Focus evaluation method, system, device and medium for wafer alignment mark

Abstract

The present invention belongs to the field of optical metrology and discloses a focus evaluation method, system, device, and medium for wafer alignment marks. The method comprises the following steps: obtaining a sequence image set of the wafer alignment mark during a defocus-focus-defocus process; performing edge extraction on each sequence image in the sequence image set; performing focus evaluation in different directions of each edge-extracted sequence image based on a Brenner function and a Roberts function to obtain an initial focus evaluation function value corresponding to each sequence image; performing variance calculation on the initial focus evaluation function value corresponding to each sequence image to obtain a final focus evaluation function value; and determining the focus state of the wafer alignment mark based on the final focus evaluation function value. The technical solution of the present invention can achieve rapid and accurate automatic focusing of the wafer alignment mark.

Description

Technical Field

[0001] The present invention belongs to the technical field of optical metrology, and in particular relates to a focus evaluation method, system, equipment and medium for wafer alignment marks. Background Art

[0002] In the integrated circuit industry, metrology inspection of micro- and nanostructures spans the entire manufacturing process, encompassing key steps such as circuit design, wafer preparation, silicon wafer fabrication, packaging, and testing. While scanning electron microscopes, transmission electron microscopes, and atomic force microscopes can all achieve nanoscale dimensional measurements, they suffer from significant drawbacks: slow measurement speed, high cost, and complex equipment operation. Therefore, optical metrology technologies, offering advantages such as fast measurement speed, non-contact, non-destructive operation, and ease of in-line integration, are widely used in chip manufacturing process control and yield management. Within the field of optical metrology, autofocus technology, as a key image acquisition and processing method, is a core technology. Targeting the requirements of wafer alignment mark metrology inspection, autofocus technology provides a highly accurate, high-speed, and reliable focus positioning method, resulting in clear images and accurate measurement results. This core technology comprises three key components: focus window selection, focus evaluation function selection, and feedback control of the stepper motor by the search algorithm. The focus evaluation function is crucial, as its performance directly reflects the efficiency and accuracy of autofocus.

[0003] At present, focusing evaluation functions are mainly divided into spatial domain, frequency domain, and information entropy types: spatial domain functions mainly include Roberts function, Brenner function, Tenengrad function, etc. This type of function describes the focus of the image by the change of image grayscale gradient. It is simple to calculate and has high stability. It is a type of focusing evaluation function that is currently widely used, but this type of function has weak noise resistance; frequency domain functions mainly include Fourier transform function and discrete cosine transform function, etc. This type of function has high sensitivity, but is sensitive to noise. The large amount of calculation leads to poor real-time performance and is difficult to apply to actual situations; information entropy function describes the richness of information. The focus of the image is proportional to the change of the corresponding grayscale value interval, that is, the information entropy value of the entire area. This type of function has good unbiasedness, but the calculation process is complicated and easily affected by light, and has low sensitivity and noise resistance.

[0004] Existing techniques combine SML with the Roberts function to propose the SMD-Roberts evaluation function, which has higher focus sensitivity. This approach can improve the accuracy and stability of focus evaluation value calculations, but the time cost of multiple focus evaluation operations is relatively high. The improved dual-threshold Tenengrad focus evaluation function, which is applicable to tool sequence images, fully utilizes the high-frequency information of the image, but the final focus evaluation result and measurement accuracy depend on the selection of an appropriate threshold, making it difficult to apply to various measurement objects.

[0005] In the prior art, an improved Laplace focusing evaluation function is used to evaluate the focus of an image. This method can improve the sensitivity of the evaluation algorithm in the x and y directions, but has weak noise immunity and low stability.

[0006] The existing technology also uses an improved gradient-weighted high-precision focusing evaluation function for part images for evaluation. It obtains the gradient value of edge pixels based on a 4-directional Sobel operator to improve the accuracy of the gradient. Then, it obtains the pixel gradient weighting coefficient based on the difference in grayscale distribution between the edge pixel and its 8 neighboring pixels to enhance the sensitivity and noise resistance of the gradient weighting algorithm. However, it also leads to more complex calculations and poor real-time performance. Summary of the Invention

[0007] The purpose of the present invention is to provide a focus evaluation method, system, device and medium for wafer alignment marks to solve the problems existing in the above-mentioned prior art.

