Wafer defect detection image processing method and device and computer storage medium

Abstract

The invention discloses a wafer defect detection image processing method and device and a computer storage medium. The method comprises the following steps: determining a target entropy value and a first gray scale division threshold value corresponding to the target entropy value by calculating gray scale distribution characteristics of all pixel points of a wafer defect detection image; determining a second gray scale division threshold value; according to the first gray scale division threshold value and the second gray scale division threshold value, dividing gray scale values of all pixel points of the wafer defect detection image to obtain a plurality of gray scale intervals of the wafer defect detection image; and according to different preset gray value adjustment strategies corresponding to the plurality of gray intervals, performing contrast adjustment on the gray values of the pixel points in the plurality of gray intervals to obtain a wafer defect detection image after contrast adjustment. According to the embodiment of the invention, the wafer defect detection precision can be improved.

Description

Technical Field

[0001] This application belongs to the field of semiconductor technology, and particularly relates to a method, device, and computer storage medium for processing wafer defect detection images. Background Art

[0002] With the development of technology and the progress of semiconductor processes, the integration of chips has been continuously improved, and the manufacturing process has become increasingly complex, which poses higher performance requirements for chip defect detection.

[0003] In related technologies, a clearer defect detection image can be obtained by increasing the beam current of the electron beam emitted by the electron beam yield detection device. And the defect detection image is further optimized through an image processing algorithm to improve the detection accuracy of wafer defect detection.

[0004] However, the image processing method in related technologies cannot well achieve the optimization processing of defect detection images, resulting in the problem of low detection accuracy of wafer defect detection. Summary of the Invention

[0005] The embodiments of this application provide a method, device, and computer storage medium for processing wafer defect detection images, which can improve the detection accuracy of wafer defect detection images.

[0006] In a first aspect, the embodiments of this application provide a method for processing a wafer defect detection image, including: determining a target entropy value and a first gray-level division threshold corresponding to the target entropy value by calculating the gray-level distribution characteristics of all pixel points of the wafer defect detection image; determining a second gray-level division threshold based on the maximum gray-level value among all pixel points of the wafer defect detection image; dividing the gray-level values of all pixel points of the wafer defect detection image into gray-level intervals by using the first gray-level division threshold and the second gray-level division threshold, to obtain multiple gray-level intervals of the wafer defect detection image, where the multiple gray-level intervals include at least three gray-level intervals; and adjusting the gray-level values of the pixel points in the multiple gray-level intervals according to different preset gray-level value adjustment strategies corresponding to the multiple gray-level intervals, to obtain a wafer defect detection image with adjusted contrast.

[0007] In an alternative embodiment of the first aspect, by calculating the gray-scale distribution characteristics of all pixel points in the wafer defect detection image, determining a target entropy value that meets a preset condition and a first gray-scale division threshold corresponding to the target entropy value, includes: Based on each gray-scale value of the wafer defect detection image, dividing the gray-scale values of all pixel points in the wafer defect detection image into foreground and background, obtaining a foreground gray-scale interval and a background gray-scale interval corresponding to each gray-scale value; According to the gray-scale distribution characteristics of each gray-scale value in the corresponding foreground gray-scale interval and background gray-scale interval, respectively calculating the foreground entropy corresponding to each gray-scale value in the foreground gray-scale interval and the background entropy corresponding to each gray-scale value in the background gray-scale interval; Taking the sum of the foreground entropy and the background entropy corresponding to each gray-scale value as the total entropy value corresponding to each gray-scale value; Taking the largest total entropy value among the total entropy values corresponding to multiple gray-scale values as the maximum entropy of the wafer defect detection image, and taking the gray-scale value corresponding to the maximum entropy as the first gray-scale division threshold.

[0008] In an alternative embodiment of the first aspect, the first gray-scale division threshold is less than the second gray-scale division threshold; The multiple gray-scale intervals at least include a first gray-scale interval, a second gray-scale interval, and a third gray-scale interval, where the critical value between the first gray-scale interval and the second gray-scale interval is the first gray-scale division threshold, and the critical value between the second gray-scale interval and the third gray-scale interval is the second gray-scale division threshold; According to different preset gray-scale value adjustment strategies corresponding to the multiple gray-scale intervals, adjusting the gray-scale values of the pixel points in the multiple gray-scale intervals to obtain a wafer defect detection image with adjusted contrast, includes: Adjusting the gray-scale values of the pixel points in the first gray-scale interval to a first preset gray-scale value, where the first preset gray-scale value is less than the first gray-scale division threshold; Linearly stretching the gray-scale values of the pixel points in the second gray-scale interval according to a preset linear stretching algorithm; Adjusting the gray-scale values of the pixel points in the third gray-scale interval to a second preset gray-scale value, where the second preset gray-scale value is greater than the second gray-scale division threshold.

[0009] In an alternative embodiment of the first aspect, before calculating the gray-scale distribution characteristics of all pixel points in the wafer defect detection image to determine the target entropy value and the first gray-scale division threshold corresponding to the target entropy value, the method further includes: Obtaining the frequency information of each pixel point in the wafer defect detection image; Calculating the cut-off frequency of all pixel points in the wafer defect detection image based on an energy spectrum model; Filtering the pixel points in the wafer defect detection image according to the frequency information of each pixel point and the cut-off frequency to obtain a filtered wafer defect detection image.

[0010] In an alternative embodiment of the first aspect, calculating the cut-off frequency of all pixel points in the wafer defect detection image based on the energy spectrum model includes: determining the cut-off frequency according to the frequency information corresponding to all pixel points and the energy loss constraint condition, where the energy loss constraint condition is used to characterize the energy constraint condition of the frequency information corresponding to all pixel points.

[0011] In an alternative embodiment of the first aspect, determining the cut-off frequency according to the frequency information corresponding to each pixel point and the energy loss constraint condition includes: sorting the frequency information corresponding to all pixel points according to the frequency magnitude to obtain a frequency sequence; calculating the energy information corresponding to each frequency information; performing energy accumulation on the energy information corresponding to the frequency information according to the frequency sequence until the accumulated energy satisfies the energy loss constraint condition and stops accumulating, and taking the last frequency information of the accumulation as the cut-off frequency.

[0012] In an alternative embodiment of the first aspect, after obtaining the wafer defect detection image with contrast adjustment, the method further includes: performing defect detection on the wafer defect detection image with contrast adjustment to obtain a defect detection result.

[0013] In a second aspect, an embodiment of the present application provides a processing method device for a wafer defect detection image. The device includes:

[0014] A first determination module, configured to determine a target entropy value and a first gray-level division threshold corresponding to the target entropy value by calculating the gray-level distribution characteristics of all pixel points in the wafer defect detection image;

[0015] A second determination module, configured to determine a second gray-level division threshold based on the maximum gray-level value among all pixel points in the wafer defect detection image;

[0016] A division module, configured to divide the gray-level values of all pixel points in the wafer defect detection image into gray-level intervals by using the first gray-level division threshold and the second gray-level division threshold, to obtain multiple gray-level intervals of the wafer defect detection image, and the multiple gray-level intervals include at least three gray-level intervals;

[0017] An adjustment module, configured to adjust the gray-level values of pixel points in the multiple gray-level intervals according to different preset gray-level value adjustment strategies corresponding to the multiple gray-level intervals, to obtain a wafer defect detection image with contrast adjustment.

