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

Abstract

The embodiment of the invention discloses a wafer defect detection method and device, equipment, a medium and a product. The wafer defect detection method comprises the steps that a to-be-detected wafer image is acquired; the image of the wafer to be detected comprises textures and defects; identifying the texture of the to-be-detected wafer image according to a preset texture identification algorithm to obtain a texture identification result; and determining a detection result of the defect according to the texture recognition result. According to the technical scheme, the texture of the to-be-detected wafer image is recognized according to the preset texture recognition algorithm, then the wafer defect detection result is determined, the influence of the texture on wafer defect recognition is eliminated, the accuracy and recognition efficiency of wafer surface wafer defect detection are improved, and the product yield is improved.

Description

Technical Field

[0001] This disclosure relates to the field of intelligent detection technology, and in particular to a wafer defect detection method, apparatus, equipment, medium, and product. Background Technology

[0002] With the deepening and popularization of industrial automation and intelligence, automated optical inspection (AOI) equipment has replaced traditional manual visual inspection, becoming a technological development trend. Wafer defect detection is currently an important sub-field within this area. Among these, the inspection of back-end wafer processes is the most prominent. In this inspection process, most wafers have undergone multiple processing steps, resulting in surfaces coated with various materials. Due to the diversity of materials, the surface often exhibits a variety of textures due to the variety of coating materials. While the texture on a specific material coating may appear characteristic, the distribution of this texture on the wafer surface becomes irregular due to subtle differences in the angle at which the wafer is placed and variations in surface flatness. Unlike ordinary color difference, which exhibits a certain gradation in grayscale, texture-based color difference possesses a degree of randomness and anisotropy.

[0003] In the field of traditional semiconductor process equipment, pattern-type wafers (wafers that form circuit patterns) are mostly inspected using the D2R (die-to-reference) method. This method uses a die or shot as the smallest repeating unit. By acquiring images of about ten smallest repeating units, the mean map and variance map are trained to provide a reference for subsequent inspection.

[0004] Wafers with reflective properties develop randomly distributed textures, and these color variations can lead to large variance values ​​during training. This, in turn, makes wafer defect detection difficult. Increased variance fluctuations cause the method to accept large deviations from the mean, resulting in decreased detection sensitivity and missed detections of critical wafer defects. Conversely, reducing the impact of variance fluctuations leads to a large number of over-detections due to surface texture color variations, making inspection, evaluation, and review difficult for machine users. Summary of the Invention

[0005] This disclosure provides a wafer defect detection method, apparatus, device, medium, and product that eliminates the influence of texture on wafer defect identification and improves the accuracy of wafer defect detection.

[0006] In a first aspect, a wafer defect detection method is provided, the method comprising:

[0007] Acquire an image of the wafer to be inspected; the image of the wafer to be inspected includes texture and defects;

[0008] The texture of the wafer image to be detected is identified according to a preset texture recognition algorithm to obtain the texture recognition result;

[0009] The detection result of the defect is determined based on the texture recognition result.

[0010] Secondly, a wafer defect detection device is provided, comprising:

[0011] An image acquisition module is used to acquire an image of the wafer to be inspected; the image of the wafer to be inspected includes texture and defects.

[0012] The texture recognition module is used to recognize the texture of the wafer image to be detected according to a preset texture recognition algorithm, and obtain the texture recognition result;

[0013] The defect detection result determination module is used to determine the detection result of the defect based on the texture recognition result.

[0014] Thirdly, an electronic device is provided, comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the wafer defect detection method as described in the first aspect above.

[0018] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the wafer defect detection method as described in the first aspect above.

[0019] Fifthly, a computer program product is provided, the computer program product comprising a computer program that, when executed by a processor, implements the wafer defect detection method as described in the first aspect above.