[0008] To achieve the above object, the present invention provides a focus evaluation method for wafer alignment marks, comprising:

[0009] Acquire a sequence of images of the wafer alignment mark during a defocus-focus-defocus process;

[0010] Performing edge extraction on each sequence image in the sequence image set;

[0011] performing focus evaluation in different directions of each sequence image after edge extraction based on the Brenner function and the Roberts function, and obtaining an initial focus evaluation function value corresponding to each sequence image;

[0012] performing variance calculation on the initial focus evaluation function values ​​corresponding to each of the sequence images to obtain a final focus evaluation function value;

[0013] The focus state of the wafer alignment mark is determined based on the final focus evaluation function value.

[0014] Optionally, performing edge extraction on each sequence image in the sequence image set specifically includes:

[0015] Determining an initial sampling interval and calculating a standard deviation of the grayscale values ​​of each of the sequence images;

[0016] Calculating the grayscale value difference between each pixel to be marked and adjacent pixels in each of the sequence images, wherein the adjacent pixels include pixels to the right and below the current pixel to be marked;

[0017] The grayscale value standard deviation is compared with the grayscale value difference of adjacent pixels, and based on the comparison result, it is determined whether the current pixel to be marked is an edge pixel.

[0018] Optionally, comparing the grayscale value standard deviation with the grayscale value difference of adjacent pixels specifically includes:

[0019] If the grayscale value differences of adjacent pixels are all smaller than the grayscale value standard deviation, it is determined that the current pixel to be marked is not an edge pixel;

[0020] If there is a grayscale value difference between the grayscale values ​​of adjacent pixels that is greater than the grayscale value standard deviation, then the current pixel to be marked is determined to be an edge pixel;

[0021] After determining that the current pixel to be marked is an edge pixel, the current pixel to be marked is marked and the initial sampling interval is reduced.

[0022] Optionally, focus evaluation is performed in different directions of each sequence image after edge extraction based on the Brenner function and the Roberts function, specifically including:

[0023] The Brenner function is used to perform evaluation calculation in the 0° and 90° directions of the sequence image, and the pixel grayscale difference calculation in the 90° direction is added to obtain a first focus evaluation function value; the 3×3 Roberts operator is used to perform grayscale gradient calculation in the 45° and 135° directions of the sequence image to obtain a second focus evaluation function value, and the first focus evaluation function value and the second focus evaluation function value are multiplied to obtain an initial focus evaluation function value.

[0024] Optionally, variance calculation is performed on the initial focus evaluation function values ​​corresponding to each of the sequence images. The specific calculation formula is:

[0025]

[0026]

[0027] Where N is the number of sequence images, U Bre2d_Rob2d is the initial average evaluation function value of the sequence image set, F Bre2d_Rob2d_Var is the final focusing evaluation function value.

[0028] A focus evaluation system for wafer alignment marks, comprising:

[0029] An image acquisition module is used to obtain a sequence of images of the wafer alignment mark during a defocus-focus-defocus process;

[0030] A focusing evaluation module is configured to perform edge extraction on each sequence image in the sequence image set; perform focus evaluation in different directions of each sequence image after edge extraction based on a Brenner function and a Roberts function to obtain an initial focus evaluation function value corresponding to each sequence image; perform variance calculation on the initial focus evaluation function value corresponding to each sequence image to obtain a final focus evaluation function value; and determine the focus state of a wafer alignment mark based on the final focus evaluation function value.

[0031] An electronic device includes a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the focus evaluation method for wafer alignment marks.

[0032] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the focus evaluation method for wafer alignment marks is implemented.

[0033] The technical effects of the present invention are:

[0034] 1. Aiming at the application background of the diversity of edge directions of wafer alignment mark image features, the present invention combines the advantages of Brenner function and Roberts function, and can efficiently extract edge gradient information in multiple directions of the mark image, thereby achieving high-precision focusing.

[0035] 2. The present invention integrates the traditional focusing evaluation function and adaptive sampling. Although the computational complexity is improved, the amount of calculation is greatly reduced through adaptive sampling, thereby achieving efficient calculation. It not only overcomes the problems of poor noise resistance and stability of the traditional focusing evaluation function, but also solves the problems of complex calculation and poor real-time performance of the fusion focusing function.

