a contour point. Understandably, traditional methods generally use the judgment result of the previous pixel as a reference or basis for determining whether the current pixel is a threshold point. Therefore, the entire process is serial, and each pixel needs to be judged sequentially, which leads to excessively high analysis and judgment time costs and affects the efficiency of the overall process. In this solution, the analysis and judgment processes of each pixel are independent and do not affect each other. Therefore, multiple pixels can be analyzed simultaneously and processed in parallel, which can greatly save time costs and improve the efficiency of the overall process.

[0032] 3. In the SEM image contour point analysis method provided in the first embodiment of the present invention, when the similarity is greater than a preset first threshold, the principal point is determined to be a contour point. It can be understood that when the similarity is greater than the first threshold, it indicates that around the principal point, there are enough secondary points whose grayscale change direction is the same as or similar to that of the principal point, satisfying the contour point recognition characteristics. It is evident that the above scheme can accurately determine whether a point is a contour point based on similarity even when the numerical signal is weak, i.e., when the contour point is not clearly visible to the naked eye. Therefore, the scheme provided in the first embodiment of the present invention has higher accuracy and stronger reliability.

[0033] 4. In the SEM image contour point analysis method provided in the first embodiment of the present invention, the field of view of the principal point does not exceed a circle with a radius of 10 nm centered on the principal point. Understandably, if the field of view of the principal point is too small, the number of secondary points obtained for similarity assessment will be insufficient, making it impossible to accurately determine whether a point is a contour point based on its similarity to existing secondary points. Conversely, if the field of view is too large, the number of samples will be excessive, leading to higher resource and time costs in the calculation process. Therefore, setting the field of view of the principal point within the aforementioned range ensures the reliability of similarity calculation while reducing computational costs and improving process efficiency.

[0034] 5. In the SEM image contour point analysis method provided in the first embodiment of the present invention, the similarity between the principal point and the secondary point is calculated using a partially normalized or non-normalized method, as shown below:

[0035] Partial normalization:

[0036]

[0037] or

[0038]

[0039] Non-normalization:

[0040]

[0041] Understandably, using partial normalization or non-normalization can reduce the division operation steps. Extending this operation to the calculation of all pixels can save considerable computational resources and time. Therefore, the above solution improves the computational efficiency of the process.

[0042] 6. The second embodiment of the present invention also provides a SEM graphic contour analysis device, which has the same beneficial effects as the above-mentioned SEM graphic contour point analysis method, and will not be described in detail here.

[0043] 7. The third embodiment of the present invention also provides a computer device that has the same beneficial effects as the above-described SEM graphic contour point analysis method, which will not be described in detail here.

[0044] 8. The fourth embodiment of the present invention also provides a program product that has the same beneficial effects as the above-described SEM graphic contour point analysis method, which will not be described in detail here. [Attached Image Description]

[0045] Figure 1 This is a flowchart illustrating the SEM graphic contour point analysis method provided in the first embodiment of the present invention.

[0046] Figure 2This is a schematic diagram of the SEM image to be analyzed using the SEM graphic contour point analysis method provided in the first embodiment of the present invention.

[0047] Figure 3 This is a diagram illustrating the loss of edge contour information caused by traditional methods.

[0048] Figure 4 This is a schematic diagram of the pixel directional derivative of the SEM graphic contour point analysis method provided in the first embodiment of the present invention.

[0049] Figure 5 This is a schematic diagram illustrating the effect of different normalization strategies on the threshold in the SEM graphic contour point analysis method provided in the first embodiment of the present invention.

[0050] Figure 6 This is a schematic diagram comparing the SEM image contour recognition rate of the SEM image contour point analysis method provided in the first embodiment of the present invention with that of traditional methods.

[0051] Figure 7 This is a schematic diagram of the contour information identified by the SEM graphic contour point analysis method provided in the first embodiment of the present invention.

[0052] Figure 8 This is a schematic diagram of the SEM graphic contour analysis device provided in the second embodiment of the present invention.

[0053] Figure 9 This is a schematic diagram of a computer device provided in the third embodiment of the present invention.

[0054] Figure 10 This is a schematic diagram of the program product provided in the fourth embodiment of the present invention.

[0055] Explanation of reference numerals in the attached diagram:

[0056] 1. SEM graphic contour point analysis method; 2. SEM graphic contour analysis device; 3. Computer equipment; 4. Program product;

[0057] 20. Receiving module; 21. Tagging module; 22. Calculation module; 30. Memory; 31. Processor; 40. Program instructions;

[0058] 300. Computer program.