[0020] This disclosure provides a wafer defect detection method, apparatus, device, medium, and product, comprising: acquiring an image of a wafer to be inspected; the image of the wafer to be inspected including texture and defects; identifying the texture of the image of the wafer to be inspected according to a preset texture recognition algorithm to obtain a texture recognition result; and determining the detection result of the defects based on the texture recognition result. This technical solution, by identifying the texture of the image of the wafer to be inspected according to a preset texture recognition algorithm, thereby determining the wafer defect detection result, eliminates the influence of texture on wafer defect identification, improves the accuracy and efficiency of wafer surface defect detection, and increases product yield.

[0021] It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this disclosure, nor is it intended to limit the scope of the embodiments of this disclosure. Other features of the embodiments of this disclosure will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a wafer defect detection method provided in Embodiment 1 of this disclosure;

[0024] Figure 2 This is a schematic diagram of a test image provided in Embodiment 1 of this disclosure;

[0025] Figure 3 This is a schematic diagram of a mean plot provided in Embodiment 1 of this disclosure;

[0026] Figure 4 This is a schematic diagram of a preset texture reference image provided in Embodiment 1 of this disclosure;

[0027] Figure 5 This is a schematic diagram of a detection result that meets the requirements for passing the inspection, provided in Embodiment 1 of this disclosure;

[0028] Figure 6 This is a schematic diagram of a detection result provided in Embodiment 1 of this disclosure;

[0029] Figure 7 This is a schematic diagram illustrating the execution process of a wafer defect detection method provided in Embodiment 1 of this disclosure;

[0030] Figure 8 This is a schematic diagram of the structure of a wafer defect detection device provided in Embodiment 2 of this disclosure;

[0031] Figure 9 This is a schematic diagram of the structure of an electronic device provided in Embodiment 3 of this disclosure. Detailed Implementation

[0032] To enable those skilled in the art to better understand the solutions of the embodiments of this disclosure, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the protection scope of the embodiments of this disclosure.

[0033] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0034] Example 1

[0035] Figure 1 This is a flowchart of a wafer defect detection method provided in Embodiment 1 of this disclosure. This embodiment is applicable to the detection of wafer defects. The method can be executed by a wafer defect detection device, which can be implemented in hardware and / or software. This wafer defect detection device can be configured in an electronic device, including but not limited to computers, terminals, and servers, which are devices with data processing capabilities. Figure 1 As shown, the method includes:

[0036] S110. Obtain an image of the wafer to be inspected; the image of the wafer to be inspected includes texture and defects;

[0037] In this embodiment, the wafer image to be inspected can be a wafer image containing texture and defects. The wafer texture refers to the microstructure of the wafer surface. Wafer defects can be non-compliant defects or flaws that occur on or inside the wafer during the semiconductor manufacturing process. These defects can severely impact the performance and reliability of the wafer; therefore, classifying and analyzing wafer defects is crucial. Wafer defects can be categorized into several types, such as impurities, cracks, and contamination.

[0038] S120. The texture of the wafer image to be detected is identified according to the preset texture recognition algorithm to obtain the texture recognition result;

[0039] In this embodiment, the preset texture recognition algorithm can be a pre-set algorithm for recognizing textures. By recognizing the texture of the wafer image to be detected according to the preset texture recognition algorithm, the texture recognition result of the wafer image to be detected can be obtained.

[0040] S130. Determine the detection result of the defect based on the texture recognition result.

[0041] Specifically, the wafer image to be inspected includes texture and defects. After obtaining the texture recognition result, the defect detection result of the wafer image to be inspected can be determined. For example, after the texture recognition result of the wafer image to be inspected is identified, the unidentified result is the defect detection result.

[0042] This disclosure provides a wafer defect detection method, comprising: acquiring a wafer image to be inspected; the wafer image to be inspected including texture and defects; identifying the texture of the wafer image to be inspected according to a preset texture recognition algorithm to obtain a texture recognition result; and determining the detection result of the defect based on the texture recognition result, thereby eliminating the influence of texture on wafer defect identification, improving the accuracy and recognition efficiency of wafer surface defect detection, and increasing product yield.