[0018] In a third aspect, an embodiment of the present application provides a processing method device for a wafer defect detection image. The device includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, the processing method of the wafer defect detection image in the first aspect or any one of the embodiments of the first aspect is implemented.

[0019] Fourth aspect, a computer-readable storage medium has computer program instructions stored thereon. When the computer program instructions are executed by a processor, the processing method of the wafer defect detection image in the first aspect or any implementation manner of the first aspect is implemented.

[0020] Fifth aspect, an embodiment of the present application provides a computer program product. When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device is caused to execute the processing method of the wafer defect detection image as in the first aspect or any implementation manner of the first aspect.

[0021] In the processing method, device, and computer storage medium of the wafer defect detection image according to the embodiment of the present application, the first gray division threshold and the second gray division threshold are determined through the gray distribution feature and the maximum gray value in the wafer defect detection image. The division condition of the determined gray interval is based on the gray value of each pixel point in the wafer defect detection image and is closely related to the characteristics of the wafer gray detection image itself, thereby achieving the optimal segmentation of the wafer defect detection image. Furthermore, when adjusting the gray value of the pixel points in different gray intervals according to the preset gray value adjustment strategy corresponding to each gray interval, a better contrast adjustment effect can be obtained, increasing the contrast of the wafer defect detection image, enhancing the resolvability of the wafer defect detection image, and further improving the defect detection accuracy, that is, improving the defect capture rate of wafer defect detection and reducing the defect misdetection rate, and realizing stable and reliable wafer defect detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings required to be used in the embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0023] Figure 1 Shows a schematic flowchart of an image processing technology provided by an embodiment of the present application;

[0024] Figure 2 Shows a schematic flowchart of a wafer defect detection image method provided by an embodiment of the present application;

[0025] Figure 3 Shows a schematic flowchart of a wafer defect detection image method provided by an embodiment of the present application;

[0026] Figure 4 Shows a schematic flowchart of a wafer defect detection image method provided by an embodiment of the present application;

[0027] Figure 5Shows a schematic flowchart of a wafer defect detection image method provided by an embodiment of the present application;

[0028] Figure 6 Shows a schematic flowchart of determining a cut-off frequency provided by an embodiment of the present application;

[0029] Figure 7 Shows a schematic flowchart of a wafer defect detection image method provided by an embodiment of the present application;

[0030] Figure 8 Is a schematic structural diagram of a processing device for wafer defect detection images provided by another embodiment of the present application;

[0031] Figure 9 Is a schematic structural diagram of a processing device for wafer defect detection images provided by yet another embodiment of the present application. Detailed implementation manners

[0032] The features and exemplary embodiments of various aspects of the present application will be described in detail below. For the purpose of making the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than limiting the present application. For those skilled in the art, the present application can be implemented without some of these specific details. The following description of the embodiments is only intended to provide a better understanding of the present application by showing examples of the present application.

[0033] It should be noted that in this document, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover a non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0034] In the field of semiconductor manufacturing, electron beam yield detection equipment bears the heavy responsibility of wafer defect detection. With the continuous development of semiconductor technology, the integration of components on the wafer continues to increase, the manufacturing process becomes more and more complex, and defect detection technology faces higher performance requirements. In related technologies, electron beam yield detection equipment can obtain defect detection images corresponding to wafers with increasingly smaller processes by continuously increasing the beam size of the electron beam, wherein the electron beam yield detection equipment may include a scanning electron microscope (SEM); the defect detection image may be an electrical defect image. However, an inappropriate beam size will lead to a decrease in the signal-to-noise ratio and contrast of the defect detection image, which in turn causes a decrease in the resolution of defect detection.

[0035] For the above-mentioned related technologies, you can consider setting up a filtering algorithm and a contrast stretch correction algorithm to improve the signal-to-noise ratio and contrast. Figure 1 As shown in the flowchart of the image processing technology shown, before performing image processing on the defect detection image, step S110 can be executed to determine whether the wafer graphic features are consistent. Among them, the wafer graphic features include at least the graphic shape and the graphic density. When it is determined that the wafer image features are inconsistent, step S120 is executed to separate the tasks according to the graphic features. And according to the different tasks after separation, S130 is executed to debug and set different filtering algorithms according to different tasks; and S140 is executed to debug and set different contrast adjustment algorithms according to different tasks. Further, S150 can be executed on the defect detection image after image processing to realize wafer defect detection through defect detection technology.

[0036] However, in Figure 1 In the process of image processing shown in the figure, it is necessary to separate tasks according to the wafer graphic features. However, when performing task separation, relevant technicians need to debug and set different filtering algorithms and contrast adjustment algorithms according to different wafer graphic features to ensure that defect detection images with different wafer graphic features can have good defect detection accuracy. In this process, since technicians need to constantly adjust algorithm parameters, the equipment throughput decreases and the detection cost increases. In view of this, how to ensure that the adaptively adjusted algorithm parameters can have a good defect capture rate and a low false detection rate is a technical problem that needs to be solved urgently.

[0037] In order to solve the problems in the prior art, the embodiments of the present application provide a method, device and computer storage medium for processing wafer defect detection images. The wafer defect detection image method provided by the embodiments of the present application is first introduced below.

[0038] Figure 2The flowchart of the wafer defect detection image method provided by an embodiment of the present application is shown. As Figure 2 shown, the wafer defect detection image method includes the following steps:

[0039] S210. By calculating the gray-scale distribution characteristics of all pixel points of the wafer defect detection image, determine the target entropy value and the first gray-scale division threshold corresponding to the target entropy value.

[0040] Exemplarily, the wafer defect detection image can be an image obtained by collecting an image of the wafer to be detected through a defect detection device. For example, the wafer to be detected can be scanned by an electron beam through an SEM, the signals generated by the interaction between the electron beam and the wafer are collected, and the signals are converted into digital image signals, so as to obtain the wafer defect detection image.

[0041] The wafer defect detection image can include multiple pixel points, and each pixel point can have a corresponding gray-scale value. The gray-scale distribution characteristics of the wafer defect detection image can be determined by statistically analyzing the gray-scale values corresponding to each pixel point in the wafer detection image. Among them, the gray-scale distribution characteristics can be used to characterize the distribution of the gray-scale values of each pixel point in the wafer defect detection image.

[0042] In one example, the gray-scale distribution characteristics can be obtained by evaluating the gray-scale values corresponding to each pixel in the wafer defect detection image through an information entropy model. For example, the gray-scale distribution characteristics of the wafer defect detection image can at least include the number of pixel points with different gray-scale values in the wafer defect detection image. Among them, the number of pixel points corresponding to different gray-scale values can be characterized by a histogram.

[0043] Exemplarily, the target entropy value can be calculated based on the gray-scale distribution characteristics of all pixel points in the wafer defect detection image, so as to evaluate the amount of information or the degree of chaos contained in the wafer defect detection image. Among them, it can be understood that the target entropy value can be used as an important basis for segmenting the wafer defect detection image.

[0044] The entropy value corresponding to each gray-scale value can be determined based on the distribution of each gray-scale value in the gray-scale distribution characteristics, and the entropy value that meets the preset conditions is used as the target entropy value.

[0045] In one example, the entropy value greater than or equal to the preset threshold can be used as the target entropy value, where the preset threshold can be set in advance by relevant technicians according to experiments.

[0046] In another example, the target entropy value can also be calculated based on the gray-scale distribution characteristics and relevant statistical algorithms.