[0036] 3. The present invention improves the problem of poor noise resistance of spatial domain focusing function by finally calculating the variance of the evaluation function, and has better robustness. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0039] Figure 1 Schematic diagram of a wafer alignment mark sequence image set in an embodiment of the present invention;

[0040] Figure 2 is a flowchart of a specific implementation of the focus evaluation method in an embodiment of the present invention;

[0041] Figure 3 This is a comparison diagram of focus evaluation function curves obtained by evaluating and calculating 11 sequential images of alignment mark defocus-focus-defocus using the six focus evaluation functions in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] Various exemplary embodiments of the present invention will now be described in detail. This detailed description should not be considered as limiting the present invention, but rather as a more detailed description of certain aspects, features, and embodiments of the present invention.

[0043] It should be understood that the terms described herein are intended only to describe particular embodiments and are not intended to limit the present invention. In addition, for numerical ranges herein, it should be understood that each intermediate value between the upper and lower limits of the range is also specifically disclosed. Each smaller range between any intermediate value within a stated value or stated range and any other stated value or intermediate value within the stated range is also encompassed by the present invention. The upper and lower limits of these smaller ranges may be independently included or excluded within the scope.

[0044] Unless otherwise indicated, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art. Although only preferred methods are described herein, any method similar or equivalent to that described herein may also be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods associated with the documents. In the event of any conflict with any incorporated document, the contents of this specification shall prevail.

[0045] It will be apparent to those skilled in the art that various modifications and variations may be made to the specific embodiments of the present invention without departing from the scope or spirit of the invention. Other embodiments will be apparent to those skilled in the art from the present invention. The present description and examples are intended to be illustrative only.

[0046] The words “include,” “including,” “have,” “contain,” etc. used in this article are open-ended terms, meaning including but not limited to.

[0047] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0048] Example 1

[0049] like Figure 1 - Figure 3 As shown, this embodiment provides a focus evaluation method for a wafer alignment mark, including: obtaining a sequence image set of the wafer alignment mark in a defocus-focus-defocus process; performing edge extraction on each sequence image in the sequence image set; performing focus evaluation in different directions of each sequence image after edge extraction based on the Brenner function and the Roberts function to obtain an initial focus evaluation function value corresponding to each sequence image; performing variance calculation on the initial focus evaluation function value corresponding to each sequence image to obtain a final focus evaluation function value; and judging the focus state of the wafer alignment mark based on the final focus evaluation function value.

[0050] The obtained wafer alignment mark defocus-focus-defocus sequence images are subjected to pixel adaptive sampling and mark feature edge pixel extraction; then, an improved function combining the Brenner function and the Roberts function is used to evaluate and calculate the sampled pixels to obtain the initial focus evaluation function value of each image; in order to reduce the sensitivity of the improved function to noise, the variance of the initial focus evaluation function value of the image set is calculated to obtain the final focus evaluation function value; according to the focus evaluation function curve of the image, the focal length can be adjusted in real time to obtain a precisely focused image, thereby realizing high-precision detection of the wafer alignment mark. The focus evaluation function constructed in this embodiment has strong noise resistance, good stability and timeliness, and is suitable for automatic focusing of wafer alignment marks with diverse changes in edge direction.

[0051] 1. This embodiment targets the application background of the diversity of edge directions of wafer alignment mark image features, combines the advantages of the Brenner function and the Roberts function, and can efficiently extract edge gradient information in multiple directions of the mark image, thereby achieving high-precision focusing.

[0052] 2. This embodiment integrates the traditional focusing evaluation function and adaptive sampling. Although the computational complexity is improved, the amount of computation is greatly reduced through adaptive sampling, thereby achieving efficient computing. It not only overcomes the problems of poor noise resistance and stability of the traditional focusing evaluation function, but also solves the problems of complex calculation and poor real-time performance of the fusion focusing function.

[0053] 3. This embodiment improves the problem of poor noise resistance of the spatial domain focusing function by finally calculating the variance of the evaluation function, and has better robustness.

