Wafer calibration method and device and storage medium

Abstract

The invention discloses a wafer calibration method and device and a storage medium, and relates to the technical field of semiconductor integrated circuits. The method comprises the following steps: respectively acquiring edge region images of a plurality of preset edge positions on a wafer to be calibrated to obtain a plurality of edge region images; for each marginal region image, extracting a gray level distribution curve of pixel points along the corresponding sampling direction of the marginal region image; determining spatial positions of a plurality of edge feature points in each edge region image according to a plurality of gradients of the gray level distribution curve; performing circumference fitting on the edge feature points corresponding to the same gradient in the plurality of edge region images to obtain a circle center position of a fitting circle corresponding to each gradient; and performing circle center position fitting according to the circle center positions of the fitting circles corresponding to the multiple gradients, and outputting a target circle center position of the wafer to be calibrated under the condition that a fitting result meets a convergence condition. According to the method, the circle centers of the concentric circles are fitted and positioned based on the wafer edge features, and accurate calibration of the wafer can be realized.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of semiconductor integrated circuits, and particularly relates to a wafer calibration method, device and storage medium. BACKGROUND

[0002] In the semiconductor manufacturing process, the accurate alignment of a bare wafer is a key link of measurement and process control. At present, positioning is mostly performed depending on natural features such as the edge of the wafer, because the bare wafer lacks alignment marks on the process layer.

[0003] However, because the wafer edge has a complex three-dimensional structure, there is still a problem of large error and insufficient precision in edge recognition, whether high-magnification or low-magnification imaging is used, which further affects the accurate positioning of the bare wafer. SUMMARY

[0004] The embodiments of the present application provide a wafer calibration method, device and storage medium, which can effectively improve the precision and stability of bare wafer calibration.

[0005] In a first aspect, the embodiments of the present application provide a wafer calibration method, comprising: collecting edge region images of a plurality of preset edge positions on a wafer to be calibrated respectively to obtain a plurality of edge region images; for each edge region image, extracting a gray scale distribution curve of a pixel point along a corresponding sampling direction of the edge region image; determining the spatial positions of a plurality of edge feature points in each edge region image according to a plurality of gradients of the gray scale distribution curve, one edge feature point corresponding to each gradient; performing circumferential fitting on the edge feature points corresponding to the same gradient in the plurality of edge region images to obtain parameters of a fitting circle corresponding to each gradient, the parameters of the fitting circle including a fitting circle center position; performing center position fitting on the fitting circle center positions corresponding to the plurality of gradients, and outputting a target circle center position of the wafer to be calibrated in a case where the fitting result meets a convergence condition.

[0006] In a second aspect, the embodiments of the present application provide a wafer calibration device, comprising: an image collection module configured to collect edge region images of a plurality of preset edge positions on a wafer to be calibrated respectively to obtain a plurality of edge region images; a sampling analysis module configured to, for each edge region image, extract a gray scale distribution curve of a pixel point along a corresponding sampling direction of the edge region image; a feature point determination module configured to determine the spatial positions of a plurality of edge feature points in each edge region image according to a plurality of gradients of the gray scale distribution curve, one edge feature point corresponding to each gradient; The concentric circle fitting module is configured to perform circle fitting on the edge feature points corresponding to the same gradient in the plurality of edge region images, to obtain parameters of a fitting circle corresponding to each gradient, the parameters of the fitting circle including a position of a center of the fitting circle; The center fitting module is configured to perform center position fitting on the positions of the centers of the fitting circles corresponding to the plurality of gradients, and output a target center position of the wafer to be calibrated when a fitting result meets a convergence condition.

[0007] In a third aspect, an electronic device is provided. The device includes a memory and a program or instructions stored on the memory and executable on a processor, and the program or instructions, when executed by the processor, implement the method provided in any one of the aspects of the embodiments of the present application.

[0008] In a fourth aspect, a readable storage medium is provided. The readable storage medium stores a program or instructions, and the program or instructions, when executed by a processor, implement the method provided in any one of the aspects of the embodiments of the present application.

[0009] In a fifth aspect, a computer program product is provided. The instructions in the computer program product, when executed by a processor of an electronic device, cause the electronic device to perform the method provided in any one of the aspects of the embodiments of the present application.