Detailed Implementation Methods

[0059] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0060] Please see Figure 1The first embodiment of the present invention provides a method 1 for analyzing contour points in SEM images, comprising the following steps:

[0061] S0: Obtain the SEM image to be analyzed (e.g., ... Figure 2 (as shown), and calculate the directional derivative of each pixel in the SEM image;

[0062] S1: Select a pixel and mark it as the master point, mark the master point field of view with the master point as the center, and mark the pixels in the master point field of view other than the master point as slave points;

[0063] S2: Calculate the similarity between the principal point and the slave point based on the directional derivatives of the principal point and the slave point, and determine whether the principal point is a contour point based on the calculation results.

[0064] Understandably, the solution provided in the first embodiment of this invention differs from the traditional thresholding method. For certain contour regions with relatively weak numerical signals, the traditional thresholding method easily classifies them as non-contour regions, resulting in the loss of contour edge information (e.g., Figure 3 As shown in the diagram, this method determines whether a pixel is a contour point based on its similarity to its neighboring pixels. Even if the numerical signal of the current pixel is weak, it does not affect the determination of whether it is a contour point. It can be seen that this method can obtain a richer set of contour points for drawing clear image contours compared to the traditional thresholding method.

[0065] Furthermore, the directional derivative includes the derivatives in the mutually perpendicular X and Y directions.

[0066] In some embodiments, after obtaining the directional derivative of each pixel in the SEM image, multiple pixels are simultaneously marked as principal points, and it is determined in parallel whether each principal point is a contour point.

[0067] Understandably, traditional methods typically use the result of the previous pixel's judgment as a reference or basis for determining whether the current pixel is a threshold point. Therefore, the entire process is sequential, requiring each pixel to be judged in turn, leading to excessively high analysis and judgment time costs and impacting the overall process efficiency. In this solution, however, the analysis and judgment processes for each pixel are independent and do not affect each other. Therefore, multiple pixels can be analyzed simultaneously and processed in parallel, significantly saving time costs and improving the overall process efficiency.

[0068] Furthermore, when the similarity is greater than a preset first threshold, the principal point is determined to be a contour point. Understandably, when the similarity is greater than the first threshold, it means that around the principal point, there are enough secondary points whose grayscale change direction is the same as or similar to that of the principal point, satisfying the contour point recognition characteristics. It is evident that the above scheme can accurately determine whether a point is a contour point based on similarity even when the numerical signal is weak, i.e., when the contour point is not clearly visible to the naked eye. Therefore, the scheme provided in the first embodiment of this invention has higher accuracy and stronger reliability.

[0069] It should be understood that the size of the preset first threshold can be determined according to the specific process flow.

[0070] In some embodiments, the field of view of the principal point does not exceed a circle with a radius of 10 nm centered on the principal point. Understandably, if the field of view of the principal point is too small, the number of secondary points obtained for similarity assessment will be insufficient, making it impossible to accurately determine whether a point is a contour point based on its similarity to existing secondary points. Conversely, if the field of view is too large, the number of samples will be excessive, leading to higher resource and time costs in the computation process. Therefore, setting the field of view of the principal point within the aforementioned range ensures the reliability of similarity calculations while reducing computational costs and improving process efficiency.

[0071] Optionally, the main point of view can be determined according to the specific process flow, and is not limited to a circle; it can also be a rectangle or other shapes.

[0072] In some embodiments, the similarity between the principal point and the secondary point is calculated using the following formula:

[0073]

[0074] Where Nrm represents the similarity score, and N represents the number of secondary points (excluding the main point) within the main point's field of view. is a vector, representing the directional derivative, i and j represent the coordinates of the principal point in the Cartesian coordinate system, and p and q represent the coordinates of the slave point relative to the principal point.

[0075] Please see Figure 4 Understandably, the directional derivative of a pixel The direction represents the direction of grayscale change of a pixel, and the magnitude represents the magnitude of the change. Pixels near a contour point will always have some pixels whose direction of the directional derivative is the same as or similar to the direction of that pixel. The direction of the directional derivative of non-contour points is disordered.

[0076] Preferably, when calculating the similarity between the principal point and the secondary points, a partially normalized or non-normalized method is used, as shown below:

[0077] Partial normalization:

[0078]

[0079] or

[0080]

[0081] Non-normalization:

[0082]

[0083] Understandably, using partial normalization or non-normalization can reduce the division operation steps. Extending this operation to the calculation of all pixels can save considerable computational resources and time. Therefore, the above solution improves the computational efficiency of the process.

