Wafer defect determination method and device, equipment, medium and product

Abstract

The invention discloses a wafer defect determination method, device and equipment, a medium and a product, and the method comprises the steps: obtaining a noise image of a wafer, and determining the noise image based on a defect image and a reference image of the wafer; performing region division on the noise image based on the gray value of each pixel point in the noise image to obtain a plurality of target regions, and generating a corresponding first noise image based on each target region; performing normalization processing on the noise intensity of each first noise image to obtain a second noise image corresponding to each first noise image; and overlapping the gray values of the plurality of second noise images to obtain a detection image, and determining the defect of the wafer based on the detection image, so that the interference of the noise on defect determination can be reduced by performing normalization processing on the noise intensity, and the efficiency and accuracy of defect detection are improved.

Description

Technical Field

[0001] The present application belongs to the field of image processing, and in particular, relates to a method, device, equipment, medium and product for determining defects of a wafer. Background Art

[0002] At present, integrated circuits are developing towards higher integration and complexity, so defect detection of wafers in integrated circuits is particularly important. In the prior art, wafer defects are often detected by detecting images formed by electrons reflected from the wafer surface through a detection platform composed of an optical detection system and a scanning electron microscope. However, since the electron beam emitted by the scanning electron microscope to the wafer surface often carries noise, the image formed based on the reflected electron beam from the wafer surface also has noise, and the noise levels are different for different materials and different areas on the wafer surface, making it difficult to detect wafer defects based on the above image, and the wafer defect detection efficiency is low. Summary of the invention

[0003] The embodiments of the present application provide a wafer defect determination method, device, equipment, medium and product, which can reduce the interference of noise on defect determination and improve the efficiency and accuracy of defect detection.

[0004] In a first aspect, an embodiment of the present application provides a method for determining defects of a wafer, comprising:

[0005] Acquire a noise image of the wafer, where the noise image is determined based on a defect image and a reference image of the wafer;

[0006] Based on the gray value of each pixel in the noise image, the noise image is divided into regions to obtain a plurality of target regions, and a corresponding first noise image is generated based on each target region;

[0007] Normalizing the noise intensity of each first noise image to obtain a second noise image corresponding to each first noise image;

[0008] The grayscale values ​​of the plurality of second noise images are superimposed to obtain a detection image, and based on the detection image, the defects of the wafer are determined.

[0009] In a second aspect, an embodiment of the present application provides a wafer defect determination device, comprising:

[0010] An acquisition module, used for acquiring a noise image of the wafer, where the noise image is determined based on a defect image and a reference image of the wafer;

[0011] A division module, used for dividing the noise image into regions based on the gray value of each pixel in the noise image to obtain a plurality of target regions, and generating a corresponding first noise image based on each target region;

[0012] A processing module, used for normalizing the noise intensity of each first noise image to obtain a second noise image corresponding to each first noise image;

[0013] The detection module is used to superimpose the grayscale values ​​of the plurality of second noise images to obtain a detection image, and determine the defects of the wafer based on the detection image.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, the device comprising:

[0015] a processor and a memory storing computer program instructions;

[0016] When the processor executes the computer program instructions, it is used to execute the wafer defect determination method of the first aspect.

[0017] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon, and when the computer program instructions are executed by a processor, the wafer defect determination method of the first aspect described above is implemented.

[0018] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when processed by a processor, implements the wafer defect determination method of the first aspect described above.

[0019] The wafer defect determination method, device, equipment, medium and product provided in the embodiments of the present application obtain a noise image of the wafer, divide the noise image into regions based on the grayscale value of each pixel in the noise image, and generate multiple first noise images based on different target regions obtained by the division, and then normalize the noise intensity of the multiple first noise images to obtain multiple second noise images. By normalizing the noise intensity of the first noise images including different target regions, the noise intensity of each different region can be unified, and then the grayscale values ​​of the multiple second noise images are superimposed to obtain a detection image. Determining defects based on the detection image can reduce the interference of noise on defect determination and improve the efficiency and accuracy of defect detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solution of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0021] Figure 1A A schematic diagram of an ideal wafer surface inspection image provided in some embodiments of the present application.

[0022] Figure 1BA schematic diagram of an actual wafer surface inspection image provided for some embodiments of the present application.

[0023] Figure 2 A schematic flow chart of a wafer defect determination method provided in some embodiments of the present application.