[0043] As an optional implementation of this embodiment, the texture detection wafer defect detection method provided in this embodiment further includes determining the target combination parameters of the preset texture recognition algorithm;

[0044] The process of determining the target combination parameters of the preset texture recognition algorithm includes:

[0045] 1) Acquire test images using an image acquisition device, and obtain the mean map of the test images;

[0046] In this embodiment, an image acquisition device can also be used to acquire a test image, wherein the image acquisition device can be a camera, and the test image can be an image captured by the camera. After obtaining the test image, a mean map acquisition algorithm can be used to process the test image to obtain the mean map of the test image. For example, the mean map can be generated by calculating the average value of each pixel position in all images; alternatively, the mean map can be obtained by using an image processing library that provides a direct mean map calculation function.

[0047] Figure 2 This is a schematic diagram of a test image provided in this embodiment, such as... Figure 2 As shown, the test images can be obtained by taking pictures with a camera. Figure 3 This is a schematic diagram of a mean plot provided in this embodiment, as shown below. Figure 3As shown, the mean map can be obtained by processing the test image using a mean map acquisition algorithm. The mean map does not contain texture.

[0048] 2) Determine a preset texture reference image using the test image and the mean image;

[0049] Specifically, the difference between the mean image and the test image can be used as a preset texture reference image.

[0050] Figure 4 This is a schematic diagram of a preset texture reference image provided in this embodiment, such as... Figure 4 As shown, the red highlight indicates the location of the texture. Figure 5 This embodiment provides a schematic diagram of a detection result that meets the requirements for passing the inspection. Figure 5 It can be seen that, Figure 4 In addition to the texture location, other texture locations were also detected.

[0051] 3) Determine the combined parameters based on the image processing parameters and the convolution kernel processing parameters; the combined parameters include an image size, an image grayscale, and a convolution kernel size;

[0052] In this embodiment, the image processing parameters can be parameters of the image processing process. For example, image processing parameters can include image size and image grayscale, and can include at least one set of image size and image grayscale. Image size can be the width and height of the image, and is typically measured in pixels, such as 800×600. Image grayscale can be the number of different gray levels in the image, which determines the amount of detail in the image's brightness and darkness; image grayscale typically includes 256 (0-255) gray levels. The convolution kernel processing parameters can include at least one convolution kernel size, which can be the size of the convolution kernel. It should be noted that the convolution kernel processing parameters can also include information such as the type of convolution kernel.

[0053] As described above, the combined parameters can be determined based on the image processing parameters and the convolution kernel processing parameters. The combined parameters include an image size, an image grayscale, and a convolution kernel size.

[0054] 4) For each of the combined parameters, the test image is detected based on the convolution kernel of the machine learning algorithm and the combined parameters to obtain the detection result;

[0055] It is known that, after determining multiple combination parameters, for any set of combination parameters, the size of the convolution kernel in the machine learning algorithm can be set to the size of the convolution kernel in the combination parameters, and the image size and image grayscale can be set to the image size and image grayscale in the combination parameters. This allows detection to be performed on the test image to obtain the detection result. The test image can be an image acquired by an image acquisition device, such as a camera; the detection result can be the detected texture, which can be highlighted.

[0056] For example, in the combined parameters, the image size is 100×50, the image grayscale is 5, and the convolution kernel size is one-dimensional convolution kernel × one-dimensional convolution kernel. The image can be processed based on the image size and the image grayscale, and the convolution kernel of the machine learning algorithm can be set to one-dimensional convolution kernel × one-dimensional convolution kernel.

[0057] 5) The detection results are evaluated using an evaluation system and a preset texture reference image to obtain an evaluation result;

[0058] In this embodiment, the evaluation system can be a standard for evaluating the detection results. For example, the evaluation system can be a scoring rule. The preset texture reference map can be a reference map with known texture positions. For example, the preset texture reference map can be a mean map, which can be generated by calculating the average value of each pixel position in all images; or the mean map can be obtained by an image processing library that provides the function of directly calculating the mean map.

[0059] Specifically, by using a preset texture reference image and a test image, the first texture position of the test image can be obtained; the second texture position, detected by combining parameters and a machine learning algorithm, can be obtained from the detection results. After obtaining the first and second texture positions, the overlap between the first and second texture positions can be calculated, and the overlap can be scored using an evaluation system to obtain an evaluation result. For example, if the overlap between the first and second texture positions is 75%, the evaluation result is 75 points.