[0054] This embodiment not only satisfies the unimodal and unbiased properties of the focusing curve, but also has good stability and noise resistance, and also has an adaptive focusing evaluation function with high computational efficiency, thereby achieving fast and accurate automatic focusing of the wafer alignment mark.

[0055] This embodiment takes the problems of poor noise resistance and stability of traditional focusing evaluation functions and large computational complexity and poor real-time performance of fused focusing evaluation functions as the research basis, and proposes an adaptive focusing evaluation method for wafer alignment marks in the application background of the diversity of edge directions of wafer alignment marks.

[0056] To achieve this goal, the technical solution adopted in this embodiment is:

[0057] An adaptive focus evaluation method for wafer alignment marks is proposed. First, the acquired defocus-focus-defocus sequence images of the wafer alignment marks are adaptively sampled with pixel points of the mark images and their characteristic edge pixels are extracted. Then, the improved function Brenner2d-Roberts2d is used to evaluate and calculate the sampled pixels to obtain the initial focus evaluation function value of each image. In order to reduce the sensitivity of the improved function to noise and enhance its stability and noise resistance, the variance of the initial evaluation function value is calculated to obtain the final focus evaluation function Brenner2d-Roberts2d-Var.

[0058] The specific steps include:

[0059] (1) Adaptive sampling of labeled image pixels:

[0060] Determine different sampling intervals S1 based on the size of the wafer alignment mark defocus-focus-defocus sequence images acquired by the camera:

[0061] S1=max(X,Y) / n

[0062] Where X and Y are the height and width of each image; n is the number of sampling times, which is determined based on the calculation efficiency of the evaluation function and the size of the wafer alignment mark.

[0063] (2) Extraction of edge pixels of the marked image features:

[0064] In order to reduce the amount of calculation, the simplest grayscale difference is used to judge the edge pixels of the alignment mark image feature. First, the grayscale value standard deviation σ of each image is calculated as the basis for judging whether it is an edge pixel:

[0065]

[0066] Where f(x,y) is the grayscale value of the pixel (x,y); XY is the image pixel size; and u is the average pixel value of the entire image.

[0067] Then calculate the grayscale difference between the pixel point (x, y) and its right pixel point (x+1, y) and the pixel point below (x, y+1), and judge it with the grayscale value standard deviation σ of the image to determine whether it is an edge pixel point and adjust the adaptive sampling spacing S in time:

[0068]

[0069] Where R is the absolute value of the grayscale difference between the pixel point (x, y) and the pixel point to its right (x+1, y); D is the absolute value of the grayscale difference between the pixel point (x, y) and the pixel point to its right (x, y+1).

[0070]

[0071] By judging the grayscale difference R, D and the standard deviation σ, if both R and D are smaller than the standard deviation σ, it means that the pixel is not the mark edge, and continue sampling according to the large spacing S1. If R or D is larger than the standard deviation σ, it means that the pixel is the mark edge, and the sampling spacing is reduced to S2. S2 is determined based on the calculation efficiency of the evaluation function and the size of the wafer alignment mark.

[0072] (3) Improved focusing evaluation function:

[0073] Considering that the wafer alignment mark has multiple characteristic edge directions, this embodiment intends to perform evaluation function calculations on the four directions of the mark image (0°, 45°, 90°, and 135°). First, the Brenner function is selected in the 0° and 90° directions. This function is one of the spatial domain focus evaluation functions with the lowest computational complexity. However, it only calculates the square of the grayscale difference of pixels with a difference of two units in the 0° direction. Therefore, without increasing the amount of calculation, the grayscale difference of pixels with a difference of two units in the 90° direction is added:

[0074]

[0075]

[0076] Then, the Roberts function is selected in the 45° and 135° directions. This function calculates the grayscale gradient value in the diagonal direction. However, since the Brenner function operator is a 3×3 matrix, in order to unify the grayscale gradient calculation dimension, the Roberts operator is adjusted from 2×2 to 3×3 matrix:

[0077]

[0078]

[0079] Finally, multiply the two functions to obtain the initial focusing evaluation function that satisfies the four directions:

[0080]

