Peak signal repositioning based scanning electron microscope image profile extraction method, system, medium, program product and terminal

Abstract

The application provides a scanning electron microscope image contour extraction method, system, medium, program product and terminal based on peak signal relocation. The method obtains corresponding brightness signal distribution on the boundary line of a layout, preliminarily locates the layout according to the brightness signal; obtains corresponding brightness signal distribution on the boundary line of the preliminarily located layout again to perform relocation search to obtain corresponding image edge points, generates a preliminary contour map of the SEM image based on a plurality of image edge points, and performs smoothing processing on the preliminary contour map to obtain a final contour map. The precision interference problem of the layout during scanning electron microscope image contour extraction is effectively solved.

Description

Technical Field

[0001] This application relates to the field of scanning electron microscope image contour extraction technology, and in particular to a scanning electron microscope image contour extraction method, system, medium, program product and terminal based on peak signal relocation. Background Technology

[0002] Scanning electron microscope (SEM) images are produced by scanning the sample surface point by point with an electron beam, collecting the signals such as secondary electrons excited from the sample using a detector, and finally converting the signal strength into pixel ranges. SEM images offer advantages such as three-dimensional visualization and large depth of field, and are of great importance in the fabrication of precision components, especially in the semiconductor field.

[0003] A crucial task in SEM image analysis is contour extraction. A suitable contour extraction algorithm can transform random and complex image information into patterned graphic information. By measuring the dimensions at key locations within the graphic (CD value), the tolerance of the manufacturing process at the location of interest can be measured. By measuring the edge placement error between the graphic and its corresponding layout (EPE value), the image quality acquired by the instrument can be assessed. In short, the quality of contour extraction algorithms is a bottleneck technology in semiconductor process quality verification.

[0004] For most SEM images, the foreground and background of the contour to be extracted do not differ significantly in image brightness. Therefore, to assist in contour extraction, producers often draw or request graphic representations of the image layout from manufacturers. Contour extraction based on the layout is currently the mainstream algorithm in the industry. It uses the layout at a given location as a base point, superimposing it on the SEM image in the same coordinate system. By evenly marking points along the edges of the layout and drawing tangents along the normal direction within a given range, it finds the intersection points with the edges of the SEM image and reconstructs the contour information from these intersection points. This method is mainly affected by the accuracy of the initial layout position. This often results in unexpected point drops during the actual calculation of contour intersection points because the true solution is not within the search range. Consequently, the final contour is not smooth in some locations, has missing shapes in others, and the overall image quality is low. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, the purpose of this application is to provide a method, system, medium, program product and terminal for contour extraction of scanning electron microscope images based on peak signal relocation, so as to solve the problem of low image quality caused by the influence of the accuracy of the initial position of the layout when extracting contours of scanning electron microscope images in the prior art.

[0006] To achieve the above and other related objectives, a first aspect of this application provides a method for contour extraction from scanning electron microscope images based on peak signal relocation, comprising: acquiring a SEM image and a first image of a sample; coarsely aligning the first image and the SEM image using image similarity matching to obtain a second image; performing corner rounding processing on the second image to obtain a third image; selecting several points on the boundary line of the third image as first sampling points; acquiring the corresponding brightness signal distribution in the SEM image along the normal direction of each first sampling point; and performing contour extraction on the second image based on the brightness signal distribution of each first sampling point. The third image is used for preliminary positioning to obtain the fourth image. Several points are selected on the boundary line of the fourth image as second sampling points. The corresponding brightness signal distribution is obtained in the SEM image along the normal direction of each second sampling point. The corresponding second sampling points are repositioned according to the brightness signal distribution of each second sampling point to obtain the third sampling point corresponding to each second sampling point. The corresponding image edge points are searched in the SEM image for each third sampling point. A preliminary contour map of the SEM image is generated based on several image edge points. The preliminary contour map is then smoothed to obtain the final contour map.

[0007] In some embodiments of the first aspect of this application, a corresponding luminance signal distribution is obtained in the SEM image along the normal direction of each first sampling point, and the third image is initially positioned based on the luminance signal distribution of each first sampling point to obtain a fourth image. The process includes: intercepting the corresponding first sampling line segment along the normal direction of each first sampling point using a fixed step size method; obtaining the luminance signal distribution of the first sampling line segment of each first sampling point in the SEM image, and obtaining the peak point of each first sampling line segment based on the luminance signal distribution; calculating the average distance based on the distance between several first sampling points and the peak point of the corresponding first sampling line segment; and initially positioning the third image based on the average distance to obtain a fourth image.

[0008] In some embodiments of the first aspect of this application, the corresponding luminance signal distribution is obtained in the SEM image along the normal direction of each second sampling point, and the corresponding second sampling point is repositioned according to the luminance signal distribution of each second sampling point to obtain a third sampling point corresponding to each second sampling point. The process includes: intercepting the corresponding second sampling line segment in the normal direction of each second sampling point using a fixed step size method; obtaining the luminance signal distribution of the second sampling line segment of each second sampling point in the SEM image, and obtaining the peak point of each second sampling line segment according to the luminance signal distribution; and moving each second sampling point to the peak point position of the corresponding second sampling line segment to obtain a third sampling point corresponding to each second sampling point.