[0010] In the wafer calibration method provided in the embodiments of the present application, edge region images of a wafer to be calibrated are collected at a plurality of predetermined edge positions, and a plurality of gradients in each image are extracted based on gray scale distribution analysis, wherein each gradient corresponds to a feature point of the edge of the wafer. Subsequently, the feature points corresponding to the same gradient in the plurality of edge region images are subjected to circle fitting, to obtain fitting circles corresponding to the plurality of gradients; and further center fitting is performed on the center positions of the fitting circles of different gradients, to accurately determine a target center position of the wafer. The method does not depend on process layer alignment marks, and can be directly applied to positioning and calibration of a bare wafer, and has a wider range of application. On this basis, by introducing a redundant verification mechanism of multiple edge positions, multiple images and multiple gradients, not only is the interference of noise, local defects and imaging errors effectively suppressed, but also the stability and precision of the center fitting are significantly improved. BRIEF DESCRIPTION OF DRAWINGS

[0011] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required in the embodiments of the present application will be briefly introduced. Those skilled in the art can obtain other drawings based on these drawings without creative labor.

[0012] Figure 1 FIG. 1 is a flowchart of a wafer calibration method provided in an embodiment of the present application; Figure 2is a schematic diagram of an edge region image provided by an embodiment of the present application; Figure 3 is a schematic diagram of a gray scale distribution curve provided by an embodiment of the present application; Figure 4 is a schematic diagram of a plurality of gray scale distribution sub-curves provided by an embodiment of the present application; Figure 5 is a schematic diagram of a wafer calibration method provided by an embodiment of the present application; Figure 6 is a schematic diagram of a wafer calibration method provided by an embodiment of the present application; Figure 7 is a schematic diagram of a wafer calibration device provided by an embodiment of the present application; Figure 8 is a schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0013] The features and exemplary embodiments of the various aspects of the present application will be described in detail below with reference to the drawings. The following detailed description is merely intended to explain the present application, and is not intended to limit the present application. The present application can be implemented without some of the specific details, which are well known to those skilled in the art. The following description of the embodiments is merely intended to provide a better understanding of the present application through showing examples of the present application.

[0014] It should be noted that, in this document, the terms such as first and second are merely used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device. Without more limitations, the elements defined by the statement "include" do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0015] It should be noted that the acquisition, storage, use, processing and the like of data in the technical solutions of the present application comply with relevant provisions of national laws and regulations. In the embodiments of the present application, some industry existing solutions such as software, components, models and the like may be mentioned, which should be considered as exemplary, and the purpose is only to illustrate the feasibility in the implementation of the technical solutions of the present application, but it does not mean that the applicant has or will necessarily use the solutions.

[0016] Firstly, the technical terms related to one or more embodiments of the present application are explained.

[0017] A bare wafer refers to a wafer whose surface has not been processed to form any pattern structure.

[0018] As described in the background section, in the semiconductor manufacturing process, the positioning accuracy of the wafer on various metrology machines (such as defect detection machines, film thickness measurement instruments, overlay error measurement systems, etc.) is directly related to the reliability of process control.

[0019] However, unlike wafers that have formed process layers, bare wafers lack alignment marks on their surfaces, so current technologies often rely on their natural features for positioning recognition, such as calibrating and positioning the wafer edge through image recognition.

[0020] However, since the wafer edge is actually a complex three-dimensional structure, whether high-magnification imaging or low-magnification imaging is used for recognition, it is easy to be disturbed by the edge topography change, resulting in difficulty in extracting edge features, which is specifically manifested in the need to collect and process a large number of color images, low processing efficiency, and difficulty in ensuring edge recognition accuracy, thereby affecting the reliability of the overall positioning.

[0021] Therefore, the present application provides a wafer calibration method, device and storage medium. In the wafer calibration method provided by the embodiments of the present application, wafer edge images are first collected and edge feature points are extracted therefrom; then a plurality of concentric circles are fitted based on the edge feature points; and on this basis, further center fitting is performed to finally determine the center position of the wafer, thereby realizing accurate calibration of the bare wafer.

[0022] For example, the wafer calibration method provided by the embodiments of the present application can be applied to the production line of a semiconductor manufacturing enterprise, and used for accurate calibration and positioning of the bare wafer involved in the production process. In actual application, before the bare wafer enters the subsequent process steps (such as photolithography, deposition, etching, detection, etc.), the position of the wafer on the operation platform can be calibrated by using the method of the present application to ensure that the placement position of the wafer on the machine is consistent with the standard position required by the process flow, thereby improving the stability and reliability of the process treatment.