[0081] (4) Variance calculation

[0082] Since the number of calculation directions of the evaluation function has increased to 4, although it has solved the orientation limitation problem of the traditional spatial focus evaluation function in grayscale gradient calculation to a certain extent, and is better adapted to the evaluation calculation of wafer alignment mark images, making the calculation results more accurate and improving the focus sensitivity, it also increases the noise sensitivity of the evaluation function and significantly weakens the noise resistance performance. Therefore, the variance calculation is added on the basis of the above improved function. The variance function can enhance the stability of the evaluation function and ensure that the evaluation function is not easily disturbed by noise:

[0083]

[0084]

[0085] Where N is the number of defocus-focus-defocus sequence image sets, u Bre2d_Rob2d is the initial average evaluation function value of the out-of-focus sequence image set, F Bre2d_Rob2d_Var is the final focusing evaluation function.

[0086] Finally, based on the focus evaluation function value of the defocus-focus-defocus sequence image, a focus evaluation function curve is fitted to achieve precise focus positioning and obtain a clear focused image, thereby realizing high-precision detection of wafer alignment marks.

[0087] The specific application object of this embodiment is the wafer bonding alignment mark automatic focusing system, through which the alignment mark defocus-focus-defocus sequence image set is first captured, such as Figure 1 After acquiring the image set, the adaptive focus evaluation method for wafer alignment marks proposed in this embodiment is used for calculation. The process is as follows: Figure 2 shown.

[0088] The detailed calculation process is as follows:

[0089] (1) Determination of pixel adaptive sampling spacing S1 and sampling times n:

[0090] The size of the sequential images of the wafer alignment mark defocus-focus-defocus acquired by the camera is 1280×1024; n is the number of sampling times, which is determined to be 60 based on the calculation efficiency of the evaluation function and the size of the wafer alignment mark; the sampling interval S1 is rounded to the integer value:

[0091] S1=max(X,Y) / n

[0092] =max(1280,1024) / 60

[0093] ≈21

[0094] (2) Determination of the threshold σ for extracting pixel points at the edge of the wafer alignment mark feature:

[0095] First, calculate the standard deviation σ of the grayscale value of each image as the basis for judging whether it is an edge pixel:

[0096]

[0097]

[0098] Where f(x,y) is the grayscale value of the pixel (x,y); XY is the image pixel size; and u is the average pixel value of the entire image.

[0099] Then calculate the grayscale difference between the pixel point (x, y) and its right pixel point (x+1, y) and the pixel point below it (x, y+1), and compare it with the grayscale value standard deviation σ of the image to determine whether it is an edge pixel and adjust the adaptive sampling spacing in time:

[0100] R=|f(x,y)-f(x+1,y)|

[0101] D=|f(x,y)-f(x,y+1)|

[0102] Where R is the absolute value of the grayscale difference between the pixel point (x, y) and the pixel point to its right (x+1, y); D is the absolute value of the grayscale difference between the pixel point (x, y) and the pixel point to its right (x, y+1).

[0103]

[0104] By judging the grayscale value difference R, D and the standard deviation σ, if both R and D are smaller than the standard deviation σ, it means that the pixel is not the mark edge, and continue to sample according to the large spacing S1. If R or D is larger than the standard deviation σ, it means that the pixel is the mark edge, and the sampling spacing is reduced to S2. S2 is determined to be 1 based on the calculation efficiency of the evaluation function and the size of the alignment mark structure.

[0105] (3) Use the multi-directional gradient focusing evaluation function to perform convolution calculation on the above sampling pixels:

[0106] First, use the Brenner operator to calculate the pixel grayscale difference of two units in the 0° and 90° directions:

[0107]

[0108] Then, the improved Roberts operator is used to calculate the pixel grayscale difference of the two diagonal units in the 45° and 135° directions:

[0109]

[0110] Finally, multiply the two functions to obtain the initial focusing evaluation function that satisfies the four directions:

[0111]

[0112] (4) Perform variance calculation on the focus evaluation function of the labeled sequence image set:

[0113]

[0114]

[0115] Where N is the number of alignment mark defocus-focus-defocus sequence image sets, which is 11, u Bre2d_Rob2d is the initial average evaluation function value of the out-of-focus sequence image set, F Bre2d_Rob2d_Var is the final focusing evaluation function.