[0009] In some embodiments of the first aspect of this application, the process of rounding corners of the second layout includes: selecting the inflection points in the second layout and converting the included angles corresponding to the inflection points into rounded corners.

[0010] In some embodiments of the first aspect of this application, the process of using image similarity matching to coarsely align the first image with the SEM image to obtain the second image includes: dividing the first image into several sub-images; calculating the structural similarity index between the SEM image and each sub-image respectively; and selecting the sub-image corresponding to the highest structural similarity index from among the structural similarity indices as the target sub-image, wherein the target sub-image is the second image.

[0011] In some embodiments of the first aspect of this application, the process of searching for the corresponding image edge point for each third sampling point within the SEM image includes: taking the normal direction of each second sampling point in the fourth image as the normal direction of the corresponding third sampling point; intercepting the corresponding third sampling line segment along the normal direction of each third sampling point using a fixed step size method; calculating the corresponding image gradient map using an edge detection operator based on the position of the third sampling line segment of each third sampling point in the SEM image; and filtering based on the image gradient map of each third sampling line segment to obtain the image edge point of each third sampling point.

[0012] To achieve the above and other related objectives, a second aspect of this application provides a scanning electron microscope image contour extraction system based on peak signal relocation. The system includes: a coarse alignment module for acquiring a SEM image and a first image of a sample, and using image similarity matching to coarsely align the first image and the SEM image to obtain a second image; an image rounding module for rounding the corners of the second image to obtain a third image; and a preliminary positioning module for selecting several points as first sampling points on the boundary line of the third image, acquiring the corresponding brightness signal distribution in the SEM image along the normal direction of each first sampling point, and determining the brightness of each first sampling point based on its brightness. The signal distribution is used to initially locate the third image to obtain the fourth image. The relocation module is used to select several points as second sampling points on the boundary line of the fourth image, obtain the corresponding brightness signal distribution in the SEM image along the normal direction of each second sampling point, and relocate the corresponding second sampling point according to the brightness signal distribution of each second sampling point to obtain the third sampling point corresponding to each second sampling point. The contour acquisition module is used to search for the corresponding image edge points in the SEM image for each third sampling point, generate a preliminary contour map of the SEM image based on several image edge points, and smooth the preliminary contour map to obtain the final contour map.

[0013] To achieve the above and other related objectives, a third aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for extracting contours from scanning electron microscope images based on peak signal relocation.

[0014] To achieve the above and other related objectives, a fourth aspect of this application provides a computer program product comprising computer program code that, when executed on a computer, causes the computer to implement the aforementioned method for extracting contours from scanning electron microscope images based on peak signal relocation.

[0015] To achieve the above and other related objectives, a fifth aspect of this application provides an electronic terminal, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the aforementioned method for extracting contours from scanning electron microscope images based on peak signal relocation.

[0016] As described above, the scanning electron microscope image contour extraction method, system, medium, program product, and terminal based on peak signal relocation of this application have the following beneficial effects:

[0017] This application employs a two-stage positioning strategy—"preliminary global calibration" and "peak-guided local fine calibration"—on the overall design layout and SEM images of the sample, effectively overcoming the shortcomings of existing technologies that are highly sensitive to the initial position of the layout. The method of this application significantly improves the robustness and accuracy of contour extraction, enabling the stable extraction of complete, smooth, and high-fidelity sample contours even when there are deviations in the initial position of the layout, providing a superior solution for semiconductor process inspection. Attached Figure Description

[0018] Figure 1 The diagram shown is a flowchart illustrating a scanning electron microscope image contour extraction method based on peak signal relocation in one embodiment of this application.

[0019] Figure 2 The diagram shown is a schematic representation of coarse alignment in one embodiment of this application.

[0020] Figure 3 The diagram shown is a schematic representation of a rounding operation in one embodiment of this application.

[0021] Figure 4 The diagram shown is a schematic diagram of obtaining a waveform in one embodiment of this application.

[0022] Figure 5 The diagram shown is a schematic representation of the relocation of the second sampling point in one embodiment of this application.

[0023] Figure 6 The diagram shown is a schematic representation of a scanning electron microscope image contour extraction system based on peak signal relocation, according to an embodiment of this application.

[0024] Figure 7 The diagram shown is a schematic representation of the structure of an electronic terminal for a scanning electron microscope image contour extraction method based on peak signal relocation, as described in one embodiment of this application. Detailed Implementation

[0025] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0026] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," "fixing," and "holding" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0027] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0028] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0029] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.

[0030] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for explaining the present invention and are not intended to limit the invention.

[0031] Before providing a further detailed description of the present invention, the nouns and terms used in the embodiments of the present invention are explained, and the nouns and terms used in the embodiments of the present invention are subject to the following interpretations:

[0032] <1> SEM image: A grayscale image obtained by scanning electron microscopy on the sample surface. Its brightness reflects the strength of the electronic signal and can clearly show the microscopic morphology of the sample.

[0033] <2> Field of view size: In image processing, it refers to the local area range extracted from the overall design layout for matching with the target image, and is a preset parameter for template matching.

[0034] <3> Rasterization: The process of converting continuous geometric shapes (such as vector maps) into discrete images represented by a pixel matrix for digital image processing.

[0035] <4> Structural similarity index Similarity index is an indicator for measuring the similarity between two images. It compares three dimensions—brightness, contrast, and structure—which is more in line with human visual perception.