[0024] Figure 3A An exemplary noise image schematic diagram provided for some embodiments of the present application.

[0025] Figure 3B An exemplary defect image schematic diagram provided for some embodiments of the present application.

[0026] Figure 3C A schematic diagram of an exemplary reference image provided for some embodiments of the present application.

[0027] Figure 3D A schematic diagram of an exemplary first noise image provided for some embodiments of the present application.

[0028] Figure 3E Another exemplary schematic diagram of a first noise image is provided for some embodiments of the present application.

[0029] Figure 3F Another exemplary first noise image schematic diagram provided for some embodiments of the present application.

[0030] Figure 4A A schematic diagram of an exemplary second noise image provided for some embodiments of the present application.

[0031] Figure 4B Another exemplary second noise image schematic diagram provided for some embodiments of the present application.

[0032] Figure 4C Another exemplary second noise image schematic diagram provided for some embodiments of the present application.

[0033] Figure 5 A schematic flow chart of another wafer defect determination method provided for some embodiments of the present application.

[0034] Figure 6 An exemplary grayscale histogram diagram is provided for some embodiments of the present application.

[0035] Figure 7 A schematic flow chart of another wafer defect determination method provided for some embodiments of the present application.

[0036] Figure 8 Another exemplary detection image schematic diagram provided for some embodiments of the present application.

[0037] Fig. 9 A schematic diagram of a wafer defect determination device provided in some embodiments of the present application.

[0038] Fig.10 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0039] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, 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 to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0040] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0041] Before describing the technical solutions provided by the embodiments of the present application, in order to facilitate the understanding of the embodiments of the present application, the present application first specifically describes the problems existing in the related art:

[0042] At present, in order to improve the performance of semiconductor devices, integrated circuits are developing towards smaller size and higher integration. In order to further reduce the size of integrated circuits, existing integrated circuits have introduced a variety of different materials to participate in the integration, which has led to an increasing number of defects in the device manufacturing process. Therefore, it is particularly important to detect defects on wafers. At present, defect detection devices are often composed of optical detection equipment and scanning electron microscope detection equipment (Scanning Electron Microscope, SEM). The detection principle of SEM is to emit an electron beam to the surface of the wafer and collect the electron beam reflected by the wafer surface for imaging, and detect wafer defects based on the generated image. However, since the electron beam emitted by the above-mentioned SEM to the surface of the wafer often carries noise, the image formed based on the reflected electron beam on the surface of the wafer also has noise, and the noise levels of different materials and different areas on the surface of the wafer are different, which makes it difficult to detect wafer defects based on the above-mentioned image, and the efficiency of wafer defect detection is low. Here, such as Figure 1A As shown, the ideal wafer surface inspection image is as follows Figure 1A As shown, Figure 1A The four grayscale 101 regions are the pattern regions of the wafer. Figure 1A The black area with a grayscale of 0 is the non-pattern area of ​​the wafer. However, due to the noise in the electron beam emitted by the SEM, the actual detection image generated is as follows: Figure 1B , and the applicant further discovered that based on the above Figure 1B The noise levels of different grayscale areas in the detection image shown are different, resulting in the above Figure 1B The efficiency and accuracy of detecting image capture defects are low.

[0043] Based on this, the embodiments of the present application provide a wafer defect determination method, device, equipment, medium and product, which can solve the above problems. Below, a wafer defect determination method provided by the embodiments of the present application is described in detail.

[0044] In some embodiments, Figure 2 As shown, the embodiment of the present application provides a method for determining defects of a wafer, which may include the following steps S210-S240:

[0045] S210: Acquire a noise image of the wafer, where the noise image is determined based on the defect image and the reference image of the wafer.

[0046] You can get Figure 3A Here, the noise image is determined based on the defect image and the reference image of the wafer. In some examples, the noise image can be obtained in advance. Figure 3B The defect image shown and Figure 3C The reference image shown, Figure 3B The four grayscale areas 301 in the figure are the pattern areas of the wafer, and the pattern areas include 3011, 3012, 3013 and 3014. Figure 3B The black non-gray area 302 in FIG. 1 is a non-pattern area of ​​the wafer, for example, Figure 3B There is a central circular defect 303 in the area 302. It can be imagined that the above Figure 3B and Figure 3C Here, the first grayscale value of each pixel in the defective image and the second grayscale value of each pixel in the reference image can be determined, and the grayscale value of each pixel in the noise image is determined based on the difference between each first grayscale value and the corresponding second grayscale value.