[0116] Finally, based on the focus evaluation function value of the alignment mark defocus-focus-defocus sequence image, a focus evaluation function curve is fitted to achieve precise focus positioning and obtain a clear focused image, thereby realizing high-precision detection of the wafer alignment mark.

[0117] In order to verify the performance improvement of the method of this embodiment, performance comparison experiments and analyses were conducted between this embodiment and traditional focusing evaluation functions: Brenner, Roberts, Tenengrad functions, DFT two-dimensional discrete Fourier transform image focusing evaluation function, and the Bre4d_var evaluation function proposed by Dong Zhengqiong et al. in 2023.

[0118] The above focus evaluation function is used to calculate the defocus-focus-defocus sequence images of the same wafer alignment mark, and the obtained focus evaluation values ​​are normalized to obtain a comparison chart of the focus evaluation function curve, as shown in the figure below. Figure 3 In order to more objectively compare and analyze the performance differences of different focusing evaluation functions, the steep area width W, clarity ratio R, steepness Stp and sensitivity f are selected. senPerformance indicators The above focusing evaluation functions are analyzed and compared, as shown in Table 1. The meanings of relevant indicators are as follows.

[0119] Steep zone width W: The steep zone width W represents the width of the region with a large curve change rate on the evaluation function curve. It depends on the specific shape and change rate of the evaluation function curve and is a very important parameter. It can be used as a reference indicator for selecting the maximum step length of the focusing process.

[0120] Clarity ratio R: The clarity ratio R represents the ability of the focus evaluation function to distinguish images with different defocus levels. The larger the focus ratio, the greater the difference in focus values ​​between the clear image and the blurred image, and the easier it is to distinguish the focus function:

[0121]

[0122] where f max is the maximum value of the focusing function, f min is the minimum value of the focusing function.

[0123] Steepness Stp: Steepness Stp indicates the steepness of the focus evaluation function curve at the peak. The greater the steepness, the stronger the function's ability to distinguish images with different defocus levels.

[0124] Sensitivity f sen : Sensitivity f sen Indicates the intensity of the change near the maximum value of the focus evaluation function. The higher the sensitivity, the more intense the change of the function and the better the image focusing effect. Conversely, the function changes more slowly:

[0125]

[0126] where f max is the maximum value of the focusing function, f(z max +θ) is the function value when the horizontal axis changes by θ.

[0127] Table 1 Summary of performance evaluation indicators of focusing evaluation function

[0128]

[0129] As shown in Table 1, the steep zone width W of the improved focus evaluation function of this embodiment is the smallest compared with other evaluation functions; the clarity ratio R is several orders of magnitude higher than other evaluation functions; the steepness Stp is 7 times the minimum value of other evaluation functions; the sensitivity f sen It is 14 times the minimum value of other evaluation functions.

[0130] In summary, this embodiment is an automatic focus evaluation algorithm with strong noise resistance, good stability and timeliness, and applicable to wafer alignment mark images with diverse edge direction changes.

[0131] A focus evaluation system for wafer alignment marks, comprising:

[0132] An image acquisition module is used to obtain a sequence of images of the wafer alignment mark during a defocus-focus-defocus process;

[0133] A focusing evaluation module is configured to perform edge extraction on each sequence image in the sequence image set; perform focus evaluation in different directions of each sequence image after edge extraction based on a Brenner function and a Roberts function to obtain an initial focus evaluation function value corresponding to each sequence image; perform variance calculation on the initial focus evaluation function value corresponding to each sequence image to obtain a final focus evaluation function value; and determine the focus state of a wafer alignment mark based on the final focus evaluation function value.

[0134] An electronic device includes a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the focus evaluation method for wafer alignment marks.

[0135] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the focus evaluation method for wafer alignment marks is implemented.