[0036] <5> Edge detection operators: mathematical operators (such as Sobel and Canny) used to identify sharp changes in grayscale or color (i.e., edges) in digital images. They are implemented by calculating the gradient of the image through convolution.

[0037] To facilitate understanding of the embodiments of this application, firstly, in conjunction with Figure 1 Detailed explanation. Figure 1 This document illustrates a flowchart of a scanning electron microscope image contour extraction method based on peak signal relocation, according to an embodiment of the present invention. The method mainly includes the following steps:

[0038] Step S11: Obtain the SEM image and the first image of the sample. Use image similarity matching to coarsely align the first image with the SEM image to obtain the second image.

[0039] It should be noted that a scanning electron microscope (SEM) is used to focus an electron beam onto the sample to obtain an SEM image (Scanning Electron Microscopy image). It should be understood that a scanning electron microscope is a high-resolution imaging and analysis instrument that uses a focused, high-energy electron beam to scan the sample surface and detects various signals generated by the interaction between the electron beam and the sample, thereby obtaining information on the microscopic morphology, composition, and structure of the sample surface. The SEM image obtained using a scanning electron microscope is a high-resolution grayscale image. The grayscale value of each pixel corresponds to the signal intensity excited by the electron beam at that point.

[0040] Furthermore, based on the overall design layout of the sample, within a preset field of view size range (such as... Figure 2 As shown, the design layout is extracted from a rectangular area (with a viewing field size of H and a viewing field size of L) and rasterized to obtain the first layout of the sample.

[0041] The preset field of view refers to the physical size of a rectangular region that is pre-defined and cropped. This rectangular region is typically slightly larger than the actual SEM image (e.g., ...). Figure 2 As shown, the field of view (a rectangular area with height x and width y in the SEM image) is designed to cover the overall positional drift that may exist in the image, serving as the search and comparison range for initial coarse alignment with the SEM image.

[0042] Specifically, such as Figure 2 As shown, the overall design layout of the sample and the SEM image 202 are located in the same plane coordinate system. The upper left corner of the first layout 201 is selected as the positioning point, and its coordinates in the plane coordinate system are (…). The coordinates of the center point of the first map in the plane coordinate system are ( ).

[0043] In one embodiment of this application, the process of using image similarity matching to coarsely align the first image with the SEM image to obtain the second image includes: dividing the first image into several sub-images; calculating the structural similarity index between the SEM image and each sub-image respectively; and selecting the sub-image corresponding to the highest structural similarity index from among the structural similarity indices as the target sub-image, wherein the target sub-image is the second image.

[0044] Furthermore, this application employs a structural similarity index to measure image similarity. The structural similarity index is a mathematical model used to objectively assess the perceptual similarity between two images. By simulating the human visual system, it comprehensively quantifies the similarity of two images in terms of brightness, contrast, and structure. A value closer to 1 indicates greater similarity between the two images, and it is widely used in image quality assessment, compression, and matching. The specific calculation method is as follows:

[0045] First, we define functional expressions for brightness, contrast, and structure. The image brightness comparison function is as follows: The corresponding component weight parameters are The image contrast comparison function is: The corresponding component weight parameters are The image structure comparison function is: The corresponding component weight parameters are Then the structural similarity index can be represented by the following formula:

[0046] ;(Formula 1)

[0047] in, Point in the plane coordinate system The structural similarity index, To adjust the parameters, The value is generally 1, but those skilled in the art can set it as needed.

[0048] Furthermore, Characterized by the local mean of the image, as follows:

[0049] ;(Formula 2)

[0050] in, Point in a planar coordinate system The local mean, These are the parameters to be adjusted.

[0051] Furthermore, Characterized by the local standard deviation of the image, as follows:

[0052] ;(Formula 3)

[0053] in, Point in a planar coordinate system Local standard deviation, These are the parameters to be adjusted.

[0054] Furthermore, Characterized by the local covariance of the image, as follows:

[0055] ;(Formula 4)

[0056] in, It is a point Local covariance, These are the parameters to be adjusted.

[0057] The above are the constants to be adjusted. Its main function is to avoid the denominator being zero or the value being unstable. It can be determined by the following empirical formula:

[0058] ;(Formula 5)

[0059] Where L represents the dynamic range of the image pixels; for grayscale images, L is 255. It is an empirical constant much less than 1, ranging from 0.01 to 0.03, and the specific value is not limited here.

[0060] It should be noted that, It is a relatively accurate metric for measuring image similarity, but it is computationally intensive. In practical engineering, the mean SSIM is often calculated. It uses a sliding window of a given size to traverse the entire image, and then takes the arithmetic mean of the SSIM values ​​of all windows as the similarity score of the entire image. By adjusting the window size and sliding step, a balance can be achieved in terms of algorithm time and accuracy.

[0061] Preferably, the structural similarity index of all points in each subgraph is calculated according to the above-described method. Then, the average of the structural similarity indices of all points in the current subgraph is calculated to obtain the SSIM average value as the structural similarity index of the current subgraph, thus obtaining the structural similarity index of each subgraph. The same method of calculating and averaging the structural similarity index is used for SEM graphs to obtain their structural similarity indices.

[0062] Furthermore, the structural similarity index of the several sub-graphs is compared with the structural similarity index of the SEM image, and the sub-graph whose structural similarity index is closest to that of the SEM image is selected as the target sub-graph, which is the second version of the image.