[0047] Exemplarily, after determining the target entropy value, the gray-scale value corresponding to the target entropy value can be used as the first gray-scale division threshold.

[0048] S220. Determine a second gray-level division threshold based on the maximum gray level value among all pixel points in the wafer defect detection image.

[0049] Exemplarily, the maximum gray level value can be the maximum gray level value among the gray level values of all pixel points in the wafer defect detection image. The gray level values of all pixel points in the wafer defect detection image can be traversed to determine the maximum gray level value.

[0050] Exemplarily, the second gray-level division threshold can be determined based on the maximum gray level value. For example, the second gray-level division threshold can be calculated through a preset gray-level division parameter and the maximum gray level value. In one example, the second gray-level division threshold T can be calculated through the following formula (1). 2 :

[0051] T 2 = m × I max (1)

[0052] Wherein, m can represent the preset gray-level division parameter; I max can represent the maximum gray level value among all pixel points in the wafer defect detection image. Among them, the preset gray-level division parameter m can be set to 99.7% based on the normal distribution principle (i.e., the 3sigma principle). It can be understood that in the embodiments of the present application, the normal distribution principle is only used to exemplarily illustrate the method for determining the value of the preset gray-level division parameter, and the preset gray-level division parameter can also be determined through methods such as the Z-score. Therefore, the preset gray-level division parameter is not limited in the embodiments of the present application.

[0053] S230. According to the first gray-level division threshold and the second gray-level division threshold, divide the gray level values of all pixel points in the wafer defect detection image into gray-level intervals, and obtain multiple gray-level intervals of the wafer defect detection image.

[0054] Among them, the multiple gray-level intervals include at least three gray-level intervals.

[0055] Exemplarily, each gray-level interval can include multiple gray level values. And the number of each gray level value in the gray-level interval can be one or more.

[0056] Exemplarily, the first gray-level division threshold and the second gray-level division threshold can be used as the division conditions of the gray-level intervals to divide the gray level values of all pixel points in the wafer defect detection image, and multiple gray-level intervals of the wafer defect detection image can be obtained.

[0057] In one example, the gray value corresponding to each pixel can be compared with a first gray division threshold and a second division threshold to determine the gray interval to which the gray value of each pixel belongs. For example, when the first gray division threshold is less than the second gray division threshold, the gray value of the pixel can be compared with the first gray division threshold. When it is determined that the gray value of the pixel is less than the first gray division threshold, the gray value of the pixel can be divided into the gray interval where the gray value is less than the first gray division threshold. When it is determined that the gray value of the pixel is greater than the first gray division threshold and less than the second gray division threshold, the gray value of the pixel can be divided into the gray interval where the gray value is greater than the first gray division threshold and less than the second gray division threshold. When it is determined that the gray value of the pixel is greater than the second gray division threshold, the gray value of the pixel can be divided into the gray interval where the gray value is greater than the second gray division threshold.

[0058] S240. According to different preset gray value adjustment strategies corresponding to multiple gray intervals, adjust the gray values of the pixels in the multiple gray intervals to obtain a wafer defect detection image with adjusted contrast.

[0059] Exemplarily, the preset gray value adjustment strategy is used to adjust the gray value of each pixel in the gray interval, so as to achieve the contrast adjustment of the wafer defect detection image.

[0060] Different gray intervals can correspond to different preset gray value adjustment strategies. The gray value of each pixel in the gray interval can be adjusted according to the preset gray value adjustment strategy corresponding to each gray interval. Among them, the preset gray value adjustment strategies corresponding to different gray value intervals can be set by those skilled in the art according to experience and experimental data.

[0061] Exemplarily, the preset gray value adjustment strategy corresponding to each gray interval can be determined, and the gray values in the gray interval can be adjusted according to the preset gray value adjustment strategy corresponding to each gray interval to obtain a wafer defect detection image with adjusted gray value. Among them, it can be understood that the contrast can be used to describe the difference degree between the brightest and darkest parts in the wafer defect detection image. The gray value can be used to characterize the brightness level of each pixel in the wafer defect detection image. By adjusting the brightness level of the pixels in each gray interval of the circular defect detection image through the preset gray value adjustment strategy, the contrast between different intervals can be changed, and the contrast adjustment can be realized.

[0062] In one example, the contrast corresponding to each gray level interval can be increased through a preset gray level value adjustment strategy, so that the details in the wafer defect detection image are clearer and easier to distinguish. For example, the preset gray level value adjustment strategy can be a gray level value adjustment algorithm. By using the gray level value adjustment algorithm, the brightness difference between the bright area and the dark area in the wafer defect detection image can be made large. That is, through the gray level value adjustment algorithm, the value of larger pixel values is made larger, and the value of smaller pixel values is made smaller, so as to improve the contrast of the wafer defect detection image and the clarity of the wafer defect detection image.

[0063] In the embodiments of the present application, the first gray level division threshold and the second gray level division threshold are determined through the gray level distribution characteristics and the maximum gray level value in the wafer defect detection image. The division condition of the gray level interval determined in this way is based on the gray level value of each pixel point in the wafer defect detection image and is closely related to the characteristics of the wafer gray level detection image itself, so as to achieve the best segmentation of the wafer defect detection image. Furthermore, when adjusting the contrast of the gray level values of the pixel points in different gray level intervals according to the preset gray level value adjustment strategy corresponding to each gray level interval, a better contrast adjustment effect can be obtained, the contrast of the wafer defect detection image is increased, the distinguishability of the wafer defect detection image is improved, and then the defect detection accuracy is improved, that is, the defect capture rate of the wafer defect detection is improved and the defect misdetection rate is reduced, so as to achieve stable and reliable wafer defect detection.

[0064] Exemplarily, the target entropy value can be the maximum entropy corresponding to the wafer defect detection image, and the first gray level division threshold corresponding to the target entropy value can be the first gray level division threshold corresponding to the maximum entropy.

[0065] The maximum entropy can be used to represent the best segmentation point of the wafer defect detection image. It can be understood that when the wafer defect detection image is divided by the best segmentation point, the information amount of the wafer defect detection image can be guaranteed to the greatest extent, and the divided image has the greatest difference.

[0066] In one example, the gray level values involved in all pixel points of the wafer defect detection image can be determined, and the entropy value corresponding to each gray level value can be calculated to determine the maximum entropy of the wafer defect detection image. Among them, the maximum entropy of the wafer defect detection image can be determined through the relevant calculation formula of the probability distribution; or the maximum entropy of the wafer defect detection image can be calculated through formulas such as the Lagrange multiplier method.

[0067] Exemplarily, the first gray-scale division threshold corresponding to the maximum entropy may be the gray value corresponding to the pixel point with the maximum entropy value. It can be understood that the maximum entropy of the wafer defect detection image can be determined based on the gray-scale distribution characteristics of the wafer defect detection image, and the gray value corresponding to the maximum entropy can be used as the first gray-scale division threshold. Among them, when dividing the wafer defect detection image according to the gray value corresponding to the maximum entropy value, the divided image has the greatest difference, achieving the best segmentation.

[0068] Furthermore, in order to determine the maximum entropy of the wafer detection image and the first gray-scale division threshold corresponding to the maximum entropy, as another implementation manner of the present application, the present application also provides another implementation manner of the processing method of the wafer defect detection image. For specific details, please refer to the following embodiments.