[0047] In some examples, the acquired defect image can be first aligned with the noise image to obtain a aligned defect image, and the grayscale value of each pixel in the noise image can be determined by the difference between the third grayscale value of each pixel in the aligned defect image and the corresponding second grayscale value.

[0048] S220: Based on the gray value of each pixel in the noise image, the noise image is divided into regions to obtain a plurality of target regions, and a corresponding first noise image is generated based on each target region.

[0049] The noise image can be divided into regions based on the grayscale value of each pixel in the noise image. Figure 3A The noise image shown is divided into three regions, for example, Figure 3A The first area including 3011 and 3012, the second area including 3013 and 3014, and the third area including 302, and then the corresponding first noise image is generated based on each of the above areas, such as Figure 3D , Figure 3E and Figure 3F , are first noise images generated based on the first area, the second area and the third area respectively.

[0050] In some examples, different grayscale intervals may be divided based on the grayscale value of each pixel, and the noise image may be divided into regions based on the different grayscale intervals.

[0051] S230: Normalizing the noise intensity of each first noise image to obtain a second noise image corresponding to each first noise image.

[0052] The noise intensity of each first noise image can be determined based on each first noise image, based on the sum of the grayscale values ​​of each pixel in each first noise image and the area of ​​each first noise image, or based on the sum of the grayscale values ​​of each pixel in the target area of ​​each first noise image and the area of ​​each first noise image; then the noise intensity of each first noise image is normalized to obtain a second noise image. Here, the noise intensity of the second noise image can be determined based on the ratio of the noise intensity of different first noise images to a preset adjustment ratio. Here, as Figure 4A , Figure 4B and Figure 4C As shown, Figure 4A , Figure 4B and Figure 4C The above Figure 3D , Figure 3E and Figure 3F The corresponding normalized second noise image.

[0053] S240: Superimposing the grayscale values ​​of the plurality of second noise images to obtain a detection image, and determining defects of the wafer based on the detection image.

[0054] The grayscale values ​​of multiple second noise images can be superimposed to obtain a detection image, where the grayscale value of each pixel in the detection image is determined by the sum of the grayscale values ​​of the pixels in each corresponding second noise image. The grayscale value of the pixel point in the first row and first column in the detection image is based on the above Figure 4A The gray value of the pixel in the first row and first column, Figure 4B The grayscale value of the pixel in the first row and first column of Figure 4C The sum of the grayscale values ​​of the pixels in the first row and first column of the image. Similarly, the grayscale value of each pixel in the above image detection is the sum of the grayscale values ​​of the pixels in the first row and first column of the image. Figure 4A , Figure 4B and Figure 4C The sum of the gray values ​​of the pixels in .

[0055] After determining the detection image, the defects of the wafer can be determined based on the detection image. Here, after the detection image is processed based on image processing technology, the defects can be determined based on the processed detection image. For example, the detection image can be filtered to remove noise and the brightness or contrast of the detection image can be adjusted to obtain a processed detection image. Determining defects based on the above-mentioned processed detection image can further improve the accuracy of defect capture.

[0056] The embodiment of the present application obtains a noise image of a wafer, divides the noise image into regions based on the grayscale value of each pixel in the noise image, generates multiple first noise images based on different target regions obtained by the division, and then normalizes the noise intensity of the multiple first noise images to obtain multiple second noise images. By normalizing the noise intensity of the first noise images including different target regions, the noise intensity of different regions can be unified, and then the grayscale values ​​of the multiple second noise images are superimposed to obtain a detection image. Determining defects based on the detection images can reduce the interference of noise on defect determination and improve the efficiency and accuracy of defect detection.

[0057] It can be imagined that in order to improve the accuracy of regional division of noisy images, a grayscale histogram can be established through the grayscale value of each pixel in the noise image, and the target grayscale value interval can be determined based on the grayscale histogram, and then the target area corresponding to different grayscale value intervals can be determined.

[0058] In some embodiments, Figure 5 As shown, the above step S120: based on the gray value of each pixel in the noise image, dividing the noise image into multiple target areas, and generating a corresponding first noise image based on each target area, may include the following steps S510-S540:

[0059] S510: Determine a grayscale histogram based on the grayscale value of each pixel in the noise image, where the grayscale histogram represents the distribution of the number of pixels in the noise image that fall into different preset grayscale intervals.