[0063] Furthermore, such as Figure 2 As shown, the position of the second map is moved to the position of the SEM map in the plane coordinate system.

[0064] Step S12: Round off the corners of the second version to obtain the third version.

[0065] In one embodiment of this application, the process of rounding corners in the second layout includes: selecting inflection points in the second layout and converting the included angles corresponding to the inflection points into rounded corners. The inflection point refers to a point in the second layout where the tangent direction on the graphic outline is discontinuous or abruptly changes, i.e., a location on the curve where a "sharp corner" appears or the direction suddenly changes.

[0066] Specifically, such as Figure 3 As shown, For any vertex in the second version of the map, obtain the current vertex. Adjacent inflection points and The circularization algorithm steps are as follows:

[0067] First, calculate the inflection point. The edge vectors on both sides ( The calculation formula is as follows:

[0068] ;(Formula 6)

[0069] Furthermore, the edge vectors are normalized to obtain ( ):

[0070] ;(Formula 7)

[0071] Furthermore, the inflection points are calculated based on the normalized edge vectors. The included angle on both sides The details are as follows:

[0072] ;(Formula 8)

[0073] Furthermore, the inflection point is calculated. The offset distance d between the points of tangency of the inscribed circles on both sides, such as Figure 4 As shown, with For example, the details are as follows:

[0074] ;(Formula 9)

[0075] Where r is an adjustment parameter, and the specific value can be set as needed.

[0076] Furthermore, assuming the inflection point The points of tangency of the incircles of both sides are respectively ( Figure 4 As shown Under the conditions, and That is, respectively ), The calculation formula is as follows:

[0077] ;(Formula 10)

[0078] Furthermore, the inflection point is calculated. The center of the incircle to which both sides belong arrive The distance is as follows:

[0079] ;(Formula 11)

[0080] Furthermore, the calculation is performed on a unit vector of length 1, with the starting point being the inflection point. The unit angle bisector vector points towards the angle bisector of the vectors on both sides of the inflection point. The details are as follows:

[0081] ;(Formula 12)

[0082] According to the unit angle bisector vector Calculate and obtain the center of the inscribed circle The details are as follows:

[0083] ;(Formula 13)

[0084] Based on the center of the inscribed circle and the inflection point The points of tangency of the incircles of both sides The inscribed circle trajectory is uniformly sampled on the contour between the two tangent points, and then inserted. Between, the inflection point is obtained. The result of rounding the corner points.

[0085] Furthermore, the corner point rounding operation described above is performed on all the vertices in the second version to obtain the third version.

[0086] Step S13: Select several points on the boundary line of the third map as first sampling points, obtain the corresponding brightness signal distribution in the SEM image along the normal direction of each first sampling point, and perform preliminary positioning of the third map based on the brightness signal distribution of each first sampling point to obtain the fourth map.

[0087] Specifically, such as Figure 4 As shown, several first sampling points are uniformly marked on the boundary line of the third version. The distance between adjacent first sampling points can be flexibly determined according to the actual structural density of the third version, and no specific limitation is made here.

[0088] In one embodiment of this application, the corresponding luminance signal distribution is obtained in the SEM image along the normal direction of each first sampling point. The third image is preliminarily located based on the luminance signal distribution of each first sampling point to obtain a fourth image. The process includes: intercepting the corresponding first sampling line segment along the normal direction of each first sampling point using a fixed step size method; obtaining the luminance signal distribution of the first sampling line segment of each first sampling point in the SEM image, and obtaining the peak point of each first sampling line segment based on the luminance signal distribution; calculating the average distance based on the distance between several first sampling points and the peak point of the corresponding first sampling line segment; and preliminarily locating the third image based on the average distance to obtain a fourth image.

[0089] Furthermore, a unique tangent can be determined at any point on the boundary line of the third layout after corner point rounding. This means the tangent at the first sampling point and the normal perpendicular to the tangent can be determined. Two line segments of the same preset length are then extracted from the inner and outer sides of the third layout using a fixed step size method, serving as the first sampling line segment corresponding to the first sampling point. The preset length can be determined based on the structural density of the third layout and is not specifically limited here.

[0090] Furthermore, the distribution of the brightness signal at the corresponding position in the SEM image of each first sampling point in the third version of the image is obtained from the first sampling line segment in the same plane coordinate system. Based on the distribution of the brightness signal, a waveform diagram is constructed. The horizontal axis of the waveform diagram is the distance from each point in the first sampling line segment to the first sampling point, and the vertical axis is the brightness signal of each point in the first sampling line segment in the corresponding SEM image.

[0091] Furthermore, such as Figure 4 As shown, the peak points in the waveform graph are obtained. Since there may be multiple local poles in the waveform graph, misjudgment may occur when selecting peak points. Therefore, the local poles among the multiple peaks are excluded by the following conditions to select and determine a unique peak point. The specific process is as follows:

[0092] For example, if the number of points in a first sampling segment is n, and the brightness signal at any point is... The peak point of the first sampled line segment needs to meet the following conditions:

[0093] ;(Formula 14)

[0094] First, the points in the first sampled line segment are sorted from smallest to largest. It refers to the brightness signal of the points in the first sampled line segment after sorting, where M is the median of the brightness signals of the points in the n first sampled line segments. When the brightness signal is greater than the brightness signal of the adjacent point in the waveform diagram and is greater than M, The corresponding point is the peak point.