[0069] Figure 3 The flowchart of the wafer defect detection image method provided by an embodiment of the present application is shown. As Figure 3 shown, the wafer defect detection image method includes the following steps:

[0070] S310. Based on each gray value of the wafer defect detection image, divide the gray values of all pixel points in the wafer defect detection image into foreground and background, and obtain the foreground gray-scale interval and the background gray-scale interval corresponding to each gray value.

[0071] Exemplarily, each gray value in the wafer defect detection image can be used as a segmentation point to divide the gray values of each pixel point in the wafer defect detection image, so as to obtain the foreground gray-scale interval and the background gray-scale interval corresponding to the gray value as the segmentation point. It can be understood that each gray value as the segmentation point corresponds to a pair of foreground gray-scale intervals and background gray-scale intervals. Therefore, the foreground gray-scale intervals and the background gray-scale intervals corresponding to different gray values may be different.

[0072] In one example, a gray value of the wafer defect detection image can be obtained as the target gray value, where the target gray value can be any gray value included in the wafer defect detection image. Taking the target gray value as the segmentation point, divide the gray values of all pixel points in the wafer defect detection image, and use the multiple gray values less than or equal to the target gray value in the wafer defect detection image as the foreground gray-scale interval, and use the multiple gray values greater than the target gray value in the wafer defect detection image as the background gray-scale interval.

[0073] S320. According to the gray-scale distribution characteristics of each gray value in the corresponding foreground gray-scale interval and background gray-scale interval, calculate the foreground entropy corresponding to each gray value in the foreground gray-scale interval and the background entropy corresponding to each gray value in the background gray-scale interval, respectively.

[0074] Exemplarily, after dividing the wafer defect detection image into foreground and background according to the gray value, the gray values in the foreground gray interval and the background gray interval can be respectively counted, so as to determine the gray distribution characteristics of the foreground gray interval and the gray distribution characteristics of the background gray interval. Further, the foreground entropy of the gray value in the foreground gray interval and the background entropy of the gray value in the background gray interval can be determined.

[0075] In one example, the foreground entropy H 0 (q) can be calculated by the following formula (2), and the background entropy H 1 (q) can be calculated by the following formula (3):

[0076]

[0077]

[0078] Among them, i represents the gray value, and its range is 0 - 255; q is the target gray value, that is, the gray value used to distinguish the foreground and the background; p(i) represents the probability distribution corresponding to each gray value; further, p(i) can be calculated by the following formula (4):

[0079]

[0080] Among them, M and N respectively represent the number of rows and columns of the wafer defect detection image, and n i can represent the number of pixel points with a gray value of i and belonging to the foreground gray interval; or n i can represent the number of pixel points with a gray value of i and belonging to the background gray interval. And, p 0 (q) can be calculated by the following formula (5):

[0081]

[0082] Exemplarily, the foreground entropy and the background entropy can be used to characterize the respective chaos degree and disorder state of the foreground gray interval and the background gray interval divided according to different divided gray values.

[0083] S330. Take the sum of the foreground entropy and the background entropy corresponding to each gray value as the total entropy value corresponding to each gray value.

[0084] Exemplarily, the sum of the foreground entropy and the background entropy corresponding to each gray value can be calculated, and the sum of the calculated foreground entropy and the background entropy is used as the total entropy value corresponding to the gray value.

[0085] In one example, the total entropy value H(q) corresponding to the gray value as the segmentation point can be calculated by the following formula (6):

[0086] H(q) = H 0 (q) + H 1 (q) (6)

[0087] S340. Take the maximum total entropy value among the total entropy values corresponding to multiple gray values as the maximum entropy of the wafer defect detection image, and take the gray value corresponding to the maximum entropy as the first gray division threshold.

[0088] Exemplarily, the total entropy value corresponding to each gray value can be determined, and the maximum entropy value among the multiple total entropy values can be taken as the maximum entropy of the wafer defect detection image, and the gray value corresponding to the maximum entropy can be taken as the first gray division threshold T 1 .

[0089] It can be understood that the gray value serving as the segmentation point has a corresponding pair of foreground gray intervals and background gray intervals, and the foreground entropy and background entropy corresponding to the gray value serving as the segmentation point can be calculated through the foreground gray intervals and background gray intervals. Furthermore, after traversing the total entropy values corresponding to all gray values, the maximum total entropy value can be determined, and the gray value serving as the segmentation point corresponding to the maximum total entropy value can be taken as the first division threshold.

[0090] S350. Determine the second gray division threshold based on the maximum gray value among all pixel points of the wafer defect detection image.

[0091] S360. According to the first gray division threshold and the second gray division threshold, divide the gray values of all pixel points of the wafer defect detection image into gray intervals to obtain multiple gray intervals of the wafer defect detection image.

[0092] S370. According to different preset gray value adjustment strategies corresponding to multiple gray intervals, adjust the gray values of pixel points in the multiple gray intervals to obtain the wafer defect detection image with adjusted contrast.

[0093] Exemplarily, steps S350 - S370 are the same as steps S220 - S240, and will not be elaborated here.

[0094] Exemplarily, according to each gray value of the wafer defect detection image, the gray values of all pixel points in the wafer defect detection image can be divided into foreground and background, and the foreground gray interval and the background gray interval corresponding to each gray value can be obtained. And according to the gray distribution characteristics of each gray value in the corresponding foreground gray interval and background gray interval, the foreground entropy corresponding to each gray value in the foreground gray interval and the background entropy corresponding to each gray value in the background gray interval are respectively calculated. The sum of the foreground entropy and the background entropy corresponding to each gray value is used as the total entropy value corresponding to each gray value, and the largest total entropy value is selected from them as the maximum entropy of the wafer defect detection image, and the gray value corresponding to the maximum entropy is used as the first gray division threshold. Furthermore, according to the first gray division threshold and the second gray division threshold, the gray values of all pixel points in the wafer defect detection image are divided into multiple gray intervals, and according to the preset gray value adjustment strategy corresponding to each gray interval, the contrast adjustment of the gray values of the pixel points in the multiple gray intervals is realized.

[0095] In the embodiments of the present application, by calculating the foreground entropy and the background entropy corresponding to each gray value, and calculating the total entropy value corresponding to each gray value according to the foreground entropy and the background entropy. The largest total entropy value is selected from the total entropy values corresponding to all gray values as the maximum entropy of the wafer defect detection image, and the gray value corresponding to the maximum entropy is used as the first gray division threshold. The first gray division threshold determined in this way can better reflect the maximum information amount and the highest uncertainty in the wafer detection image, so as to realize stable and reliable wafer defect detection. It can be understood that when determining the maximum entropy of the wafer defect detection image through the gray distribution characteristics and using the gray value corresponding to the maximum entropy as the first gray division threshold, the optimal gray division threshold can be automatically determined, realizing the adaptive adjustment of the algorithm parameters in image processing, thus avoiding the problems of decreased equipment throughput and increased detection cost.

[0096] In order to be able to adjust the gray values of the pixel points in different gray intervals according to different preset gray value adjustment strategies, as another implementation manner of the present application, the present application also provides another implementation manner of the processing method of the wafer defect detection image, which is specifically described in the following embodiments.

[0097] Figure 4 The flowchart of the wafer defect detection image method provided by an embodiment of the present application is shown. As Figure 4 shown, the wafer defect detection image method includes the following steps:

[0098] S410. By calculating the gray distribution characteristics of all pixel points in the wafer defect detection image, determine the target entropy value and the first gray division threshold corresponding to the target entropy value.