[0084] It should be understood that different threshold schemes will be used for different normalization strategies; please refer to [link / reference] for details. Figure 5 .

[0085] Please combine Figure 6 and Figure 7 After adopting the SEM image contour point analysis method 1 provided in the first embodiment of the present invention, it can be clearly seen that the contour edges of the SEM image are richer, and compared with the traditional method, the recognition rate of the SEM image contour obtained by this solution is also significantly improved.

[0086] Please see Figure 8 The second embodiment of the present invention also provides a SEM graphic contour analysis device 2, which can implement the steps of the analysis method 1 as described in the first embodiment, including:

[0087] Receiver module 20: Used to acquire the SEM image to be analyzed;

[0088] Marking module 21: Used to mark the main point, the main point's field of view, and secondary points;

[0089] Calculation module 22: used to calculate the directional derivative of the pixel and the similarity between the principal point and the slave point, and to determine whether the principal point is a contour point.

[0090] For example, after receiving the SEM image to be analyzed, the receiving module 20 calculates the directional derivative of each pixel in the SEM image to be analyzed by the calculation module 22. Then, the marking module 21 selects a pixel to mark as the master point and marks the master point field of view based on the master point. Then, the pixels within the master point field of view are marked as slave points. Finally, the calculation module 22 calculates the similarity between the master point and the slave points based on the directional derivatives of the master point and the slave points, and determines whether the master point is a contour point based on the similarity.

[0091] Please participate in the drawing. Figure 9The third embodiment of the present invention also provides a computer device 3, including a memory 30, a processor 31 and a computer program 300 stored in the memory 30, wherein the processor 31 executes the computer program 300 to implement the steps of the SEM graphic contour point analysis method 1 as described in the first embodiment.

[0092] Please see Figure 10 The fourth embodiment of the present invention also provides a program product 4, which includes computer program instructions 40. When the computer program instructions 40 are executed, they implement the steps of the SEM graphic contour point analysis method 1 as described in the first embodiment.

[0093] In the embodiments provided by this invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined based on A. However, it should also be understood that determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information.

[0094] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of the invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Those skilled in the art should also recognize that the embodiments described in the specification are optional embodiments, and the actions and modules involved are not necessarily essential to the invention.

[0095] In various embodiments of the present invention, it should be understood that the sequence number of each process does not necessarily imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0096] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, or they may sometimes be executed in reverse order, depending on the functions involved. It is particularly important to note that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0097] Compared with existing technologies, the SEM graphic contour point analysis method provided by this invention has the following beneficial effects:

[0098] 1. The SEM image contour point analysis method provided in the first embodiment of the present invention includes the following steps: acquiring the SEM image to be analyzed and calculating the directional derivative of each pixel in the SEM image; selecting a pixel as the master point, marking a preset range of master point field of view centered on the master point, and marking pixels other than the master point within the master point field of view as slave points; calculating the similarity between the master point and slave points based on the directional derivatives of the master point and slave points, and determining whether the master point is a contour point based on the calculation result. It can be understood that the scheme provided in the first embodiment of the present invention differs from the traditional thresholding method. For some contour regions with relatively weak numerical signals, the traditional thresholding method easily determines them as non-contour regions. However, this scheme uses the similarity between a pixel and its neighboring pixels as the basis for determining whether it is a contour point. Even if the numerical signal of the current pixel is weak, it does not affect the determination of whether it is a contour point. Therefore, this scheme can obtain richer contour points for drawing clear image contours compared to the traditional thresholding method.

[0099] 2. In the SEM image contour point analysis method provided in the first embodiment of the present invention, after obtaining the directional derivative of each pixel in the SEM image, multiple pixels are simultaneously marked as principal points, and each principal point is judged in parallel to determine whether it is a contour point. Understandably, traditional methods generally use the judgment result of the previous pixel as a reference or basis for determining whether the current pixel is a threshold point. Therefore, the entire process is serial, and each pixel needs to be judged sequentially, which leads to excessively high analysis and judgment time costs and affects the efficiency of the overall process. In this solution, the analysis and judgment processes of each pixel are independent and do not affect each other. Therefore, multiple pixels can be analyzed simultaneously and processed in parallel, which can greatly save time costs and improve the efficiency of the overall process.