[0095] Furthermore, the direction vector m from each first sampling point to the corresponding peak point in the waveform is calculated, and the coordinates of the i-th first sampling point are... The coordinates of the peak point in the corresponding waveform are Then the direction vector can be expressed by the following formula:

[0096] ;(Formula 15)

[0097] Where N is the total number of the first sampling points in the third version of the map. Let be the direction vector of the i-th first sampling point.

[0098] Calculate the average value of all direction vectors The calculation formula is as follows:

[0099] ;(Formula 16)

[0100] Using the average value as the average distance, the coordinates of all points in the third map are moved based on the average distance to obtain the fourth map.

[0101] Step S14: Select several points on the boundary line of the fourth map as second sampling points, obtain the corresponding brightness signal distribution in the SEM map along the normal direction of each second sampling point, and relocate the corresponding second sampling point according to the brightness signal distribution of each second sampling point to obtain the third sampling point corresponding to each second sampling point.

[0102] Specifically, several second sampling points are evenly marked on the boundary line of the fourth version. The distance between adjacent second sampling points can be flexibly determined according to the actual structural density of the fourth version, and no specific limitation is made here.

[0103] In one embodiment of this application, the corresponding luminance signal distribution is obtained in the SEM image along the normal direction of each second sampling point. The corresponding second sampling point is repositioned according to the luminance signal distribution of each second sampling point to obtain a third sampling point corresponding to each second sampling point. The process includes: intercepting the corresponding second sampling line segment along the normal direction of each second sampling point using a fixed step size method; obtaining the luminance signal distribution of the second sampling line segment of each second sampling point in the SEM image; obtaining the peak point of each second sampling line segment according to the luminance signal distribution; and moving each second sampling point to the peak point position of the corresponding second sampling line segment to obtain a third sampling point corresponding to each second sampling point.

[0104] Specifically, such as Figure 5 As shown, several second sampling points 5012 are uniformly marked on the boundary line 501 of the fourth version. The distance between adjacent sampling points can be flexibly determined according to the actual structural density of the fourth version, and no specific limitation is made here.

[0105] Furthermore, along the normal direction of the second sampling point 5012, two line segments of the same preset length are respectively cut from the inner and outer sides of the fourth pattern using a fixed step size method, as the corresponding second sampling line segment 5011 of the second sampling point 5012. The preset length can be determined according to the structural density of the fourth pattern, and is not specifically limited here.

[0106] Furthermore, the distribution of the brightness signal at the corresponding position in the SEM image of each second sampling point 5012 in the fourth version of the image is obtained from the second sampling line segment 5011 in the same plane coordinate system. Based on the distribution of the brightness signal, a waveform diagram is constructed. The horizontal axis of the waveform diagram is the distance of each point in the second sampling line segment 5011 from the second sampling point 5012, and the vertical axis is the brightness signal of each point in the second sampling line segment 5011 in the corresponding SEM image.

[0107] Further, according to the peak point acquisition method in step S14 above, the peak point 5021 in the waveform diagram corresponding to each second sampling line segment 5011 is obtained. The peak points 5021 corresponding to the second sampling points 5012 are connected to form the peak trajectory 502 of the boundary line 501 of the fourth layout. Along the normal direction of the second sampling point 5012, two line segments of the same preset length are respectively intercepted on the inside and outside of the peak point 5021 in the waveform diagram corresponding to the second sampling line segment 5011 using a fixed step size method, as the third sampling line segment 5022 corresponding to the second sampling point, for subsequent edge search.

[0108] Step S15: Search for each third sampling point within the SEM image to obtain the corresponding image edge point, generate a preliminary contour map boundary line 503 of the SEM image based on several image edge points, and smooth the preliminary contour map to obtain the final contour map.

[0109] In one embodiment of this application, the process of searching for the corresponding image edge point for each third sampling point within the SEM image includes: taking the normal direction of each second sampling point 5012 in the fourth image as the normal direction of the corresponding third sampling point 5021; intercepting the corresponding third sampling line segment 5022 along the normal direction of each third sampling point 5021 using a fixed step size method; calculating the corresponding image gradient map using an edge detection operator based on the position of the third sampling line segment 5022 of each third sampling point 5021 in the SEM image; and filtering based on the image gradient map of each third sampling line segment to obtain the image edge point of each third sampling point.

[0110] Specifically, this application obtains image edge points 5031 based on the gradient information of the third sampled line segment 5022. More specifically, it uses an edge detection operator to calculate the image gradient map corresponding to the third sampled line segment 5022. Edge detection is a fundamental technique in image processing used to identify regions with abrupt changes in grayscale or color in an image. Its core is to locate object boundaries by calculating the gradient of image brightness. Common edge detection operators include: the Sobel operator (which detects edges using horizontal and vertical convolution kernels), the Canny operator (which achieves high-precision detection through multi-stage noise suppression and dual-threshold processing), the Laplacian operator (which enhances edges based on second-order differentiation), the Prewitt operator (similar to Sobel but with different convolution kernel weights), and the Roberts operator (which performs simple and fast edge detection through local difference calculation). These operators each have their own characteristics and are suitable for edge extraction needs in different scenarios.