[0099] S420. Determine a second gray-level division threshold based on the maximum gray-level value among all the pixel points in the wafer defect detection image.

[0100] S430. Divide the gray-level values of all the pixel points in the wafer defect detection image into gray-level intervals according to the first gray-level division threshold and the second gray-level division threshold, to obtain a first gray-level interval, a second gray-level interval, and a third gray-level interval.

[0101] Exemplarily, steps S430 - S430 are the same as steps S210 - S230, and will not be elaborated here.

[0102] Exemplarily, the first gray-level division threshold can be less than the second gray-level division threshold, and based on the first gray-level division threshold and the first gray-level division threshold, the wafer defect detection image can be divided into a first gray-level interval, a second gray-level interval, and a third gray-level interval.

[0103] Among them, the critical value between the first gray-level interval and the second gray-level interval is the first gray-level division threshold, and the critical value between the second gray-level interval and the third gray-level interval is the second division threshold. It can be understood that the gray-level values of the first gray-level interval are all less than those of the second gray-level interval, and the gray-level values of the second gray-level interval are all less than those of the third gray-level interval.

[0104] S440. Adjust the gray-level values of the pixel points in the first gray-level interval to a first preset gray-level value.

[0105] Among them, the first preset gray-level value is less than the first gray-level division threshold.

[0106] Exemplarily, the gray-level values of all the pixel points in the first gray-level interval can be adjusted to the first preset gray-level value. Among them, the magnitude of the first preset gray-level value can be preset by the technician according to different detection requirements.

[0107] In one example, the technician can preset the first preset gray-level value to 0, that is, the gray-level values of all the pixel points in the first gray-level interval can be adjusted to 0.

[0108] In another example, the first preset gray-level value can be set to the minimum gray-level value in the wafer gray-level detection image. That is, the gray-level values of all the pixel points in the first gray-level interval can be adjusted to the minimum gray-level value in the first gray-level interval.

[0109] S450. Linearly stretch the gray-level values of the pixel points in the second gray-level interval according to a preset linear stretching algorithm.

[0110] Exemplarily, the gray-level values of all the pixel points in the second gray-level interval can be stretched according to the preset linear stretching algorithm to obtain the stretched gray-level values.

[0111] The linear stretching algorithm can map the gray values of all pixel points in the second gray interval according to a linear ratio and convert them to a new, specified numerical range. During this process, the relative magnitude relationship of the original gray data is ensured to remain unchanged after stretching, only the overall range of the gray values changes. Thus, the difference between the bright and dark regions in the wafer defect detection image is increased, making the originally unobvious details or features become more clearly visible in the stretched wafer defect detection image.

[0112] In some alternative embodiments, the gray values of all pixel points in the second gray interval can also be stretched according to the relevant algorithms of non-linear stretching. For example, the gray value stretching of all pixel points in the second gray interval can be achieved by methods such as histogram stretching algorithm, gamma transformation, etc.

[0113] S460. Adjust the gray values of the pixel points in the third gray interval to the second preset gray value.

[0114] Among them, the second preset gray value is greater than the second gray division threshold.

[0115] Exemplarily, the gray values of all pixel points in the third gray interval can be adjusted to the second preset gray value. Among them, the magnitude of the second preset gray value can be preset by technicians according to different detection requirements.

[0116] In one example, the technician can preset the second preset gray value to 255. That is, the gray values of all pixel points in the third gray interval can be adjusted to 255.

[0117] In another example, the third preset gray value can be set to the maximum gray value in the wafer gray detection image. That is, the gray values of all pixel points in the third gray interval can be adjusted to the maximum gray value in the third gray interval.

[0118] Exemplarily, when using the first gray division threshold and the second gray division threshold as critical values to divide the wafer defect detection image into a first gray interval, a second gray interval, and a third gray interval, all pixel values in the first gray interval can be adjusted to the first preset gray threshold, all gray values in the third gray interval can be adjusted to the second preset gray threshold, and the gray values in the second gray interval can be linearly stretched according to a preset linear stretching algorithm to achieve the adjustment of the gray values in the second gray interval.

[0119] In the embodiments of the present application, by adjusting the gray values in the first gray interval to gray values less than the first gray division threshold, the darker parts in the wafer defect detection image are made more prominent, which helps to identify details in the darker parts. The gray values in the third gray interval are adjusted to gray values greater than the second gray division threshold, so that the brighter parts in the wafer defect detection image are made more prominent, which helps to identify details in the brighter parts. Moreover, the second gray interval can be processed by a preset linear stretching algorithm, which can make the range of change of gray values wider, thereby enhancing the contrast and sense of hierarchy of the image.

[0120] In some alternative embodiments, different gray value adjustment strategies can also be used for different gray intervals for contrast adjustment, different from the above-mentioned preset gray value adjustment strategy. For example, linear stretching algorithms with different parameters can be used for different gray intervals; or, non-linear stretching algorithms can be used for some gray intervals, and linear stretching algorithms can be used for the rest, and so on. Therefore, in the embodiments of the present application, the gray value adjustment strategy for each gray interval is not limited.

[0121] Furthermore, in order to improve the signal-to-noise ratio of the wafer defect detection image, as another implementation manner of the present application, the present application also provides another implementation manner of the processing method of the wafer defect detection image. For specific details, please refer to the following embodiments.

[0122] Figure 5 The flowchart of the wafer defect detection image method provided by an embodiment of the present application is shown. As Figure 5 shown, the wafer defect detection image method includes the following steps:

[0123] S510. Obtain the frequency information of each pixel point in the wafer defect detection image.

[0124] S520. Calculate the cut-off frequency of all pixel points in the wafer defect detection image based on the energy spectrum model.

[0125] S530. Filter the pixel points in the wafer defect detection image according to the frequency information of each pixel point and the cut-off frequency to obtain the filtered wafer defect detection image.

[0126] S540. Determine the target entropy value and the first gray division threshold corresponding to the target entropy value by calculating the gray distribution characteristics of all pixel points in the wafer defect detection image.

[0127] S550. Determine the second gray division threshold based on the maximum gray value among all pixel points in the wafer defect detection image.

[0128] S560. Divide the gray values of all pixel points in the wafer defect detection image into gray intervals according to the first gray division threshold and the second gray division threshold, obtaining multiple gray intervals of the wafer defect detection image.

[0129] S570. Adjust the gray values of the pixel points in the multiple gray intervals according to different preset gray value adjustment strategies corresponding to the multiple gray intervals, obtaining the wafer defect detection image after contrast adjustment.

[0130] In some embodiments, in S510, the frequency information of each pixel point in the wafer defect detection image can be obtained.

[0131] The frequency information of each pixel point can be obtained by acquiring the pixel value of each pixel point in the wafer defect detection image and converting the pixel value of the pixel point into the frequency domain coordinate system.

[0132] In one example, the frequency information of each pixel point can be obtained through the formula of discrete Fourier transform, as shown in the following formula (7):

[0133]

[0134] Among them, f(x, y) represents the pixel value of the wafer defect detection image at the spatial domain (x, y), where the value range of x is [0, M - 1], M is the number of rows of the wafer defect detection image; the value range of y is [0, N - 1], N is the number of columns of the wafer defect detection image; u, v represent the frequency domain coordinates, and F(u, v) is the frequency information corresponding to the pixel point at (x, y);

[0135] In some embodiments, in S520, the cut-off frequency of all pixel points in the wafer defect detection image can be calculated based on the energy spectrum model.