[0060] 6) Determine target combination parameters based on the evaluation results corresponding to each of the aforementioned combination parameters and the actual detection requirements, and use the target combination parameters to construct a preset texture recognition algorithm.

[0061] Specifically, by combining various parameters and machine learning algorithms, corresponding evaluation results can be obtained. Based on these evaluation results and actual detection requirements, target combination parameters can be determined. Among these, textures exist in different categories, and the evaluation results can determine the sensitivity of each combination parameter to different types of textures.

[0062] As described above, the actual detection requirements may include pre-set over-detection and under-detection requirements for preset texture categories; wherein, the over-detection requirement is the requirement that the evaluation result is higher than a preset threshold; the under-detection requirement is the requirement that the evaluation result is lower than a preset threshold, the preset threshold being a pre-set threshold, for example, the preset threshold can be 100, if the evaluation result is 125, it is an over-detection requirement, which can be understood as the current combination of parameters being able to detect textures other than those in the preset texture reference image; if the evaluation result is 80, it is an under-detection requirement, which can be understood as the current combination of parameters being able to detect some textures in the preset texture reference image.

[0063] For example, the target combination function can be determined according to the detection requirements of different types of textures, or it can be determined according to the actual detection requirements of the preset texture category, such as the need to construct the preset texture category.

[0064] As an optional implementation of this embodiment, the image processing parameters include at least one set of image sizes and image grayscale; the convolution kernel processing parameters include at least one convolution kernel size;

[0065] The determination of combined parameters based on image processing parameters and convolution kernel processing parameters includes:

[0066] Each image processing parameter and each convolution kernel processing parameter are matched separately to obtain multiple combined parameters.

[0067] Specifically, each image processing parameter and each convolution kernel processing parameter can be matched separately to obtain multiple combined parameters. These combined parameters can include a set of image dimensions, an image grayscale level, and the size of the convolution kernel.

[0068] As an optional implementation of this embodiment, the detection of the test image by the convolution kernel based on the machine learning algorithm and the combined parameters to obtain the detection result includes:

[0069] 1) Perform image preprocessing on the test image using the image size and image grayscale to determine the preprocessed test image;

[0070] Specifically, after determining the image size and grayscale, image preprocessing can be performed on the test image based on these parameters to determine the final test image. It should be noted that the image preprocessing process can also include: splitting the color channels and performing data augmentation on both the mean image and the image to be detected from multiple dimensions, enabling the effective information of the image to be identified from multiple dimensions.

[0071] 2) Determine the convolution kernel of the machine learning algorithm based on the convolution kernel size;

[0072] Specifically, after the convolution kernel size is determined, the size of the convolution kernel for the machine learning algorithm can be set based on the convolution kernel size. It should be noted that the type of convolution kernel can also be selected. The types of convolution kernels can include gradient kernels, point kernels, line kernels, ripple kernels, and oscillating kernels, etc.

[0073] 3) The preprocessed test image is detected by a convolutional kernel based on a machine learning algorithm to obtain the detection result, which includes the location of the texture.

[0074] As we know, once the convolution kernel is determined, the test image can be tested based on the convolution kernel of the machine learning algorithm. This yields the detection results, which include texture locations, which can be displayed through highlighting. Figure 6 This is a schematic diagram of a detection result provided in this embodiment, such as... Figure 6 As shown, the red highlighted areas represent the detected texture locations.

[0075] As an optional implementation of this embodiment, the step of evaluating the detection results using an evaluation system and a preset texture reference image to obtain an evaluation result includes:

[0076] 1) Determine the degree of overlap between the texture positions of the preset texture reference image and the detection result; the texture in the preset texture reference image is a preset texture category;

[0077] Specifically, after obtaining the detection result, the degree of overlap between the texture position of the preset texture reference image and the texture position of the detection result can be determined. For example, the degree of overlap is 75%. The texture in the preset texture reference image is a preset texture category, which can be a pre-determined texture category.