[0111] Preferably, this embodiment uses the Sobel operator to draw the image gradient map, and the algorithm steps are as follows:

[0112] First, calculate the horizontal gradient of the third sampled line segment, using the horizontal gradient kernel. A convolution operation is performed on the position of the third sampled line segment on the SEM image. The horizontal gradient at any point in the third sampled line segment after convolution is then calculated. It can be determined by the following formula:

[0113] ;(Formula 17)

[0114] ;(Formula 18)

[0115] in, The original grayscale image of the position of the third sampled line segment on the SEM image is used as the input for calculating the horizontal gradient. Horizontal gradient kernel.

[0116] Furthermore, the vertical gradient of the third sampled line segment is calculated, here using the vertical gradient kernel. A convolution operation is performed on the position of the third sampled line segment on the SEM image. The vertical gradient at any point in the third sampled line segment after convolution is then calculated. It can be determined by the following formula:

[0117] ;(Formula 19)

[0118] ;(Formula 20)

[0119] in, The original grayscale image of the position of the third sampled line segment on the SEM image is used as the input for calculating the vertical gradient. Vertical gradient kernel.

[0120] Furthermore, the total gradient intensity at each point in the third sampling segment is calculated. The image gradient map of the third sampling line segment is composed of the total gradient intensity of each point in the third sampling line segment. The specific calculation formula is as follows:

[0121] ;(Formula 21)

[0122] Image edge points are selected from the image gradient map of each third sampling line segment using a preset boundary threshold. These edge points are then connected sequentially according to their adjacency to form the preliminary contour map corresponding to the SEM image. Specifically, when the total gradient intensity difference between two adjacent points in the image gradient map of the third sampling line segment exceeds the boundary threshold, the point with the higher total gradient intensity among the two adjacent points is identified as the image edge point of the corresponding third sampling line segment. The boundary threshold can be set as needed.

[0123] Furthermore, the preliminary contour image is smoothed: first, the preliminary contour image is rasterized, and then Gaussian blurring is applied to the rasterized contour image to obtain a rasterized contour image after Gaussian blurring. The Gaussian kernel (filter) size can be selected as 3x3 with a standard deviation of 1.0. In this embodiment, during the rasterization process of the preliminary contour image, the edges of diagonal lines or curves will produce obvious "step-like" jagged edges. A Gaussian blur with a 3x3 kernel and a standard deviation of 1.0 can slightly blend the grayscale values ​​of edge pixels and background pixels.

[0124] Furthermore, the foreground region (contour region) and background region of the rasterized image after Gaussian blurring show significant differences in grayscale values. This application uses the BlockScan algorithm to binarize the Gaussian-blurred rasterized image, obtaining a clear binary image suitable for contour extraction. The BlockScan algorithm refers to an adaptive thresholding binarization algorithm, which divides the image into several small blocks and calculates an optimal threshold for each block individually. In this embodiment, the optimal threshold is set to 125, but the setting of the optimal threshold is not limited.

[0125] It should be noted that the coordinate unit of the contour of the binary image is pixels, but for scientific measurement or engineering applications (such as semiconductor testing, precision manufacturing, etc.), pixel coordinates have no physical meaning. In order to convert the pixel-level binary image into a contour image at a physical scale, this application extracts the contour of the corresponding binary image based on the image size information of the SEM image to obtain the final contour image of the SEM image.

[0126] Furthermore, let the pixel grid size in the width direction of the SEM image be denoted as . The pixel grid size in the height direction of the SEM image is The coordinates of each point in the binary graph are: The process of extracting the contour of the binary image is as follows:

[0127] ;(Formula 22)

[0128] in, Let be any point in the final contour map.

[0129] This application addresses the technical bottleneck of poor contour extraction quality in SEM images due to fluctuations in the initial design layout position. It offers two methods to measure the quality of contours extracted from SEM images. The Layout-Scanline algorithm is used as a comparison; it is a classic model-based contour extraction method in semiconductor metrology. Its core idea is to use the design layout as a priori guide, actively emitting "scan lines" to detect edges in the real image.

[0130] (1) The error between the measured CD value and the true value of the contour at a given location is analyzed. As shown in Table 1, the same area in the test SEM image is extracted. Compared with the Layout-Scanline algorithm, this application significantly improves the measurement accuracy of CD at key locations by extracting the contour CD Diff value (nm). The CD Diff value refers to the difference between the key size value measured at the key location of the contour extracted from the SEM image using a certain algorithm and the reference value. The unit is nanometers (nm).

[0131] Table 1 Comparison of CD Accuracy

[0132]

[0133] (2) Analyze the EPE values ​​of the given location contour and design layout, as shown in Table 2. Take a batch of SEM images and their design layouts, and measure the EPE rms value using the image as the smallest unit. The contour extraction method described in this application can significantly reduce the EPE error. The EPE rms value is used to quantitatively evaluate the overall level of edge position deviation between the actual manufactured graphic and the design layout.

[0134] Table 2 Comparison of EPE rms values

[0135]

[0136] like Figure 6 The diagram illustrates the structure of a step system according to an embodiment of the present invention. The system 600 in this embodiment includes the following modules: a coarse alignment module 601, a layout circularization module 602, a preliminary positioning module 603, a repositioning module 604, and a contour acquisition module 605.