[0136] Exemplarily, the frequency information of all pixel points in the wafer defect detection image can be calculated through the energy spectrum model, and the cut-off frequency corresponding to the wafer defect detection image can be determined according to the frequency information of all pixel points.

[0137] In some alternative embodiments, the cut-off frequency can be determined through the frequency information corresponding to all pixel points and the energy loss constraint condition. Among them, the energy loss constraint condition can be used to represent the constraint condition of the energy of the frequency information corresponding to all pixel points. And the energy constraint condition can be determined in advance by those skilled in the relevant art according to different requirements.

[0138] It can be understood that the energy constraint condition can be determined through the following formula (8) and formula (9):

[0139] E f= α × E (8)

[0140]

[0141] Wherein, E represents the energy of the frequency information corresponding to all pixel points; P(u, v) represents the energy of the pixel point corresponding to (u, v) in the frequency domain coordinate system, and α is an adjustment parameter that can be preset by those skilled in the art.

[0142] In the embodiments of the present application, the energy loss constraint condition is determined by the energy of the frequency information corresponding to all pixel points, and the cut-off frequency is determined according to the energy loss constraint condition and the energy of the frequency information corresponding to each pixel point. When determining the energy loss constraint condition, considering the frequency information of all pixel points, thus more comprehensively understanding the frequency distribution corresponding to all gray values in the wafer defect detection image. The energy loss constraint condition determined in this way can better reflect the gray situation of each pixel point in the wafer defect detection image. The cut-off frequency determined in this way is more practically significant, that is, it can more effectively remove noise while retaining defect features, thereby improving the accuracy of defect detection.

[0143] In some alternative embodiments Figure 6 shows a schematic flow chart of determining the cut-off frequency provided by an embodiment of the present application. As Figure 6 shown, the processing process of determining the cut-off frequency may include the following steps

[0144] S521. Sort the frequency information corresponding to all pixel points according to the frequency magnitude to obtain a frequency sequence.

[0145] Exemplarily, the frequency information corresponding to each pixel point in the wafer defect detection image may be sorted according to the frequency magnitude to obtain a frequency sequence.

[0146] In one example, the frequency information may be sorted in descending order.

[0147] In another example, the frequency information may be sorted in ascending order.

[0148] S522. Calculate the energy information corresponding to each frequency information.

[0149] Exemplarily, the energy information P(u, v) corresponding to each pixel point in the wafer defect detection image may be calculated through some formulas in the energy spectrum model, as shown in the following formula (10):

[0150] P(u, v) = |F(u, v)| 2 (10)

[0151] Wherein, |()| 2 is used to represent the square of the modulus.

[0152] S523. Cumulatively sum the energy information corresponding to the frequency information according to the frequency sequence until the cumulative energy meets the energy loss constraint condition and then stop the summation. Take the last frequency information in the summation as the cut-off frequency.

[0153] Exemplarily, the energy information corresponding to the frequency information can be cumulatively summed according to the frequency sequence until the cumulative energy is greater than or equal to the energy loss constraint condition for the first time. At this time, stop the summation and take the last frequency information in the summation as the cut-off frequency.

[0154] In one example, sum the energy corresponding to each frequency information in ascending order of frequency to obtain the total energy value after summation. Compare the total energy value after summation with the energy loss constraint condition. If it is determined that the total energy value after summation is less than the energy loss constraint condition, then continue to sum the energy value corresponding to the next frequency information. Until the total energy value after summation is greater than or equal to the energy loss constraint condition for the first time, take the frequency information corresponding to this energy value as the cut-off frequency.

[0155] In some alternative embodiments, the frequency information can be sorted to obtain a frequency sequence, and the energy corresponding to each frequency information can be calculated. Cumulatively sum the energy information corresponding to the frequency information according to the determined frequency sequence until the cumulative energy meets the energy loss constraint condition and then stop the summation. Take the last frequency information in the summation as the cut-off frequency. It can be understood that by sorting the frequency information to obtain a frequency sequence, it is possible to more comprehensively understand the frequency distribution corresponding to all gray values in the wafer defect detection image. And, by cumulatively summing the energy information corresponding to the frequency information through the frequency sequence and taking the frequency information corresponding to the preset energy loss constraint condition as the cut-off frequency, the determined cut-off frequency has practical significance and can more effectively remove noise while retaining defect features, thereby improving the accuracy of defect detection.

[0156] In some embodiments, in S530, the pixel points in the wafer defect detection image can be filtered according to the frequency information of each pixel point and the cut-off frequency to obtain a filtered wafer defect detection image.

[0157] Exemplarily, the filtering of the wafer defect detection image can be achieved according to the magnitude relationship between the frequency information of each pixel point and the cut-off frequency.

[0158] In one example, the pixel points with frequency information less than or equal to the cut-off frequency can be used as the filtered pixel points.

[0159] In some embodiments, steps S540 - S570 are the same as steps S210 - S240, and thus will not be elaborated herein.

[0160] In the embodiments of the present application, the cut-off frequency of the wafer defect detection image can be calculated according to the energy spectrum model by obtaining the frequency information of each pixel point in the wafer defect detection image. Using the frequency information of each pixel point and the cut-off frequency, the pixel points in the wafer defect detection image are filtered, thereby improving the signal-to-noise ratio of the wafer defect detection image and realizing the stability and reliability of defect detection. In addition, in the embodiments of the present application, during the filtering process, the cut-off frequency is determined through the frequency information of each pixel point in the wafer defect detection image, and the optimal frequency information for filtering can be automatically determined, realizing the adaptive adjustment of the filtering parameters in image processing, thereby avoiding the problems of decreased equipment throughput and increased detection costs.

[0161] Furthermore, in order to implement the defect detection of the wafer, as another implementation manner of the present application, the present application also provides another implementation manner of the processing method of the wafer defect detection image. For details, please refer to the following embodiments.

[0162] Figure 7 FIG. shows a schematic flowchart of a method for a wafer defect detection image provided by an embodiment of the present application. As Figure 7 shown, the method for the wafer defect detection image includes the following steps:

[0163] S710. Determine the target entropy value and the first gray-scale division threshold corresponding to the target entropy value by calculating the gray-scale distribution characteristics of all pixel points in the wafer defect detection image.

[0164] S720. Determine the second gray-scale division threshold based on the maximum gray-scale value among all pixel points in the wafer defect detection image.

[0165] S730. Divide the gray-scale values of all pixel points in the wafer defect detection image into gray-scale intervals according to the first gray-scale division threshold and the second gray-scale division threshold, and obtain multiple gray-scale intervals of the wafer defect detection image.

[0166] S740. Adjust the gray-scale values of the pixel points in the multiple gray-scale intervals according to different preset gray-scale value adjustment strategies corresponding to the multiple gray-scale intervals, and obtain the wafer defect detection image after contrast adjustment.

[0167] S750. Perform defect detection on the wafer defect detection image after contrast adjustment to obtain a defect detection result.

[0168] Exemplarily, steps S710 - S740 are the same as steps S210 - S240, and thus will not be elaborated herein.

[0169] In some embodiments, in S750, the wafer defect detection image after contrast adjustment can be subjected to defect detection through a defect detection technique to obtain a defect detection result. Among them, the defect detection technique can be a reference-based defect detection technique or a reference-free defect detection technique.