[0023] It should be noted that the application scenarios described in the above embodiments of the present application are for more clearly illustrating the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application. Those skilled in the art can know that, with the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are also applicable to similar technical problems. The wafer calibration method provided by the embodiments of the present application can be applied to various application scenarios that need to calibrate a wafer.

[0024] The image defect classification method provided by the embodiments of the present application will be introduced below. In actual application, the execution subject of the wafer calibration method of the embodiments of the present application can be an electronic device.

[0025] The specific embodiments of the wafer calibration method, device, equipment, medium and product provided by the embodiments of the present application will be introduced below. First, the wafer calibration method will be introduced.

[0026] Figure 1 A flowchart of the wafer calibration method provided by an embodiment of the present application is shown. As shown in Figure 1 , the method comprises steps S110 to S150.

[0027] S110, respectively collecting edge region images of a plurality of preset edge positions on a wafer to be calibrated, to obtain a plurality of edge region images.

[0028] The wafer to be calibrated refers to a wafer that needs to be calibrated.

[0029] It should be noted that, based on the description in the background section, the wafer to be calibrated in the present application is mainly for bare wafers, which will not be emphasized below.

[0030] At each of the plurality of preset edge positions on the wafer to be calibrated, an edge region image corresponding to the preset edge position is collected respectively. That is, an edge region image is collected for each preset edge position.

[0031] It should be understood that, in order to further improve the recognition accuracy, in one possible implementation, a plurality of images can be collected at the same preset edge position, and a more stable edge region image can be obtained through image merging processing. The present application does not expand the description of such variants.

[0032] The edge region image refers to a local image covering the preset edge position of the wafer. It should be understood that the field of view of the edge region image not only includes part of the wafer, but also includes the background area outside it. For example, as shown in Figure 2 , in a schematic example of an edge region image, the line segment AB in the rectangular field of view can be regarded as the edge of the wafer, and the left side of the line segment AB is the background area and the right side is part of the wafer.

[0033] The preset edge position is an edge position of the wafer selected for image acquisition, for example, an edge position corresponding to a direction or an angle of the wafer.

[0034] It should be noted that the edge position can be roughly set according to the shape characteristics of the wafer to be calibrated, or can be more accurate point positions obtained through a large number of image acquisition in the early experiment combined with complex analysis.

[0035] In some embodiments, the wafer edge circumference can be finely divided to obtain n edge positions; a large number of wafer images can be collected at the n edge positions, and the center position can be predicted based on the image analysis; the predicted center position is compared with the actual center position, and the position whose prediction accuracy meets the preset condition is selected from the n edge positions as the preset edge position. In a possible implementation manner, the wafer image corresponding to each edge position can be collected through multiple imaging modes, for example, collected under different magnifications (such as 5 times, 10 times or 20 times), or collected by using bright field and dark field imaging modes. Exemplarily, the preset condition can be set as that the offset of the predicted center position is not more than ±0.5 μm.

[0036] However, directly analyzing a large number of images is not only complex, but also has a large amount of calculation. In contrast, the present application directly designs a calibration scheme based on the preset edge positions with empirical significance, which can significantly reduce the processing complexity while ensuring accuracy. Since the preset edge position is selected based on empirical analysis, compared with an arbitrarily selected edge position, the accuracy and stability of wafer positioning can be more effectively improved.

[0037] S120, for each edge region image, extracting a gray scale distribution curve of a pixel point along a sampling direction corresponding to the edge region image.

[0038] The gray scale distribution curve (profile) is a distribution curve obtained by statistically and visually processing the gray scale values of the pixel points along the sampling direction of the edge region image. In the embodiments of the present application, the plot algorithm can be used to generate the gray scale distribution curve, that is, the function curve of the change of the gray scale values of the pixel points with the pixel positions is sequentially drawn according to the sampling direction.

[0039] It should be understood that the sampling direction of each edge region image is not fixed and consistent, but is set according to the preset edge position corresponding to the image. Exemplarily, the sampling direction can be set as a direction from the inside of the wafer to the outside, so as to cross the edge region of the wafer, thereby being able to completely capture the gray scale change at the edge.

[0040] However, in another possible implementation manner, each edge region image can be rotated to a unified direction (for example, the direction from the inside of the wafer to the outside) first, and then the gray scale distribution curve of the pixel points along the sampling direction can be extracted. Figure 2In the case shown in FIG. 2, the sampling direction of each edge region image can be kept consistent, for example, both being horizontal direction.