[0137] The coarse alignment module 601 is used to acquire the SEM image and the first image of the sample, and to coarsely align the first image and the SEM image using image similarity matching to obtain the second image.

[0138] The layout rounding module 602 is used to perform corner rounding processing on the second layout to obtain the third layout;

[0139] The preliminary positioning module 603 is used to select several points as first sampling points on the boundary line of the third map, obtain the corresponding brightness signal distribution in the SEM image along the normal direction of each first sampling point, and perform preliminary positioning of the third map based on the brightness signal distribution of each first sampling point to obtain the fourth map.

[0140] The repositioning module 604 is used to select several points as second sampling points on the boundary line of the fourth map, obtain the corresponding brightness signal distribution in the SEM map along the normal direction of each second sampling point, and reposition the corresponding second sampling point according to the brightness signal distribution of each second sampling point to obtain the third sampling point corresponding to each second sampling point.

[0141] The contour acquisition module 605 is used to search for the corresponding image edge points of each third sampling point in the SEM image, generate a preliminary contour map of the SEM image based on several image edge points, and smooth the preliminary contour map to obtain the final contour map.

[0142] It should be understood that the specific process of each module performing the above-mentioned steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0143] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0144] Figure 7 This is a schematic block diagram of the electronic terminal provided in an embodiment of this application. Figure 7 As shown, the electronic terminal includes at least one processor 701, a memory 702, at least one network interface 703, and a user interface 705. The various components in the device are coupled together via a bus system 704. It is understood that the bus system 704 is used to implement communication between these components. In addition to a data bus, the bus system 704 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in... Figure 7 The general will label all buses as bus systems.

[0145] The user interface 705 may include a monitor, keyboard, mouse, trackball, clicker, button, touchpad, or touch screen.

[0146] It is understood that memory 702 can be volatile memory or non-volatile memory, or both. Non-volatile memory can be read-only memory (ROM) or programmable read-only memory (PROM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM) and synchronous static random access memory (SSRAM). The memories described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable categories of memory.

[0147] In this embodiment of the invention, the memory 702 is used to store various types of data to support the operation of the electronic terminal 700. Examples of this data include: any executable program for operation on the electronic terminal 700, such as the operating system 7021 and application program 7022; the operating system 7021 contains various system programs, such as the framework layer, core library layer, driver layer, etc., for implementing various basic services and handling hardware-based tasks. The application program 7022 may contain various applications, such as a media player, browser, etc., for implementing various application services. The implementation of the automatic farmland irrigation method based on region division provided in this embodiment of the invention can be included in the application program 7022.

[0148] The methods disclosed in the above embodiments of the present invention can be applied to processor 701, or implemented by processor 701. Processor 701 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the integrated logic circuit of the hardware in processor 701 or by instructions in software form. The processor 701 may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Processor 701 can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. General-purpose processor 701 may be a microprocessor or any conventional processor, etc. The steps of the accessory optimization method provided in the embodiments of the present invention can be directly reflected as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module may be located in a storage medium, which is located in memory. The processor reads the information in the memory and combines it with its hardware to complete the steps of the aforementioned method.

[0149] In an exemplary embodiment, the electronic terminal 700 may be used to execute the aforementioned method by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), or complex programmable logic devices (CPLDs).

[0150] According to the method provided in the embodiments of this application, this application also provides a computer program product, which includes: computer program code, which, when run on a computer, causes the computer to execute a scanning electron microscope image contour extraction method based on peak signal relocation according to any of the embodiments shown.

[0151] According to the method provided in the embodiments of this application, this application also provides a computer-readable storage medium storing program code that, when run on a computer, causes the computer to perform a scanning electron microscope image contour extraction method based on peak signal relocation according to any of the embodiments shown.

[0152] As used in this specification, the terms "component," "module," "system," etc., are used to refer to computer-related entities, hardware, firmware, combinations of hardware and software, software, or software in execution. For example, a component can be, but is not limited to, a process running on a processor, a processor, an object, an executable file, an execution thread, a program, and / or a computer. As illustrated, applications running on computing devices and computing devices can both be components. One or more components may reside in a process and / or an execution thread, and components may be located on a single computer and / or distributed among two or more computers. Furthermore, these components can be executed from various computer-readable media on which various data structures are stored. Components can communicate, for example, via local and / or remote processes based on signals having one or more data packets (e.g., data from two components interacting with another component between a local system, a distributed system, and / or a network, such as the Internet interacting with other systems via signals).

[0153] Those skilled in the art will recognize that the various illustrative logical blocks and steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0154] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0155] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0156] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0157] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0158] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs, DVDs), or semiconductor media (e.g., solid-state disks, SSDs, etc.).

[0159] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0160] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0161] In summary, this application provides a method, system, medium, program product, and terminal for scanning electron microscope (SEM) image contour extraction based on peak signal relocation. The method involves acquiring the corresponding brightness signal distribution along the boundary line of the image layout, performing preliminary positioning of the layout based on the brightness signal, then relocating and searching along the boundary line of the initially positioned layout to obtain corresponding image edge points. A preliminary contour map of the SEM image is generated based on several image edge points, and the preliminary contour map is smoothed to obtain the final contour map. This effectively solves the problem of layout accuracy interference during scanning electron microscope image contour extraction.