[0170] In the embodiments of the present application, by performing defect detection on the wafer defect detection image after contrast adjustment, more accurate detection results can be obtained, and the stability and reliability of wafer defect detection can be improved.

[0171] Based on the processing method of the wafer defect detection image provided in the above embodiments, correspondingly, the present application also provides a specific implementation manner of the processing device for the wafer defect detection image. Please refer to the following embodiments.

[0172] First, refer to Figure 8 , the processing device for the wafer defect detection image provided in the embodiments of the present application includes the following modules:

[0173] The first determination module 801 is configured to determine a target entropy value and a first gray-scale division threshold corresponding to the target entropy value by calculating the gray-scale distribution characteristics of all pixel points of the wafer defect detection image;

[0174] The second determination module 802 is configured to determine a second gray-scale division threshold based on the maximum gray-scale value among all pixel points of the wafer defect detection image;

[0175] The division module 803 is configured to divide the gray-scale values of all pixel points of the wafer defect detection image into gray-scale intervals according to the first gray-scale division threshold and the second gray-scale division threshold, to obtain multiple gray-scale intervals of the wafer defect detection image, and the multiple gray-scale intervals include at least three gray-scale intervals;

[0176] The adjustment module 804 is configured to adjust the gray-scale values of the pixel points in the multiple gray-scale intervals according to different preset gray-scale value adjustment strategies corresponding to the multiple gray-scale intervals, to obtain a wafer defect detection image after contrast adjustment.

[0177] In one embodiment, the target entropy value is the maximum entropy corresponding to the wafer defect detection image, and the first gray-level division threshold corresponding to the target entropy value is the first gray-level division threshold corresponding to the maximum entropy. The first determination module 801 can determine the target entropy value that meets the preset conditions and the first gray-level division threshold corresponding to the target entropy value by calculating the gray-level distribution characteristics of all pixel points in the wafer defect detection image in the following manner: A division unit is configured to divide the gray-level values of all pixel points in the wafer defect detection image into foreground and background based on each gray-level value of the wafer defect detection image, so as to obtain the foreground gray-level interval and the background gray-level interval corresponding to each gray-level value; A first calculation unit is configured to calculate the foreground entropy corresponding to each gray-level value in the foreground gray-level interval and the background entropy corresponding to each gray-level value in the background gray-level interval respectively according to the gray-level distribution characteristics of each gray-level value in the corresponding foreground gray-level interval and background gray-level interval; A first determination unit is configured to use the sum of the foreground entropy and the background entropy corresponding to each gray-level value as the total entropy value corresponding to each gray-level value; A second determination unit is configured to use the maximum total entropy value among the total entropy values corresponding to multiple gray-level values as the maximum entropy of the wafer defect detection image, and use the gray-level value corresponding to the maximum entropy as the first gray-level division threshold.

[0178] In one embodiment, the first gray-level division threshold is less than the second gray-level division threshold; the multiple gray-level intervals at least include a first gray-level interval, a second gray-level interval, and a third gray-level interval, wherein the critical value between the first gray-level interval and the second gray-level interval is the first gray-level division threshold, and the critical value between the second gray-level interval and the third gray-level interval is the second gray-level division threshold; The adjustment module 804 can adjust the gray-level values of the pixel points in the multiple gray-level intervals according to different preset gray-level value adjustment strategies corresponding to the multiple gray-level intervals to obtain the wafer defect detection image with adjusted contrast in the following manner: A first adjustment unit is configured to adjust the gray-level values of the pixel points in the first gray-level interval to a first preset gray-level value, wherein the first preset gray-level value is less than the first gray-level division threshold; A second adjustment unit is configured to linearly stretch the gray-level values of the pixel points in the second gray-level interval according to a preset linear stretching algorithm; A third adjustment unit is configured to adjust the gray-level values of the pixel points in the third gray-level interval to a second preset gray-level value, wherein the second preset gray-level value is greater than the second gray-level division threshold.

[0179] In one embodiment, before determining a target entropy value that meets a preset condition and a first gray-scale division threshold corresponding to the target entropy value by calculating the gray-scale distribution characteristics of all pixel points in the wafer defect detection image, the apparatus further includes a third determination module, configured to obtain the frequency information of each pixel point in the wafer defect detection image; a fourth determination module, configured to calculate the cut-off frequency of all pixel points in the wafer defect detection image based on an energy spectrum model; and a filtering module, configured to filter the pixel points in the wafer defect detection image according to the frequency information of each pixel point and the cut-off frequency, so as to obtain a filtered wafer defect detection image.

[0180] In one embodiment, the fourth determination module calculates the cut-off frequency of all pixel points in the wafer defect detection image based on the energy spectrum model in the following manner: determining the cut-off frequency according to the frequency information corresponding to all pixel points and an energy loss constraint condition, where the energy loss constraint condition is used to characterize the constraint condition of the energy of the frequency information corresponding to all pixel points.

[0181] In one embodiment, the fourth determination module determines the cut-off frequency according to the frequency information corresponding to each pixel point and the energy loss constraint condition in the following manner: a sorting unit, configured to sort the frequency information corresponding to all pixel points according to the frequency magnitude to obtain a frequency sequence; a second calculation unit, configured to calculate the energy information corresponding to each frequency information; and a third calculation unit, configured to cumulatively calculate the energy information corresponding to the frequency information according to the frequency sequence until the cumulative energy meets the energy loss constraint condition and then stop the accumulation, and use the last frequency information in the accumulation as the cut-off frequency.

[0182] In one embodiment, after obtaining the wafer defect detection image with contrast adjustment, the apparatus further includes: a detection module, configured to perform defect detection on the wafer defect detection image with contrast adjustment to obtain a defect detection result.

[0183] Figure 9 FIG. shows a schematic hardware structure diagram of a method for processing a wafer defect detection image provided by an embodiment of the present application.

[0184] The processing device for the wafer defect detection image may include a processor 901 and a memory 902 storing computer program instructions.

[0185] Specifically, the above-mentioned processor 901 may 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.

[0186] The memory 902 may include a mass memory for data or instructions. By way of example and not limitation, the memory 902 may include a hard disk drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a universal serial bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 902 may include removable or non-removable (or fixed) media. Where appropriate, the memory 902 may be internal or external to the integrated gateway disaster recovery device. In a particular embodiment, the memory 902 is a non-volatile solid-state memory.

[0187] The memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage media device, an optical storage media device, a flash memory device, an electrical, optical, or other physical / tangible memory storage device. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method according to an aspect of the present disclosure.

[0188] The processor 901 reads and executes the computer program instructions stored in the memory 902 to implement any one of the wafer defect detection image processing methods in the above embodiments.

[0189] In one example, the wafer defect detection image processing device may further include a communication interface 903 and a bus 910. Among them, as Figure 9 shown, the processor 901, the memory 902, and the communication interface 903 are connected through the bus 910 and complete communication with each other.

[0190] The communication interface 903 is mainly used to implement communication between the various modules, devices, units, and / or devices in the embodiments of the present application.

[0191] The bus 910 includes hardware, software, or both, and couples the components of the online data flow metering device to each other. By way of example and not limitation, the bus may 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 interconnect, a Low Pin Count (LPC) bus, a memory bus, a MicroChannel 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 other suitable buses or a combination of two or more of these. Where appropriate, the bus 910 may include one or more buses. Although the embodiments of the present application describe and illustrate specific buses, the present application contemplates any suitable bus or interconnect.