[0041] Exemplarily, the point set of the vertical direction line and the point set of the horizontal direction line can be respectively calculated in a unified direction, and the obtained point sets are subjected to outlier and abnormal point detection and elimination operation based on a random sample consensus (RANSAC) algorithm, so as to effectively remove noise points or interference caused by local imaging abnormalities. Subsequently, curve fitting is performed on the retained effective point set, so as to obtain a more smooth and reliable gray distribution curve. Exemplarily, the abnormal points can be high noise points.

[0042] In an implementation, the step S120 can be implemented by the following steps.

[0043] S121, in each edge region image, a plurality of pixel sequences are extracted along the sampling direction corresponding to the edge region image, and a plurality of gray distribution sub-curves corresponding to the plurality of pixel sequences are generated.

[0044] That is, a pixel sequence corresponds to draw a gray distribution sub-curve.

[0045] For example, when the sampling direction is set to horizontal direction, a plurality of pixel sequences can be extracted from the edge region image; when the sampling direction is set to vertical direction, a plurality of pixel sequences can be extracted, thereby obtaining a plurality of corresponding gray distribution sub-curves.

[0046] S122, fitting the plurality of gray distribution sub-curves to obtain a gray distribution curve.

[0047] For example, the least square method can be used to fit the plurality of gray distribution sub-curves in the same sampling direction, and the gray variation trend of each sub-curve is weighted and smoothed, so as to obtain a representative gray distribution curve.

[0048] In the implementation, by extracting a plurality of pixel gray in the edge region image along the sampling direction and fitting the comprehensive curve obtained, the noise interference possibly existing in a single sub-curve can be eliminated to a certain extent, and the reliability and stability of the edge feature point extraction are improved by means of the redundant information of the plurality of sub-curves.

[0049] Figure 3 A schematic diagram of the gray distribution curve provided by the embodiment of the application is shown. Wherein, the abscissa represents the pixel position, and the ordinate represents the gray value. From the left to the right of the abscissa, the pixel position is from left to right, and the pixel position is from top to bottom. Figure 3It can be seen that the gray scale distribution curve corresponding to the wafer edge position presents obvious gray scale change at the position. Further, it can be understood that the gradient of the gray scale distribution curve can represent the position of the wafer edge, that is, the gray scale change amplitude is the largest at the wafer edge, so that the wafer edge feature can be accurately identified.

[0050] S130, according to the plurality of gradients of the gray scale distribution curve, the spatial positions of the plurality of edge feature points in each edge region image are determined, and each gradient corresponds to determine an edge feature point.

[0051] Gradient refers to the gray scale gradient in the gray scale distribution curve.

[0052] Specifically, a plurality of gradient values can be calculated and extracted in each gray scale distribution curve. Since there can be a large number of gradient points in the curve, in order to avoid redundancy and noise interference, only a few representative gradient values can be selected, such as the maximum value, half of the maximum value, one-third of the maximum value, etc.

[0053] Subsequently, the pixel point corresponding to each selected gradient is obtained, and the spatial position of the pixel point is taken as the corresponding edge feature point.

[0054] Therefore, from the gray scale distribution curve of each edge region image, a plurality of edge feature points corresponding to the preset number of gray scale gradients can be extracted, that is, under each preset gradient, the position of an edge feature point can be determined.

[0055] S140, the edge feature points corresponding to the same gradient in the plurality of edge region images are circularly fitted to obtain the parameters of the fitting circle corresponding to each gradient.

[0056] Under the same gradient, the edge feature points extracted from different edge region images can be collectively fitted into a circular curve. That is, one gradient corresponds to one fitting circle; when there are a plurality of gradients, a plurality of fitting circles can be obtained. It can be understood that the plurality of fitting circles are in concentric relationship.

[0057] In the circular fitting process, the parameters of each fitting circle are calculated, including but not limited to the center position of the fitting circle and the radius of the fitting circle.

[0058] S150, according to the center positions of the fitting circles corresponding to the plurality of gradients, the center position fitting is performed, and in the case that the fitting result meets the convergence condition, the target center position of the wafer to be calibrated is output.

[0059] The center positions of the fitting circles corresponding to the plurality of gradients can be taken as input, and the final target center position is calculated through the center position fitting. When the change of the center position in the fitting process meets the preset convergence condition, it can be determined that the fitting is completed, and the determined target center position is output as the calibration result of the wafer to be calibrated.

[0060] Exemplarily, the convergence condition can be set as that the deviation between the positions of the centers of the plurality of fitting circles is less than a preset threshold. The preset threshold can be set according to actual accuracy requirements, for example, can be set to 0.2 mm.