[0162] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A method for contour extraction from scanning electron microscope images based on peak signal relocation, characterized in that, include: Obtain the SEM image and the first image of the sample. Use image similarity matching to coarsely align the first image and the SEM image to obtain the second image. The second version is rounded at the corners to obtain the third version; Several points are selected on the boundary line of the third map as first sampling points. The corresponding brightness signal distribution is obtained in the SEM image along the normal direction of each first sampling point. The third map is initially located based on the brightness signal distribution of each first sampling point to obtain the fourth map. Several points are selected on the boundary line of the fourth map as second sampling points. The corresponding brightness signal distribution is obtained in the SEM map along the normal direction of each second sampling point. The corresponding second sampling point is relocated according to the brightness signal distribution of each second sampling point to obtain the third sampling point corresponding to each second sampling point. For each third sampling point, a search is performed within the SEM image to obtain the corresponding image edge point. Based on several image edge points, a preliminary contour map of the SEM image is generated, and the preliminary contour map is smoothed to obtain the final contour map.

2. The scanning electron microscope image contour extraction method based on peak signal relocation according to claim 1, characterized in that, The corresponding luminance signal distribution is obtained in the SEM image along the normal direction of each first sampling point. Based on the luminance signal distribution of each first sampling point, the third image is initially located to obtain the fourth image. The process includes: The corresponding first sampling line segment is intercepted along the normal direction of each first sampling point using a fixed step size method; Obtain the brightness signal distribution of the first sampling line segment of each first sampling point in the SEM image, and obtain the peak point of each first sampling line segment based on the brightness signal distribution; The average distance is obtained by averaging the distances between several first sampling points and the peak points of the corresponding first sampling line segments. The third map is initially located based on the average distance to obtain the fourth map.

3. The method for contour extraction of scanning electron microscope images based on peak signal relocation according to claim 1, characterized in that, The corresponding luminance signal distribution is obtained in the SEM image along the normal direction of each second sampling point. The second sampling point is then relocated based on its luminance signal distribution to obtain the third sampling point corresponding to each second sampling point. This process includes: The corresponding second sampling line segment is intercepted along the normal direction of each second sampling point using a fixed step size method; Obtain the brightness signal distribution of the second sampling line segment of each second sampling point in the SEM image, and obtain the peak point of each second sampling line segment based on the brightness signal distribution; Each second sampling point is moved to the peak point of the corresponding second sampling line segment to obtain the third sampling point corresponding to each second sampling point.

4. The method for contour extraction of scanning electron microscope images based on peak signal relocation according to claim 1, characterized in that, The process of rounding corners in the second version of the drawing includes: selecting the inflection points in the second version of the drawing and converting the included angles corresponding to the inflection points into rounded corners.

5. The method for contour extraction of scanning electron microscope images based on peak signal relocation according to claim 1, characterized in that, The process of coarsely aligning the first image with the SEM image using image similarity matching to obtain the second image includes: The first map was divided into several sub-maps; Calculate the structural similarity index between the SEM image and each sub-image. Select the sub-image with the highest structural similarity index from among the structural similarity indices as the target sub-image. The target sub-image is the second version image.

6. The scanning electron microscope image contour extraction method based on peak signal relocation according to claim 1, characterized in that, The process of searching for the corresponding image edge point within the SEM image for each third sampling point includes: The normal direction of each second sampling point in the fourth version of the drawing is taken as the normal direction of the corresponding third sampling point; The corresponding third sampling line segment is intercepted along the normal direction of each third sampling point using a fixed step size method; The corresponding image gradient map is obtained by using an edge detection operator based on the position of the third sampling line segment of each third sampling point in the SEM image; Image edge points for each third sampling point are obtained by filtering the image gradient map of each third sampling line segment.

7. A scanning electron microscope image contour extraction system based on peak signal relocation, characterized in that, The system includes: The coarse alignment module is used to acquire the SEM image and the first image of the sample, and to coarsely align the first image with the SEM image using image similarity matching to obtain the second image. The map rounding module is used to round the corners of the second map to obtain the third map; The preliminary positioning module is used to select several points as first sampling points on the boundary line of the third map, obtain the corresponding brightness signal distribution in the SEM image along the normal direction of each first sampling point, and perform preliminary positioning of the third map based on the brightness signal distribution of each first sampling point to obtain the fourth map. The relocation module is used to select several points as second sampling points on the boundary line of the fourth map, obtain the corresponding brightness signal distribution in the SEM map along the normal direction of each second sampling point, and relocate the corresponding second sampling point according to the brightness signal distribution of each second sampling point to obtain the third sampling point corresponding to each second sampling point. The contour acquisition module is used to search for the corresponding image edge points of each third sampling point in the SEM image, generate a preliminary contour map of the SEM image based on several image edge points, and smooth the preliminary contour map to obtain the final contour map.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the scanning electron microscope image contour extraction method based on peak signal relocation as described in any one of claims 1 to 6.

9. A computer program product, characterized in that, The computer program product includes computer program code, which, when run on a computer, causes the computer to implement the scanning electron microscope image contour extraction method based on peak signal relocation as described in any one of claims 1 to 6.

10. An electronic terminal, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the scanning electron microscope image contour extraction method based on peak signal relocation as described in any one of claims 1 to 6.

Images