[0078] 2) Based on the evaluation system, the degree of overlap of the texture positions is quantified to obtain the evaluation result.

[0079] In this embodiment, the evaluation system can be a scoring rule, which can be determined based on historical detection results. Based on the evaluation system, the degree of texture position overlap can be quantified, thereby obtaining an evaluation result. For example, if the texture position overlap is 75%, after quantification by the evaluation system, the evaluation result is a score, such as 75.

[0080] As an optional implementation of this embodiment, determining the target combination parameters based on the evaluation results corresponding to each combination parameter and the actual detection requirements includes:

[0081] 1) Determine initial combination parameters based on the actual detection requirements; the initial combination parameters include at least one set of combination parameters; the actual detection requirements include pre-set over-detection requirements and under-detection requirements for the preset texture category; the over-detection requirement is the requirement that the evaluation result is higher than a preset threshold; the under-detection requirement is the requirement that the evaluation result is lower than a preset threshold;

[0082] Specifically, actual detection requirements can be based on the actual operating environment. For example, in time detection, texture over-detection is required, and the actual detection requirement can be an over-detection requirement. After obtaining the evaluation results, the sensitivity of each combined parameter to the preset texture category can be obtained. The higher the evaluation result of the combined parameter, the higher the sensitivity of the combined parameter to the preset texture category; the lower the evaluation result of the combined parameter, the lower the sensitivity of the combined parameter to the preset texture category. The initial combined parameters can be determined through the actual detection requirements; the initial combined parameters include at least one set of combined parameters.

[0083] For example, if the actual testing requirement is an over-test requirement, the requirement in the evaluation result that is higher than the preset threshold can be used as the initial combination parameter.

[0084] 2) Select the combination parameters that meet the preset conditions from the initial combination parameters as the target combination parameters.

[0085] Specifically, after obtaining the initial combination parameters, the initial combination parameters can be filtered based on preset conditions, and the combination parameters that meet the preset conditions can be used as target combination parameters. The preset conditions can be pre-set filtering conditions. For example, the preset conditions can be to select the combination parameter with the largest corresponding evaluation result among the initial combination parameters as the target combination parameter.

[0086] Figure 7 This is a schematic diagram illustrating the execution process of a wafer defect detection method provided in this embodiment, as shown below. Figure 7As shown, a camera captures an image (test image), and a mean image is obtained from the test image. By comparing the two images (test image and mean image), the location and region of the texture (preset texture reference image) are obtained. Then, texture learning is performed on the test image. Image preprocessing (image scaling and grayscale compression) and convolution kernel preprocessing (selection of convolution kernel size and combination of one-dimensional convolution kernels) are performed based on combined parameters. Finally, texture learning is performed on the image to be detected (test image) based on machine learning algorithms to obtain the detection result. Based on the preset texture reference image, a scoring mechanism (evaluation system) is set according to the texture location and recorded. At the same time, different image sizes, image grayscales, and convolution kernel sizes can be matched separately. The image to be detected (test image) is repeatedly tested according to each matched combination parameter to obtain multiple scoring results. The parameters with the highest scores (combined parameters) are selected as the learning results (and the detection results are evaluated to obtain evaluation results, and the target combination parameters are determined based on the evaluation results and actual detection requirements). After the target combination parameters are determined, this recognition step is added to the subsequent text detection process. This increases the detection capability and improves the over-detection rate, especially when D2R detection is already sensitive. By recognizing the texture in the image being detected and then filtering the detection results based on location information, a single detection is completed. This allows the D2R detection method to maintain high sensitivity and a good over-detection rate even in high color difference (texture) scenes.

[0087] Example 2

[0088] Figure 8 This is a schematic diagram of the structure of a wafer defect detection device provided in Embodiment 2 of this disclosure; as shown Figure 8 As shown, the device includes: an image acquisition module 210, a texture recognition module 220, and a defect detection result determination module 230.

[0089] The image acquisition module 210 is used to acquire an image of the wafer to be inspected; the image of the wafer to be inspected includes texture and defects.