[0061] The wafer calibration method provided in the application scheme comprises the following steps: collecting edge region images of a wafer to be calibrated at a plurality of edge positions, extracting a plurality of gradients in each image based on gray scale analysis, wherein each gradient reflects an edge feature of the wafer, performing circumference fitting on the same gradient in a plurality of images, and performing center fitting on the fitting circles corresponding to different gradients, so as to obtain a target center position of the wafer. This method does not depend on process layer alignment marks and is suitable for bare wafer positioning. In addition, through multi-image and multi-gradient redundancy verification, the stability and accuracy of the center positioning are significantly improved.

[0062] To improve the fitting accuracy and efficiency, in one embodiment, fitting a plurality of gray scale distribution sub-curves to obtain a gray scale distribution curve can comprise the following steps: S121, performing consistency analysis on the plurality of gray scale distribution sub-curves.

[0063] In an implementation manner, the median of each sub-curve can be calculated and compared with the median of the overall distribution to determine whether it deviates from the overall trend.

[0064] In an implementation manner, the similarity between the plurality of sub-curves can be calculated based on the similarity, so as to identify the curves that are too different from most of the sub-curves.

[0065] Through the consistency analysis, the distorted sub-curves caused by noise interference or abnormal edges can be effectively identified.

[0066] S122, removing the gray scale distribution sub-curves that do not meet the consistency requirement.

[0067] The process of removing abnormal curves can reduce the interference of noise or local abnormal pixels on the final fitting result, so as to ensure that the input data for subsequent fitting is more pure and stable.

[0068] S123, fitting the gray scale distribution sub-curves that meet the consistency requirement into a gray scale distribution curve.

[0069] Figure 4 A schematic diagram of a plurality of gray scale distribution sub-curves is shown.

[0070] In an implementation, step S123 can be further refined as: determining at least one group of pixel sequences corresponding to the sub-curve of the gray scale distribution that meets the consistency requirement; and performing line segment fitting on the at least one group of pixel sequences by using the least square method to obtain the gray scale distribution curve. The fitting method based on the least square method can not only effectively reduce the influence of individual outliers on the result, but also improve the overall smoothness and fitting accuracy of the curve.

[0071] In this embodiment, by performing consistency analysis on the sub-curve and eliminating abnormal curves, and then performing fitting by using the least square method, the overall shape of the curve can be ensured to be real while the interference of local abnormalities is effectively inhibited, so that the finally obtained gray scale distribution curve is smoother and more accurate.

[0072] In order to make the target center position obtained by the above scheme more accurate, in an embodiment, the present application further designs a retest verification link. Figure 5 A wafer calibration method provided by an embodiment of the present application is shown, which includes the following steps.

[0073] S510, based on the target center position and the fitting circle radius corresponding to each gradient, the predicted position of the unsampled pixel point on each gradient is calculated.

[0074] The unsampled pixel point refers to a pixel point that is not used for drawing the gray scale distribution curve in step S120.

[0075] In a possible implementation, one or more unsampled pixel points can be positioned on each gradient to test the prediction accuracy.

[0076] S520, the position deviation of the actual position and the predicted position of the unsampled pixel point is calculated, and the first gradient is selected from the multiple gradients according to the position deviation.

[0077] The position deviation of the actual position and the predicted position of the unsampled pixel point is calculated, and the first gradient and the second gradient are determined according to the position deviation.

[0078] The first gradient is a normal gradient with a deviation meeting the expectation, and can include one or more gradients. The second gradient is an abnormal gradient with a deviation exceeding the allowable range, and can also include one or more gradients.

[0079] In an implementation, when only one unsampled pixel point is positioned on each gradient, the gradient with a position deviation between the actual position and the predicted position of the pixel point less than a deviation threshold can be determined as the first gradient, and vice versa.

[0080] In an implementation, when each gradient locates multiple unsampled pixel points, the number of unsampled pixel points with a position deviation between the actual position and the predicted position less than a deviation threshold can be counted, and when the number reaches or exceeds a preset threshold, the corresponding gradient is determined as the first gradient, otherwise as the second gradient.

[0081] Exemplarily, the deviation threshold can be set according to actual needs, for example, can be set as a position corresponding to 1 pixel.

[0082] S530, continue to fit the center position of the circle according to the first gradient corresponding fitting circle center position, and update the target circle center position.