[0060] The grayscale value of each pixel in the noise image can be determined, and a grayscale histogram can be determined. The grayscale value can be used as the X-axis and the number of pixels can be used as the Y-axis to determine the grayscale histogram. The grayscale histogram can characterize the distribution of the number of pixels in the noise image that fall into different preset grayscale intervals. For example, based on the noise image shown in 3A, the following can be determined: Figure 6 The grayscale histogram shown above Figure 6 The X-axis is the pre-divided grayscale value interval, and the Y-axis is the number of pixels falling into different intervals. For example Figure 6 As shown, it can be determined that the grayscale values ​​corresponding to the three peaks are 21, 89 and 179 respectively.

[0061] S520: Determine multiple target grayscale value intervals based on the grayscale histogram.

[0062] A plurality of target grayscale intervals may be determined based on the grayscale histogram. For example, the target grayscale value interval may be determined based on the intervals where the peak values ​​in the grayscale value histogram are located. Figure 6The grayscale values ​​corresponding to the peaks in the grayscale histogram shown are 21, 89 and 179 respectively. The target grayscale value interval can be determined based on the grayscale values ​​corresponding to the above peaks. For example, the target grayscale value interval can be determined based on the median of the grayscale values ​​corresponding to different peaks. The median of the grayscale value 21 corresponding to the first peak and the grayscale value 89 corresponding to the second peak is determined to be 55, so the first target grayscale value interval can be determined to be [0,55). The second target grayscale value interval can be determined to be [55,133) based on the median 133 of 89 and 179. By analogy, multiple target grayscale value intervals can be determined.

[0063] S530: Based on the multiple target gray value intervals, determine the target area corresponding to the target gray value interval in the noise image, wherein one gray value interval corresponds to one target area.

[0064] Based on the grayscale value of each pixel in the noise image and the above target grayscale value interval, the target area corresponding to the grayscale value interval can be determined. For example, based on Figure 3A In the noisy image shown, three target regions are identified.

[0065] S540: extracting multiple target areas from the noise image to obtain multiple first noise images including the target areas.

[0066] Multiple target areas can be extracted from the noise image to generate a first noise image, for example, Figure 3A Multiple target areas are extracted from the noise image shown in the figure, and 3011 and 3012 in 3A are extracted to generate Figure 3D ,extract Figure 3A 3013 and 3014 are generated in Figure 3E ,extract Figure 3A 302 generated in Figure 3F .

[0067] The embodiment of the present application constructs a grayscale histogram based on the grayscale values ​​of different pixels in the noise image. By establishing the grayscale histogram, the distribution of the grayscale values ​​of the pixels in the noise image in different grayscale value intervals can be clearly displayed. Based on the grayscale histogram, multiple target grayscale value intervals are determined. Based on the target grayscale value intervals, the noise image is divided into regions and multiple target regions are extracted to obtain multiple first noise images. This can accurately divide the noise image into regions and improve the efficiency of region division.

[0068] In some embodiments, before step S230 normalizes the noise intensity of each first noise image to obtain a second noise image corresponding to each first noise image, the defect determination method further includes:

[0069] The sum of the grayscale values ​​of a plurality of pixels in the target area included in each first noise image is calculated; and the noise intensity of each first noise image is determined based on the sum of the grayscale values ​​corresponding to each first noise image and the area of ​​the first noise image.

[0070] Here, the sum of the grayscale values ​​of multiple pixels in the target area included in the first noise image may be calculated, for example, Figure 3D In the first noise image shown, the sum of the grayscale values ​​of the multiple pixels in the target areas 3011 and 3012 can be calculated. It is conceivable that the total area of ​​the multiple first noise images is the same. The noise intensity of each first noise image can be determined based on the ratio of the grayscale value sum to the area of ​​the first noise image.

[0071] In some embodiments, the noise intensity of each first noise image is determined based on the sum of the grayscale values ​​corresponding to each first noise image and the area of ​​the first noise image, and the following conditions are met:

[0072]

[0073] Where K is the noise intensity of each first noise image, S is the area of ​​each first noise image, and a i,j is the grayscale value of the pixel in the i-th row and j-th column in each first noise image.