[0090] The texture recognition module 220 is used to recognize the texture of the wafer image to be detected according to a preset texture recognition algorithm, and obtain the texture recognition result;

[0091] The defect detection result determination module 230 is used to determine the detection result of the defect based on the texture recognition result.

[0092] Embodiment 2 of this disclosure provides a wafer defect detection device that eliminates the influence of texture on wafer defect identification and improves the accuracy of wafer defect detection.

[0093] Furthermore, the device further includes: a target combination parameter determination module; the target combination parameter determination module includes:

[0094] An image acquisition unit is used to acquire test images using an image acquisition device and to acquire the mean image of the test images;

[0095] A preset texture reference map determination unit is used to determine a preset texture reference map using the test image and the mean map;

[0096] A combined parameter determination unit is used to determine combined parameters based on image processing parameters and convolution kernel processing parameters; the combined parameters include an image size, an image grayscale, and a convolution kernel size.

[0097] The detection unit is used to detect the test image based on the convolution kernel of the machine learning algorithm and the combined parameters for each of the combined parameters, and obtain the detection result;

[0098] The evaluation result determination unit is used to evaluate the detection results using an evaluation system and a preset texture reference image to obtain an evaluation result;

[0099] The construction unit is used to determine the target combination parameters based on the evaluation results and actual detection requirements corresponding to each of the combined parameters, so as to construct a preset texture recognition algorithm using the target combination parameters.

[0100] Furthermore, the image processing parameters include at least one set of image dimensions and image grayscale; the convolution kernel processing parameters include at least one convolution kernel size;

[0101] The combined parameter determination unit is also used for

[0102] Each image processing parameter and each convolution kernel processing parameter are matched separately to obtain multiple combined parameters.

[0103] Furthermore, the detection unit is also used for

[0104] The test image is preprocessed using the image size and image grayscale to determine the preprocessed test image;

[0105] The convolution kernel of the machine learning algorithm is determined based on the convolution kernel size;

[0106] A convolutional kernel based on a machine learning algorithm is used to detect the preprocessed test image to obtain detection results, which include the location of textures.

[0107] Furthermore, the evaluation result determination unit is also used for:

[0108] Determine the degree of overlap between the texture positions of the preset texture reference image and the detection result; the texture in the preset texture reference image is a preset texture category;

[0109] Based on the evaluation system, the degree of overlap of the texture positions is quantified to obtain the evaluation result.

[0110] Furthermore, building blocks are also used for:

[0111] The initial combination parameters are determined based on the actual detection requirements; the initial combination parameters include at least one set of combination parameters; the actual detection requirements include pre-set over-detection requirements and under-detection requirements for the preset texture category; the over-detection requirement is the requirement that the evaluation result is higher than a preset threshold; the under-detection requirement is the requirement that the evaluation result is lower than a preset threshold;

[0112] The combination parameters that satisfy the preset conditions from the initial combination parameters are taken as the target combination parameters.

[0113] The wafer defect detection device provided in this disclosure can execute the wafer defect detection method provided in any embodiment of this disclosure, and has the corresponding functional modules and beneficial effects for executing the method.

[0114] Example 3

[0115] Figure 9 A schematic diagram of the structure of an electronic device 10 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the embodiments of the present disclosure described and / or claimed herein.

[0116] like Figure 9 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0117] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0118] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microprocessor, etc. Processor 11 performs the various methods and processes described above, such as wafer defect detection methods.

[0119] In some embodiments, the wafer defect detection method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or mounted on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the wafer defect detection method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the wafer defect detection method by any other suitable means (e.g., by means of firmware).

[0120] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0121] Computer programs for implementing the methods of embodiments of this disclosure may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0122] In the context of embodiments of this disclosure, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0123] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0124] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0125] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0126] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the embodiments of this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of the embodiments of this disclosure can be achieved, and this document does not impose any limitations.

[0127] The specific embodiments described above do not constitute a limitation on the scope of protection of the embodiments disclosed herein. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the embodiments disclosed herein should be included within the scope of protection of the embodiments disclosed herein.