[0192] The processing device for the wafer defect detection image can execute the processing method for the wafer defect detection image in the embodiments of the present application based on the gray distribution characteristics of the wafer defect detection image, so as to implement the combination of Figure 2 and Figure 8 the processing method and device for the wafer defect detection image described.

[0193] In addition, in combination with the processing method for the wafer defect detection image in the above embodiments, the embodiments of the present application can be implemented by providing a computer storage medium. Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by a processor, any one of the processing methods for the wafer defect detection image in the above embodiments is implemented.

[0194] The embodiments of the present application also provide a computer program product, including a computer program, and when the computer program is executed by a processor, any one of the processing methods for the wafer defect detection image in the above embodiments is implemented.

[0195] It should be clear that the present application is not limited to the specific configurations and processes described above and illustrated in the figures. For the sake of brevity, the detailed description of known methods is omitted here. In the above embodiments, several specific steps are described and illustrated as examples. However, the method process of the present application is not limited to the specific steps described and illustrated, 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.

[0196] The functional blocks shown in the above-described structural block diagrams can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, it can be, for example, an electronic circuit, an application-specific integrated circuit (ASIC), appropriate firmware, a plug-in, a functional card, and so on. When implemented in software, the elements of the present application are programs or code segments used to perform the required tasks. The program or code segment can be stored in a machine-readable medium or transmitted via a data signal carried in a carrier wave over a transmission medium or a communication link. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical discs, hard disks, fiber optic media, radio frequency (RF) links, and so on. The code segment can be downloaded via a computer network such as the Internet, an intranet, and so on.

[0197] It should also be noted that in the exemplary embodiments mentioned in the present application, some methods or systems are described based on a series of steps or devices. However, the present application is not limited to the order of the above steps, that is, the steps can be executed in the order mentioned in the embodiments, or different from the order in the embodiments, or several steps can be executed simultaneously.

[0198] Aspects of the present disclosure have been described above with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each block in the flowcharts and / or block diagrams, and the combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device enable the implementation of the functions / actions specified in one or more blocks of the flowcharts and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It should also be understood that each block in the block diagrams and / or flowcharts, and the combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or by a combination of dedicated hardware and computer instructions.

[0199] As described above, this is only the specific implementation manner of the present application. Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein. It should be understood that the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present application.

Claims

1. A method for processing wafer defect detection images, characterized in that: include: Determine a target entropy value and a first grayscale division threshold corresponding to the target entropy value by calculating the grayscale distribution characteristics of all pixels of the wafer defect detection image; Determining a second grayscale division threshold based on a maximum grayscale value among all pixels of the wafer defect detection image; Dividing the grayscale values ​​of all pixels of the wafer defect detection image into grayscale intervals according to the first grayscale division threshold and the second grayscale division threshold to obtain a plurality of grayscale intervals of the wafer defect detection image; According to different preset grayscale value adjustment strategies corresponding to the multiple grayscale intervals, contrast adjustment is performed on the grayscale values ​​of the pixels in the multiple grayscale intervals to obtain a wafer defect detection image after contrast adjustment.

2. The method for processing wafer defect detection images according to claim 1, characterized in that: The method of calculating the grayscale distribution characteristics of all pixels of the wafer defect detection image to determine a target entropy value that meets a preset condition and a first grayscale division threshold value corresponding to the target entropy value includes: Based on each grayscale value of the wafer defect detection image, the grayscale values ​​of all pixels in the wafer defect detection image are divided into foreground and background to obtain a foreground grayscale interval and a background grayscale interval corresponding to each grayscale value; According to the grayscale distribution characteristics of each grayscale value in the corresponding foreground grayscale interval and the background grayscale interval, respectively calculating the foreground entropy corresponding to each grayscale value in the foreground grayscale interval and the background entropy corresponding to the background grayscale interval; The sum of the foreground entropy and the background entropy corresponding to each gray value is taken as the total entropy value corresponding to each gray value; The largest total entropy value among the total entropy values ​​corresponding to the multiple grayscale values ​​is used as the maximum entropy of the wafer defect detection image, and the grayscale value corresponding to the maximum entropy is used as the first grayscale division threshold.

3. The method for processing wafer defect detection images according to claim 1, characterized in that: The first grayscale division threshold is less than the second grayscale division threshold; the multiple grayscale intervals include at least a first grayscale interval, a second grayscale interval, and a third grayscale interval, wherein the critical value between the first grayscale interval and the second grayscale interval is the first grayscale division threshold, and the critical value between the second grayscale interval and the third grayscale interval is the second grayscale division threshold; The step of performing contrast adjustment on the grayscale values ​​of the pixels in the multiple grayscale intervals according to different preset grayscale value adjustment strategies corresponding to the multiple grayscale intervals to obtain a contrast-adjusted wafer defect detection image includes: Adjusting the grayscale value of the pixel point in the first grayscale interval to a first preset grayscale value, wherein the first preset grayscale value is less than the first grayscale division threshold; Linearly stretching the grayscale values ​​of the pixels in the second grayscale interval according to a preset linear stretching algorithm; The grayscale value of the pixel point in the third grayscale interval is adjusted to a second preset grayscale value, wherein the second preset grayscale value is greater than the second grayscale division threshold.

4. The method for processing wafer defect detection images according to claim 1, characterized in that: Before determining the target entropy value and the first grayscale division threshold value corresponding to the target entropy value by calculating the grayscale distribution characteristics of all pixels of the wafer defect detection image, the method further includes: Obtaining frequency information of each pixel in the wafer defect detection image; Calculating the cutoff frequency of all pixels in the wafer defect detection image based on an energy spectrum model; According to the frequency information and the cutoff frequency of each pixel point, the pixel points in the wafer defect detection image are filtered to obtain a filtered wafer defect detection image.

5. The method for processing wafer defect detection images according to claim 4, characterized in that: The calculating the cutoff frequency of all pixels in the wafer defect detection image based on the energy spectrum model includes: The cutoff frequency is determined according to the frequency information corresponding to all the pixels and the energy loss constraint condition, wherein the energy loss constraint condition is used to characterize the energy constraint condition of the frequency information corresponding to all the pixels.

6. The method for processing wafer defect detection images according to claim 5, characterized in that: The step of determining the cutoff frequency according to the frequency information corresponding to each pixel point and the energy loss constraint condition includes: Sorting the frequency information corresponding to all the pixels according to the frequency to obtain a frequency sequence; Calculate the energy information corresponding to each frequency information; According to the frequency sequence, energy information corresponding to the frequency information is accumulated until the accumulated energy meets the energy loss constraint condition and the accumulation is stopped, and the last accumulated frequency information is used as the cutoff frequency.

7. The method for processing wafer defect detection images according to any one of claims 1 to 6, characterized in that: After obtaining the wafer defect detection image after contrast adjustment, the method further includes: Defect detection is performed on the contrast-adjusted wafer defect detection image to obtain a defect detection result.

8. A wafer defect detection image processing device, characterized in that: The device comprises: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, the method for processing wafer defect detection images as described in any one of claims 1 to 7 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the method for processing wafer defect detection images as described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that When the instructions in the computer program product are executed by a processor of an electronic device, the electronic device executes the method for processing wafer defect detection images as described in any one of claims 1 to 7.

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