[0074] The embodiment of the present application calculates the noise intensity of each first noise image based on the above method, which can more intuitively characterize the noise intensity of different grayscale value intervals in the noise image and improve the accuracy of determining the noise intensity.

[0075] In some embodiments, the plurality of first noise images include a target noise image, and the target noise image is a noise image including a non-pattern area of ​​the wafer;

[0076] like Figure 7 As shown, the above step S230: normalizing the noise intensity of each first noise image to obtain a second noise image corresponding to each first noise image may include the following steps S710-S720:

[0077] S710: Determine an adjustment ratio of each first noise image based on the noise intensity of the first noise image and the noise intensity of the target noise image.

[0078] Here, a noise image including a non-pattern area of ​​the wafer may be determined as a target noise image among the plurality of first noise images. Figure 3FAs the target noise image. The adjustment ratio of each first noise image can be determined based on the noise intensity of the first noise image and the noise intensity of the target noise image, for example, the ratio of the noise intensity of each first noise image to the target noise intensity is calculated to determine the adjustment ratio. For example, Figure 3F The target noise intensity is 1. Figure 3D The noise intensity is 3, Figure 3E The noise intensity is 2, so we can determine Figure 3D The adjustment ratio is 3 / 1=3. Figure 3E The adjustment ratio is 2 / 1=2. Figure 3F The adjustment ratio is 1 / 1=1.

[0079] In some examples, the adjustment ratio of each first noise image is determined based on the noise intensity of the first noise image and the noise intensity of the target noise image, and the following conditions are met:

[0080]

[0081] Among them, b is the adjustment ratio of each first noise image, K is the noise intensity of each first noise image, and K z is the noise intensity of the target noise image.

[0082] S720: Normalize the noise intensity of each first noise image based on the adjustment ratio to obtain a second noise image corresponding to each first noise image. The grayscale value of each pixel in the target area of ​​each first noise image can be divided by the adjustment ratio to obtain the corresponding second noise image.

[0083] The noise intensity of the first noise image can be normalized based on the adjustment ratio of each first noise image to obtain a second noise image corresponding to each first noise image. Here, the grayscale value of each pixel in the target area included in the first noise image can be adjusted based on the adjustment ratio to obtain a second noise image. Here, the second noise image includes a first area, and the first area corresponds to the target area in the first noise image. The grayscale value of each pixel in the first area of ​​the second noise image can be determined based on the ratio of the grayscale value of each pixel in the target area included in the first noise image to the adjustment ratio. For example, Figure 3D The ratio of the pixel value of each pixel point 3011 and 3012 in the target area included to the adjustment ratio 3 is determined as follows: Figure 4A The pixel values ​​of the two first regions 401 and 402 are obtained as follows: Figure 4A The second noisy image is shown.

[0084] The embodiment of the present application determines the adjustment ratio of each first noise image based on the noise intensity of the first noise image and the noise intensity of the target noise image, and normalizes the noise intensity of the first noise image by adjusting the ratio, so that the noise intensity of each different first noise image and the noise intensity of the target noise image can be maintained at the same order of magnitude, thereby reducing the impact of noise intensity on captured defects and improving the accuracy of defects determined subsequently.

[0085] In some embodiments, before the above step S210: acquiring the noise image of the wafer, the defect determination method may further include:

[0086] Acquire a defect image and a reference image of the wafer; align the defect image and the reference image based on a preset image alignment algorithm to obtain an aligned defect image; and generate a noise image based on the aligned defect image and the reference image.

[0087] A defect image of a wafer can be obtained. The defect image can be an image of a wafer with defects collected based on a SEM device. The reference image can also be an image of a wafer without defects collected based on the SEM. The defect image and the reference image can be registered based on a preset image alignment algorithm to obtain a registered defect image. The image alignment algorithm can include any one of the following: homography, MeshFlow, and optical flow. A noise image can be generated based on the registered defect image and the reference image. For example, the difference between the grayscale value of each pixel in the pattern area of ​​the wafer included in the registered defect image and the grayscale value of each pixel in the pattern area of ​​the wafer included in the reference image is calculated to obtain the grayscale value of each pixel in the pattern area of ​​the wafer included in the noise image, and generate a noise image.

[0088] The embodiment of the present application can improve the accuracy of defects subsequently determined based on the noise image by acquiring a defect image and a reference image of the wafer before acquiring the noise image, aligning the defect image with the reference image, and then generating a noise image based on the aligned defect image and reference image.