[0083] By eliminating abnormal second gradients, only using the high-credibility fitting circle center corresponding to the first gradient to re-perform the circle center position fitting in step S150, a new target circle center position is obtained, which can further improve the accuracy and robustness of the circle center position calculation.

[0084] The method of the embodiment, based on the target circle center obtained by convergence and the fitting circle radius corresponding to each gradient, reversely deduces the boundary position in the original image for re-measurement verification, effectively identifies and eliminates abnormal gradients, thereby optimizing the target circle center position and improving the accuracy and stability of the circle center positioning.

[0085] For each second gradient, further correction processing can be performed. In an embodiment, the method of the application can further include: For each second gradient, update the unsampled pixel points, and calculate the position deviation between the predicted position and the actual position of the updated unsampled pixel points based on the target circle center position and the fitting circle radius corresponding to the second gradient; in the case that the position deviation does not exceed the deviation threshold, the second gradient is converted into the first gradient.

[0086] It should be understood that the "update" refers to replacing the unsampled pixel points corresponding to the gradient preliminarily determined as the second gradient. For example, new pixel points can be selected near the original pixel points, or pixel points can be re-extracted with different intervals within the same gradient range.

[0087] The embodiment introduces a "re-measurement" mechanism to re-verify the second gradient preliminarily determined as abnormal. By replacing the test pixel points, the pseudo-abnormal situation caused by local noise, defects or imaging errors can be effectively identified, and these data that should normally belong to normal data are corrected and restored. In this way, not only the problem of reducing the fitting accuracy due to excessive data elimination is avoided, but also the deviation of the circle center positioning result caused by individual abnormal data is prevented, thereby further improving the calculation reliability and stability of the target circle center position.

[0088] In one embodiment, in the case that the number of the first gradients is lower than the number threshold, a compensatory calibration process can be triggered, which can include the following steps: re-acquiring a plurality of edge region images on the wafer to be calibrated, and fitting a new target circle center position based on the re-acquired plurality of edge region images, iteratively performing until the number of the first gradients corresponding to the fitted target circle center position reaches the number threshold, and outputting the corresponding target circle center position.

[0089] The embodiment introduces image re-acquisition and iterative fitting mechanism when the number of the first gradients is insufficient, realizes dynamic supplement and optimization of abnormal gradients or insufficient gradients. On the one hand, it can avoid calculation deviation caused by insufficient sampling, and on the other hand, it can also gradually converge to a stable circle center position through multiple iterations. The scheme compensates for data loss by actively re-acquiring, enhances the fault tolerance to abnormal conditions, significantly improves the accuracy and robustness of the circle center positioning, and ensures the reliability and stability of the wafer calibration result under various working conditions.

[0090] Figure 6 A flowchart of a wafer calibration method provided by an embodiment of the application is shown. It should be understood that, Figure 6 The embodiment can be regarded as Figure 1 One example of the embodiment includes steps S110-S150 and S610-S660.

[0091] Steps S110-S150 are described in detail in Figure 1 The introduction of the embodiment will not be repeated here. After obtaining the target circle center position, the following steps are continued.

[0092] S610, based on the target circle center position and the fitting circle radius corresponding to each gradient, the predicted position of the unsampled pixel point on each gradient is calculated.

[0093] S620, the position deviation of the actual position and the predicted position of the unsampled pixel point is calculated, and the plurality of gradients is divided into the first gradient and the second gradient according to the position deviation.

[0094] Specifically, the gradient with a position deviation less than a deviation threshold is determined as the first gradient, and the gradient with a position deviation greater than the deviation threshold is determined as the second gradient.

[0095] S630, the number of the first gradients is counted, and in the case that the number of the first gradients reaches the number threshold, the circle center position fitting is continued according to the parameters of the fitting circle corresponding to the first gradient, and the target circle center position is updated.

[0096] S640, in a case where the number of the first gradients is lower than the number threshold, reacquiring the plurality of edge region images on the wafer to be calibrated and re-fitting a new target center position, iteratively performing until the number of the first gradients corresponding to the target center position obtained by fitting reaches the number threshold, and outputting the corresponding target center position.

[0097] S650, for each second gradient, updating the unsampled pixel points of the second gradient, and calculating a position deviation between a predicted position and an actual position of the updated unsampled pixel points based on the target center position and a fitting circle radius corresponding to the second gradient.

[0098] S660, in a case where the position deviation does not exceed the deviation threshold, converting the second gradient into the first gradient.