[0100] 3. In the SEM image contour point analysis method provided in the first embodiment of the present invention, when the similarity is greater than a preset first threshold, the principal point is determined to be a contour point. It can be understood that when the similarity is greater than the first threshold, it indicates that around the principal point, there are enough secondary points whose grayscale change direction is the same as or similar to that of the principal point, satisfying the contour point recognition characteristics. It is evident that the above scheme can accurately determine whether a point is a contour point based on similarity even when the numerical signal is weak, i.e., when the contour point is not clearly visible to the naked eye. Therefore, the scheme provided in the first embodiment of the present invention has higher accuracy and stronger reliability.

[0101] 4. In the SEM image contour point analysis method provided in the first embodiment of the present invention, the field of view of the principal point does not exceed a circle with a radius of 10 nm centered on the principal point. Understandably, if the field of view of the principal point is too small, the number of secondary points obtained for similarity assessment will be insufficient, making it impossible to accurately determine whether a point is a contour point based on its similarity to existing secondary points. Conversely, if the field of view is too large, the number of samples will be excessive, leading to higher resource and time costs in the calculation process. Therefore, setting the field of view of the principal point within the aforementioned range ensures the reliability of similarity calculation while reducing computational costs and improving process efficiency.

[0102] 5. In the SEM image contour point analysis method provided in the first embodiment of the present invention, the similarity between the principal point and the secondary point is calculated using a partially normalized or non-normalized method, as shown below:

[0103] Partial normalization:

[0104]

[0105] or

[0106]

[0107] Non-normalization:

[0108]

[0109] Understandably, using partial normalization or non-normalization can reduce the division operation steps. Extending this operation to the calculation of all pixels can save considerable computational resources and time. Therefore, the above solution improves the computational efficiency of the process.

[0110] 6. The second embodiment of the present invention also provides a SEM graphic contour analysis device, which has the same beneficial effects as the above-mentioned SEM graphic contour point analysis method, and will not be described in detail here.

[0111] 7. The third embodiment of the present invention also provides a computer device that has the same beneficial effects as the above-described SEM graphic contour point analysis method, which will not be described in detail here.

[0112] 8. The fourth embodiment of the present invention also provides a program product that has the same beneficial effects as the above-described SEM graphic contour point analysis method, which will not be described in detail here.

[0113] The foregoing has provided a detailed description of a SEM graphic contour point analysis method, apparatus, computer device, and program product disclosed in the embodiments of the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention. Any modifications, equivalent substitutions, and improvements made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for analyzing contour points in SEM images, characterized in that: Includes the following steps: Acquire the SEM image to be analyzed and calculate the directional derivative of each pixel in the SEM image; Select a pixel and mark it as the master point. Mark a preset range of master point field of view with the master point as the center. Mark all pixels in the master point field of view other than the master point as slave points. The similarity between the principal point and the slave point is calculated based on the directional derivatives of the principal point and the slave point, and the principal point is determined as a contour point based on the calculation results. When the similarity is greater than a preset first threshold, the principal point is determined to be a contour point; The field of view of the main point does not exceed a circle with a radius of 10 nm centered on the main point.

2. The SEM graphic contour point analysis method as described in claim 1, characterized in that: After obtaining the directional derivative of each pixel in the SEM image, multiple pixels are simultaneously marked as principal points, and it is determined in parallel whether each principal point is a contour point.

3. The SEM graphic contour point analysis method as described in claim 1, characterized in that: The similarity between the principal point and the secondary point is calculated using the following formula: ； Where Nrm represents the similarity, N represents the number of slave points other than the master point within the master point's field of view, ∇I is a vector representing the directional derivative, i and j represent the coordinates of the master point in the Cartesian coordinate system, and p and q represent the coordinates of the slave points relative to the master point.

4. The SEM graphic contour point analysis method as described in claim 3, characterized in that: When calculating the similarity between the principal point and the secondary points, a partially normalized or non-normalized method is used, as shown below: Partial normalization: ； or ； Non-normalization: 。 5. The SEM graphic contour point analysis method as described in claim 4, characterized in that: The directional derivatives include the derivatives in the mutually perpendicular X and Y directions.

6. A SEM graphic contour analysis device, characterized in that: The SEM image contour analysis device can implement the steps of the SEM image contour point analysis method as described in claim 1, including: Receiver module: Used to acquire the SEM image to be analyzed; Marking module: Used to mark the master point, the master point's field of view, and slave points; Calculation module: used to calculate the directional derivative of pixels and the similarity between principal and slave points, and to determine whether the principal point is a contour point.

7. A computer device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the SEM graphic contour point analysis method as described in claim 1.

8. A program product, characterized in that: The program product includes computer program instructions, which, when executed, implement the steps of the SEM graphic contour point analysis method as described in claim 1.

Images