[0089] In some embodiments, before determining the defect of the wafer based on the inspection image, the defect determination method further includes:

[0090] The contrast of the detection image is adjusted to a preset contrast to obtain an adjusted detection image; and based on the detection image, the defects of the wafer are determined, including: based on the adjusted detection image, the defects of the wafer are determined.

[0091] Here, before determining the defect of the wafer based on the detection image, the contrast of the detection image can also be adjusted to a preset contrast, and the contrast of the detection image can be increased to a preset contrast. For example, by increasing the detection contrast to a preset contrast, the following is obtained: Figure 8 The adjusted detection image shown is Figure 8 The center circle area of ​​the adjusted inspection image is a defect, and then the defect of the wafer is determined through the adjusted inspection image.

[0092] In some embodiments, after obtaining the above-mentioned detection image, the detection image can be first filtered to remove noise to obtain a filtered detection image, and then the filtered detection image is adjusted to a preset contrast to obtain an adjusted detection image.

[0093] The embodiment of the present application can further highlight the defect area by adjusting the contrast of the detection image to a preset contrast after determining the detection image, thereby facilitating improving the accuracy of defect determination.

[0094] Based on the same inventive concept, an embodiment of the present application also provides a wafer defect determination device.

[0095] In some embodiments, Fig. 9 As shown, an embodiment of the present application provides a wafer defect determination device, comprising:

[0096] An acquisition module 901 is used to acquire a noise image of a wafer, where the noise image is determined based on a defect image and a reference image of the wafer;

[0097] A division module 902 is used to divide the noise image into regions based on the gray value of each pixel in the noise image to obtain multiple target regions, and generate a corresponding first noise image based on each target region;

[0098] A processing module 903 is used to normalize the noise intensity of each first noise image to obtain a second noise image corresponding to each first noise image;

[0099] The detection module 904 is used to superimpose the grayscale values ​​of the plurality of second noise images to obtain a detection image, and determine the defects of the wafer based on the detection image.

[0100] The embodiment of the present application acquires a noise image of a wafer through an acquisition module, divides the noise image into regions based on the grayscale value of each pixel in the noise image, and generates multiple first noise images based on different target regions divided by the division module. Then, the noise intensity of the multiple first noise images is normalized by the processing module to obtain multiple second noise images. By normalizing the noise intensity of the first noise images including different target regions, the noise intensity of each different region can be unified. Then, the grayscale values ​​of the multiple second noise images are superimposed by the detection module to obtain a detection image. Defects are determined based on the detection images, which can reduce the interference of noise on defect determination and improve the efficiency and accuracy of defect detection.

[0101] In some embodiments, the partitioning module may be specifically used for:

[0102] Based on the grayscale value of each pixel in the noise image, a grayscale histogram is determined, where the grayscale histogram represents the distribution of the number of pixels in the noise image that fall into different preset grayscale intervals;

[0103] Based on the grayscale histogram, multiple target grayscale value intervals are determined;

[0104] Based on the multiple target grayscale value intervals, determining the target area corresponding to the target grayscale value interval in the noise image, wherein one grayscale value interval corresponds to one target area;

[0105] A plurality of target areas are extracted from the noise image to obtain a plurality of first noise images including the target areas.

[0106] In some embodiments, the processing module may also be used to:

[0107] Calculating the sum of grayscale values ​​of a plurality of pixels in the target area included in each first noise image;

[0108] The noise intensity of each first noise image is determined based on the sum of the grayscale values ​​corresponding to each first noise image and the area of ​​the first noise image.

[0109] In some embodiments, the plurality of first noise images include a target noise image, and the target noise image is a noise image including a non-pattern area of ​​the wafer;

[0110] Processing modules can be used to:

[0111] Determining an adjustment ratio of each first noise image based on the noise intensity of the first noise image and the noise intensity of the target noise image;

[0112] The noise intensity of each first noise image is normalized based on the adjustment ratio to obtain a second noise image corresponding to each first noise image.

[0113] In some embodiments, the acquisition module may be specifically used to:

[0114] Acquire defect images and reference images of wafers;

[0115] The defect image and the reference image are registered based on a preset image alignment algorithm to obtain a registered defect image;

[0116] A noise image is generated based on the registered defect image and the reference image.