[0099] Through the above scheme, the fitting accuracy of different gradients is dynamically evaluated in the wafer center positioning process, and the gradients are processed based on the position deviation: when most of the gradients meet the accuracy requirement, the gradient data with high confidence can be directly used for wafer center updating, thereby improving the fitting accuracy and stability; when the fitting result does not meet the requirement, reacquisition and iterative optimization can be automatically triggered to effectively avoid error accumulation caused by local abnormality or insufficient sampling. At the same time, by gradually correcting the second gradient and converting it into the first gradient, adaptive correction of abnormal gradients is realized, thereby significantly improving the robustness and reliability of wafer center positioning.

[0100] Based on the wafer calibration method. Accordingly, specific embodiments of a wafer calibration device are also provided.

[0101] Figure 7 A structure schematic diagram of a wafer calibration device provided by an embodiment of the present application is shown.

[0102] As Figure 7 shown, the wafer calibration device 1000 provided by the embodiment of the present application can include the following modules.

[0103] An image acquisition module 1001 is configured to acquire edge region images of a plurality of preset edge positions on a wafer to be calibrated respectively, and obtain the plurality of edge region images.

[0104] A sampling analysis module 1002 is configured to, for each edge region image, extract a gray scale distribution curve of pixel points along a sampling direction corresponding to the edge region image.

[0105] A feature point determination module 1003 is configured to determine spatial positions of a plurality of edge feature points in each edge region image according to a plurality of gradients of the gray scale distribution curve, and determine one edge feature point corresponding to each gradient.

[0106] The concentric circle fitting module 1004 is configured to perform circle fitting on the edge feature points corresponding to the same gradient in the plurality of edge region images to obtain parameters of the fitting circle corresponding to each gradient. The parameters of the fitting circle include a center position of the fitting circle The center fitting module 1005 is configured to perform center position fitting on the center positions of the fitting circles corresponding to the plurality of gradients, and output a target center position of the wafer to be calibrated when the fitting result meets a convergence condition.

[0107] The wafer calibration method is based on a wafer calibration method. Accordingly, specific embodiments of an electronic device are also provided.

[0108] Figure 8 A hardware structure schematic diagram of the electronic device provided by the embodiments of the present application is shown.

[0109] The electronic device can include a processor 7001 and a memory 7002 storing computer program instructions.

[0110] Specifically, the processor 7001 can include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0111] The memory 7002 can include a mass storage for data or instructions. By way of example and not limitation, the memory 7002 can include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive or a combination of two or more of these. Where appropriate, the memory 7002 can include removable or non-removable (or fixed) media. Where appropriate, the memory 7002 can be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, the memory 7002 is a non-volatile solid-state memory.

[0112] The processor 7001 reads and executes the computer program instructions stored in the memory 7002 to implement any of the wafer calibration methods in the above embodiments.

[0113] In one example, the electronic device can also include a communication interface 7003 and a bus 7004. As shown, the processor 7001, the memory 7002, and the communication interface 7003 are connected through the bus 7004 and complete communication among each other. Figure 5

[0114] ​The communication interface 7003 is mainly configured to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0115] Bus 7004 includes hardware, software, or both, that couples components of the electronic device to each other. As an example and not by way of limitation, the bus can include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand (IB) interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or another suitable bus or a combination of two or more of these. Where appropriate, bus 7004 can include one or more buses. Although the present application is described and shown with respect to particular bus, the present application contemplates any suitable bus or interconnect.

[0116] In addition, in combination with the image defect classification method in the above embodiments, the embodiments of the present application can provide a computer storage medium for implementation. The computer storage medium has computer program instructions stored thereon; the computer program instructions are executed by a processor to implement any of the wafer calibration methods in the above embodiments.

[0117] In addition, in combination with the wafer calibration method in the above embodiments, the embodiments of the present application can provide a computer program product for implementation, and the instructions in the computer program product are executed by the processor of the electronic device to make the electronic device execute the wafer calibration method provided by any aspect of the above embodiments of the present application.

[0118] It needs to be clear that the present application is not limited to the specific configurations and processes described above and shown in the drawings. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of the present application is not limited to the specific steps described and shown, and those skilled in the art can make various changes, modifications and additions, or change the order between steps, after understanding the spirit of the present application.

[0119] The functions noted in the description of the structural block diagrams above can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, and the like. When implemented in software, the elements of the present application are program or code segments that are used to perform the required tasks. The program or code segments can be stored in a machine-readable medium, or transmitted through a data signal carried in a carrier wave over a transmission medium or communication link. A "machine-readable medium" includes any medium that can store or transport information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like. The code segments can be downloaded via computer networks such as the Internet, intranets, and the like.