[0128] This disclosure also provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implements the wafer defect detection method provided in any embodiment of this application.

[0129] In implementing a computer program product, computer program code for performing the operations of the embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0130] Note that the above are merely preferred embodiments and the technical principles applied in this disclosure. Those skilled in the art will understand that this disclosure is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the protection scope of this disclosure. Therefore, although the embodiments of this disclosure have been described in detail above, this disclosure is not limited to the above embodiments. More other equivalent embodiments may be included without departing from the concept of this disclosure, and the scope of this disclosure is determined by the scope of the appended claims.

Claims

1. A wafer defect detection method, characterized in that: The method comprises: Acquire a wafer image to be inspected; the wafer image to be inspected includes texture and defects; Identify the texture of the wafer image to be detected according to a preset texture recognition algorithm to obtain a texture recognition result; The defect detection result is determined according to the texture recognition result.

2. The method according to claim 1, characterized in that The method further includes determining target combination parameters of the preset texture recognition algorithm; The process of determining the target combination parameters of the preset texture recognition algorithm includes: Acquire a test image using an image acquisition device, and acquire a mean map of the test image; Determine a preset texture reference image using the test image and the mean image; Determining a combination parameter based on the image processing parameter and the convolution kernel processing parameter; the combination parameter includes an image size, an image grayscale, and a convolution kernel size; For each of the combined parameters, the test image is detected based on the convolution kernel of the machine learning algorithm and the combined parameters to obtain a detection result; Using an evaluation system and a preset texture reference image to evaluate the detection result to obtain an evaluation result; The target combination parameters are determined based on the evaluation results corresponding to each of the combination parameters and the actual detection requirements, so as to construct a preset texture recognition algorithm using the target combination parameters.

3. The method according to claim 2, characterized in that The image processing parameters include at least one set of image size and image grayscale; the convolution kernel processing parameters include at least one convolution kernel size; The determining of the combined parameters based on the image processing parameters and the convolution kernel processing parameters comprises: Each image processing parameter and each convolution kernel processing parameter are matched respectively to obtain multiple combination parameters.

4. The method according to claim 2, characterized in that: The convolution kernel based on the machine learning algorithm and the combined parameters detect the test image to obtain a detection result, including: Performing image preprocessing on the test image using the image size and image grayscale to determine a preprocessed test image; Determining a convolution kernel of the machine learning algorithm according to the convolution kernel size; The convolution kernel based on the machine learning algorithm detects the preprocessed test image to obtain a detection result, which includes the position of the texture.

5. The method according to claim 2, characterized in that: The step of evaluating the detection result by using the evaluation system and the preset texture reference map to obtain the evaluation result includes: Determining the degree of overlap between the texture positions of the preset texture reference image and the detection result; the texture in the preset texture reference image is a preset texture category; Based on the evaluation system, the degree of texture position overlap is quantified to obtain the evaluation result.

6. The method according to claim 2, characterized in that The step of determining the target combination parameters based on the evaluation results corresponding to each combination parameter and the actual detection requirements includes: Determine the initial combination parameters based on the actual detection requirements; the initial combination parameters include at least one group of combination parameters; the actual detection requirements include the pre-set over-detection requirements and missed-detection requirements for the preset texture category; the over-detection requirement is the requirement that the evaluation result is higher than the preset threshold; the missed-detection requirement is the requirement that the evaluation result is lower than the preset threshold; The combination parameters that meet the preset conditions in the initial combination parameters are used as target combination parameters.

7. A wafer defect detection device, characterized in that: include: An image acquisition module, used to acquire an image of the wafer to be inspected; The wafer image to be inspected includes textures and defects; A texture recognition module, used to recognize the texture of the wafer image to be detected according to a preset texture recognition algorithm to obtain a texture recognition result; The defect detection result determination module is used to determine the defect detection result according to the texture recognition result.

8. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the wafer defect detection method as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the wafer defect detection method as described in any one of claims 1 to 6 is implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the computer program implements the wafer defect detection method as described in any one of claims 1 to 6.

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