[0136] The above description is merely a preferred embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A focus evaluation method for wafer alignment marks, characterized in that: include: Acquire a sequence of images of the wafer alignment mark during a defocus-focus-defocus process; Performing edge extraction on each sequence image in the sequence image set specifically includes: Determining an initial sampling interval and calculating a standard deviation of the grayscale values ​​of each of the sequence images; Calculating the grayscale value difference between each pixel to be marked and adjacent pixels in each of the sequence images, comparing the calculated grayscale value difference with the grayscale value standard deviation of the sequence images, and determining whether the current pixel to be marked is an edge pixel based on the comparison result; wherein the adjacent pixels include pixels located to the right and below the current pixel to be marked; Compare the calculated grayscale value difference with the grayscale value standard deviation of the sequence image, specifically including: If the grayscale value differences of adjacent pixels are all smaller than the grayscale value standard deviation, it is determined that the current pixel to be marked is not an edge pixel; If there is a grayscale value difference between the grayscale values ​​of adjacent pixels that is greater than the grayscale value standard deviation, then the pixel to be marked is determined to be an edge pixel; After determining that the current pixel to be marked is an edge pixel, marking the current pixel to be marked and reducing the initial sampling interval; performing focus evaluation in different directions of each sequence image after edge extraction based on the Brenner function and the Roberts function, and obtaining an initial focus evaluation function value corresponding to each sequence image; The variance calculation is performed on the initial focus evaluation function values ​​corresponding to each of the sequence images to obtain the final focus evaluation function value. The specific calculation formula is: Where N is the number of sequence images, U Bre2d_Rob2d is the initial average evaluation function value of the sequence image set, F Bre2d_Rob2d is the initial focusing evaluation function, F Bre2d_Rob2d_Var is the final focusing evaluation function value; The focus state of the wafer alignment mark is determined based on the final focus evaluation function value.

2. The focus evaluation method for wafer alignment marks according to claim 1, wherein: Focus evaluation is performed in different directions of each sequence image after edge extraction based on the Brenner function and Roberts function, specifically including: The Brenner function is used to perform evaluation calculation in the 0° and 90° directions of the sequence image, and the pixel grayscale difference calculation in the 90° direction is added to obtain a first focus evaluation function value; the 3×3 Roberts operator is used to perform grayscale gradient calculation in the 45° and 135° directions of the sequence image to obtain a second focus evaluation function value, and the first focus evaluation function value and the second focus evaluation function value are multiplied to obtain an initial focus evaluation function value.

3. A focus evaluation system for wafer alignment marks, characterized in that: include: An image acquisition module is used to obtain a sequence of images of the wafer alignment mark during a defocus-focus-defocus process; The focus evaluation module is used to extract edges from each sequence image in the sequence image set, specifically including: Determining an initial sampling interval and calculating a standard deviation of the grayscale values ​​of each of the sequence images; Calculating the grayscale value difference between each pixel to be marked and adjacent pixels in each of the sequence images, comparing the calculated grayscale value difference with the grayscale value standard deviation of the sequence images, and determining whether the current pixel to be marked is an edge pixel based on the comparison result; wherein the adjacent pixels include pixels located to the right and below the current pixel to be marked; Compare the calculated grayscale value difference with the grayscale value standard deviation of the sequence image, specifically including: If the grayscale value differences of adjacent pixels are all smaller than the grayscale value standard deviation, it is determined that the current pixel to be marked is not an edge pixel; If there is a grayscale value difference between the grayscale values ​​of adjacent pixels that is greater than the grayscale value standard deviation, then the pixel to be marked is determined to be an edge pixel; After determining that the current pixel to be marked is an edge pixel, marking the current pixel to be marked and reducing the initial sampling interval; performing focus evaluation in different directions of each sequence image after edge extraction based on the Brenner function and the Roberts function, and obtaining an initial focus evaluation function value corresponding to each sequence image; The variance calculation is performed on the initial focus evaluation function values ​​corresponding to each of the sequence images to obtain the final focus evaluation function value. The specific calculation formula is: Where N is the number of sequence images, U Bre2d_Rob2d is the initial average evaluation function value of the sequence image set, F Bre2d_Rob2d is the initial focusing evaluation function, F Bre2d_Rob2d_Var is the final focusing evaluation function value; The focus state of the wafer alignment mark is determined based on the final focus evaluation function value.

4. An electronic device, characterized in that: It comprises a memory and a processor, the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to perform a focus evaluation method for wafer alignment marks according to any one of claims 1-2.

5. A computer-readable storage medium, characterized in that It stores a computer program, which, when executed by a processor, implements a focus evaluation method for wafer alignment marks as described in any one of claims 1 to 2.

Images