[0117] In some embodiments, the detection module may also be used to:

[0118] Adjusting the contrast of the detection image to a preset contrast to obtain an adjusted detection image;

[0119] Based on the adjusted inspection image, defects of the wafer are determined.

[0120] The device of the above embodiment is used to implement the defect determination method of the corresponding wafer in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0121] like Fig.10 , a schematic diagram of the hardware structure of an electronic device provided in an application embodiment.

[0122] The electronic device 1000 may include a processor 1001 and a memory 1002 storing computer program instructions.

[0123] Specifically, the processor 1001 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.

[0124] The memory 1002 may include a large capacity memory for data or instructions. By way of example and not limitation, the memory 1002 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. In appropriate cases, the memory 1002 may include a removable or non-removable (or fixed) medium. In appropriate cases, the memory 1002 may be inside or outside the integrated gateway disaster recovery device. In a specific embodiment, the memory 1002 is a non-volatile solid-state memory.

[0125] In certain embodiments, memory 1002 includes a read-only memory (ROM). The ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM), or flash memory, or a combination of two or more of the above, where appropriate.

[0126] The memory may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage medium device, an optical storage medium device, a flash memory device, an electrical, optical or other physical / tangible memory storage device. Thus, typically, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) 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 the first aspect of the present application.

[0127] The processor 1001 implements any one of the wafer defect determination methods in the above embodiments by reading and executing computer program instructions stored in the memory 1002 .

[0128] In one example, the electronic device may further include a communication interface 1003 and a bus 1004. For example, the processor 1001, the memory 1002, and the communication interface 1003 are connected via the bus 1004 and communicate with each other.

[0129] The communication interface 1003 is mainly used to implement communication between various modules, devices, units and / or equipment in the embodiments of the present application.

[0130] Bus 1004 includes hardware, software or both, and the parts of online data flow billing equipment are coupled to each other. For example, but not limitation, bus may include accelerated graphics port (AGP) or other graphics bus, enhanced industrial standard architecture (EISA) bus, front-end bus (FSB), hypertransport (HT) interconnection, industrial standard architecture (ISA) bus, infinite bandwidth interconnection, low pin count (LPC) bus, memory bus, micro channel architecture (MCA) bus, peripheral component interconnection (PCI) bus, PCI-Express (PCI-X) bus, serial advanced technology attachment (SATA) bus, video electronics standard association local (VLB) bus or other suitable bus or two or more of these combinations. In appropriate cases, bus 1004 may include one or more buses. Although the present application embodiment describes and shows a specific bus, the present application considers any suitable bus or interconnection.

[0131] The electronic device of the above-mentioned embodiment is used to implement the defect determination method of the corresponding wafer in any of the above-mentioned embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0132] In addition, in combination with the wafer defect determination method in the above embodiment, the embodiment of the present application can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when the computer program instructions are executed by the processor, any one of the wafer defect determination methods in the above embodiment is implemented.

[0133] In addition, in combination with the wafer defect determination method in the above embodiment, the embodiment of the present application can provide a computer program product for implementation. When the computer program product instructions are executed by a processor of an electronic device, any wafer defect determination method in the above embodiment is implemented.

[0134] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0135] The functional blocks shown in the above-described block diagram 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 function card, etc. When implemented in software, the elements of the present application are programs or code segments that are used to perform the required tasks. The program or code segment can be stored in a machine-readable medium, or transmitted on a transmission medium or a communication link by a data signal carried in a carrier wave. "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 disks, hard disks, optical fiber media, radio frequency (RF) links, etc. The code segment can be downloaded via a computer network such as the Internet, an intranet, etc.

[0136] It should also be noted that the exemplary embodiments mentioned in this application describe some methods or devices based on a series of steps or devices. However, this application is not limited to the order of the above steps, that is, the steps can be performed in the order mentioned in the embodiment, or in a different order from the embodiment, or several steps can be performed simultaneously.

[0137] The above reference is according to the method of the embodiment of the present application, the flow chart of the device (device) and the computer program product and / or the block diagram described various aspects of the present application.It should be understood that each square box in the flow chart and / or the block diagram and the combination of each square box in the flow chart and / or the block diagram can be realized by computer program instructions.These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine so that these instructions executed by the processor of the computer or other programmable data processing device enable the realization of the function / action specified in one or more square boxes of the flow chart and / or the block diagram.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 can also be understood that each square box in the block diagram and / or the flow chart and the combination of the square boxes in the block diagram and / or the flow chart can also be realized by the dedicated hardware that performs the specified function or action, or can be realized by the combination of dedicated hardware and computer instructions.