[0120] It is also important to note that the examples mentioned in the present application describe some methods or systems based on a series of steps or devices. However, the present application is not limited to the order of the steps mentioned above, that is, the steps can be performed in the order mentioned in the examples, or in an order different from the examples, or several steps can be performed simultaneously.

[0121] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer program instructions can also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other processing devices to operate in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks. The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer program instructions can also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other processing devices to operate in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function / act specified in the flowchart and / or block diagram block or blocks.

[0122] The above merely describes a specific implementation of the present application. Those skilled in the art can clearly understand the specific working processes of the system, modules and units described above for the convenience and brevity of description, and can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein again. It should be understood that the protection scope of the present application is not limited to this, and any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application.

Claims

1. A wafer calibration method, characterized in that, include: Multiple edge region images are obtained by acquiring edge region images at multiple preset edge locations on the wafer to be calibrated. For each edge region image, extract the grayscale distribution curve of the pixels along the corresponding sampling direction of that edge region image; Based on the multiple gradients of the grayscale distribution curve, the spatial locations of multiple edge feature points in each edge region image are determined, with each gradient corresponding to one edge feature point. For the edge feature points corresponding to the same gradient in the multiple edge region images, perform circumferential fitting to obtain the parameters of the fitting circle corresponding to each gradient. The parameters of the fitting circle include the position of the center of the fitting circle. The center position of the fitted circle is fitted according to the multiple gradients. If the fitting result meets the convergence condition, the target center position of the wafer to be calibrated is output.

2. The method according to claim 1, characterized in that, For each edge region image, extracting the grayscale distribution curve of pixels along the corresponding sampling direction of that edge region image includes: In each edge region image, multiple sets of pixel sequences are extracted along the sampling direction corresponding to the edge region image, and multiple gray-level distribution sub-curves corresponding to the multiple sets of pixel sequences are generated; The grayscale distribution curve is obtained by fitting the multiple grayscale distribution sub-curves.

3. The method according to claim 2, characterized in that, The process of fitting the multiple grayscale distribution sub-curves to obtain the grayscale distribution curve includes: A consistency analysis was performed on the multiple grayscale distribution sub-curves. Remove grayscale distribution sub-curves that do not meet the consistency requirements; The grayscale distribution sub-curve that meets the consistency requirement is fitted to the grayscale distribution curve.

4. The method according to claim 3, characterized in that, The step of fitting the grayscale distribution sub-curve that meets the consistency requirement to the grayscale distribution curve includes: Determine at least one set of pixel sequences corresponding to the grayscale distribution sub-curve that meets the consistency requirement; The least squares method is used to fit line segments to the at least one set of pixel sequences to obtain the grayscale distribution curve.

5. The method according to any one of claims 1 to 4, characterized in that, The parameters of the fitted circle also include the radius of the fitted circle, and the method further includes: Based on the target circle center position and the fitted circle radius corresponding to each gradient, calculate the predicted position of the unsampled pixel point on each gradient; Calculate the positional deviation between the actual position of the unsampled pixel and its corresponding predicted position, and select the first gradient from the plurality of gradients based on the positional deviation; Based on the first gradient corresponding to the center position of the fitted circle, continue to fit the center position and update the target center position.

6. The method according to claim 5, characterized in that, The method further includes: If the number of the first gradients is lower than the number threshold, multiple edge region images on the wafer to be calibrated are re-acquired, and a new target center position is fitted based on the re-acquired multiple edge region images. This process is repeated iteratively until the number of the first gradients corresponding to the fitted target center position reaches the number threshold, and the corresponding target center position is output.

7. The method according to claim 5, characterized in that, The method further includes: The gradient where the positional deviation exceeds the deviation threshold is determined as the second gradient; For each second gradient, the unsampled pixel is updated, and based on the target circle center position and the radius of the fitted circle corresponding to the second gradient, the positional deviation between the predicted position and the actual position of the updated unsampled pixel is calculated. If the positional deviation does not exceed the deviation threshold, the second gradient is converted into the first gradient.

8. The method according to any one of claims 1 to 4, characterized in that, The multiple preset edge positions are determined by analyzing wafer images acquired under various imaging modes.

9. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the wafer calibration method as described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the wafer calibration method as described in any one of claims 1 to 8.

Images