[0138] The above is only a specific implementation of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described devices, modules and units can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here. It should be understood that the protection scope of the present application is not limited to this. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application.

Claims

1. A method for determining defects of a wafer, characterized in that: include: Acquire a noise image of the wafer, wherein the noise image is determined based on a defect image and a reference image of the wafer; Based on the gray value of each pixel in the noise image, the noise image is divided into regions to obtain a plurality of target regions, and a corresponding first noise image is generated based on each of the target regions; Normalizing the noise intensity of each of the first noise images to obtain a second noise image corresponding to each of the first noise images; The grayscale values ​​of the plurality of second noise images are superimposed to obtain a detection image, and defects of the wafer are determined based on the detection image.

2. The wafer defect determination method according to claim 1, characterized in that: The step of dividing the noise image into multiple target areas based on the grayscale value of each pixel in the noise image, and generating a corresponding first noise image based on each target area includes: Based on the grayscale value of each pixel in the noise image, a grayscale histogram is determined, wherein the grayscale histogram represents the distribution of the number of pixels in the noise image that fall into different preset grayscale intervals; Based on the grayscale histogram, determining a plurality of target grayscale value intervals; Based on the multiple target grayscale value intervals, determining a target area corresponding to the target grayscale value interval in the noise image, wherein one grayscale value interval corresponds to one target area; A plurality of target regions are extracted from the noise image to obtain the plurality of first noise images including the target regions.

3. The wafer defect determination method according to claim 1, characterized in that: Before normalizing the noise intensity of each of the first noise images to obtain a second noise image corresponding to each of the first noise images, the defect determination method further includes: Calculating the sum of grayscale values ​​of a plurality of pixels in the target area included in each of the first noise images; The noise intensity of each first noise image is determined based on the sum of the grayscale values ​​corresponding to each first noise image and the area of ​​the first noise image.

4. The wafer defect determination method according to claim 1, characterized in that: The plurality of first noise images include a target noise image, and the target noise image is a noise image including a non-pattern area of ​​the wafer; The normalizing the noise intensity of each of the first noise images to obtain a second noise image corresponding to each of the first noise images includes: determining an adjustment ratio of each of the first noise images based on the noise intensity of the first noise image and the noise intensity of the target noise image; The noise intensity of each first noise image is normalized based on the adjustment ratio to obtain the second noise image corresponding to each first noise image.

5. The wafer defect determination method according to claim 1, characterized in that: Before acquiring the noise image of the wafer, the defect determination method further includes: Acquire defect images and reference images of wafers; Based on a preset image alignment algorithm, the defect image and the reference image are registered to obtain a registered defect image; The noise image is generated based on the registered defect image and the reference image.

6. The wafer defect determination method according to claim 1, characterized in that: Before determining the defect of the wafer based on the detection image, the defect determination method further includes: Adjusting the contrast of the detection image to a preset contrast to obtain an adjusted detection image; The step of determining defects of the wafer based on the detection image includes: Defects of the wafer are determined based on the adjusted inspection image.

7. A wafer defect determination device, characterized in that: include: An acquisition module, used for acquiring a noise image of a wafer, wherein the noise image is determined based on a defect image and a reference image of the wafer; A division module, configured to divide the noise image into regions based on the gray value of each pixel in the noise image to obtain a plurality of target regions, and generate a corresponding first noise image based on each of the target regions; A processing module, configured to normalize the noise intensity of each of the first noise images to obtain a second noise image corresponding to each of the first noise images; The detection module is used to superimpose the grayscale values ​​of the multiple second noise images to obtain a detection image, and determine the defects of the wafer based on the detection image.

8. An electronic device, characterized in that: The device comprises: a processor, and a memory storing computer program instructions; The processor reads and executes the computer program instructions to implement the wafer defect determination method according to any one of claims 1 to 6.

9. A readable storage medium, characterized in that: The readable storage medium stores computer program instructions, and when the computer program instructions are executed by a processor, the wafer defect determination method according to any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is processed by a processor, the defect determination method of the wafer as claimed in any one of claims 1 to 6 is implemented.

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