Chip detection method, system and device and storage medium

Abstract

The invention relates to the technical field of semiconductor detection, provides a semiconductor machine vision detection method, and particularly relates to a chip detection method, system and device and a storage medium. A space domain of an image is converted into a frequency domain, and whether defects exist or not is determined according to the distribution condition of frequency domain features; and if the image has the defect, determining a defect edge for the image based on region gray calculation, and obtaining a defect form according to the defect edge. According to the method provided by the embodiment of the invention, the problem that the detection cost of small target defects or sub-pixel-level target defects is relatively high in the prior art can be solved, and the detection efficiency is improved and the detection cost is reduced on the basis of ensuring the detection accuracy.

Description

Technical Field

[0001] The present application relates to the field of semiconductor detection technology, and is a semiconductor machine vision detection method, and specifically to a chip detection method, system, device and storage medium. Background Art

[0002] The chip's defect detection capability continues to improve with the improvement of shooting requirements. The current process of mainstream domestic suppliers has reached 0.6um, which has reached the limit of optical imaging. If the defect control is lowered, a large number of over-inspections will occur. Loose defect control cannot detect sub-pixel defects. For defects smaller than the pixel size, the sub-pixel frequency domain detection method is used for detection.

[0003] For current chips, especially CMOS high-precision technology, under the condition of 10x optical resolution lens detection, the chip bayer array format has been displayed on the spatial image, showing a periodic lattice pattern, and defects are also hidden in the array format, which makes it difficult to detect extremely small defects in traditional spatial domain images. Summary of the invention

[0004] In order to solve the above problems, the present application provides a chip detection method, system, device and storage medium, which detects extreme defects in the frequency domain. In the frequency domain, the main task is to distinguish between the background periodic frequency and the defect frequency period, and detect the extreme defects through an accurate screening algorithm.

[0005] In order to achieve the above purpose, the technical solution adopted in the embodiment of the present application is as follows:

[0006] In a first aspect, a chip detection method is provided, the method comprising: converting pixel information of a target area image of a chip to be detected into frequency domain information by signal type conversion, and determining whether the target area has a defect based on the abnormal distribution of the frequency domain information; the abnormal distribution of the frequency domain information includes a high-frequency signal exceeding a preset frequency domain threshold; when there is the high-frequency signal, traversing the grayscale changes of the target area image, and when a grayscale change occurs and the grayscale change is greater than the change threshold, it indicates that the grayscale change position is the edge position of the defect, and it is marked.

[0007] In some specific implementations, converting the signal type of the pixel information into frequency domain information includes: traversing the target area image through a sliding window, and performing Fourier transform on the target area image to obtain a power spectrum of the target area image; the power spectrum is an expression form of the frequency domain information.

[0008] In some specific implementations, determining whether the target area has defects based on abnormal distribution of frequency domain information includes: screening the power spectrum according to a preset power threshold, and when the frequency domain characteristics are higher than the power threshold, it indicates that the target area has defects.

[0009] In some specific implementations, the method further includes: after having the high-frequency signal, enhancing the contrast of the target area image to obtain a target enhanced image, and traversing the grayscale changes of the target enhanced image through a sliding window.

[0010] In some specific implementations, enhancing the contrast of the target area image includes: performing two enhancement processes on the target area image respectively to obtain a first enhanced image and a second enhanced image, and performing pyramid reconstruction on the first enhanced image and the second enhanced image to obtain a target enhanced image.

[0011] In some specific implementations, pyramid reconstruction is performed on the first enhanced image and the second enhanced image, including: respectively obtaining exposure weight maps corresponding to the first enhanced image and the second enhanced image, and obtaining high-frequency information layered maps corresponding to the first enhanced image and the second enhanced image, and splicing the weight map and the layered map to obtain a first spliced ​​map and a second spliced ​​map respectively, and reconstructing the target enhanced image based on the first spliced ​​map and the second spliced ​​map.

[0012] In some specific implementations, the first enhanced image and the second enhanced image are processed based on a Gaussian pyramid to obtain a first weight map and a second weight map; the first enhanced image and the second enhanced image are processed based on a Laplacian pyramid to obtain a first layered map and a second layered map.

[0013] In a second aspect, a chip detection system is provided, the system comprising a dark field light source, a detector assembly and a processing device, the light source being used to project an incident light beam onto a target area of ​​a chip to be detected, the detector assembly being used to collect a reflected light beam and to image the target area to obtain an image of the target area, the processing device being used to receive the image of the target area and execute any of the chip detection methods described above.

[0014] According to a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements any of the above-mentioned chip detection methods when executing the computer program.

[0015] In a fourth aspect, a storage medium is provided, on which a computer program is stored, characterized in that when the computer program is executed by a processor, a chip detection system as described in any one of the above items is implemented.

[0016] In the technical solution provided in the embodiment of the present application, the spatial domain of the image is converted into the frequency domain and the presence of a defect is determined based on the distribution of the frequency domain features; if there is a defect, the defect edge is determined based on the regional grayscale calculation of the image, and the defect morphology is obtained based on the defect edge. The method provided in this embodiment can solve the problem of high detection cost for small target defects or sub-pixel level target defects in the prior art, improve the detection efficiency and reduce the detection cost on the basis of ensuring the detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] The methods, systems and / or programs in the accompanying drawings will be further described according to exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments, wherein example numbers represent similar mechanisms in the various views of the accompanying drawings.

[0019] Figure 1 It is a schematic diagram of the structure of the chip detection system provided in the embodiment of the present application.

[0020] Figure 2 It is a schematic diagram of the COMS chip structure in the embodiment of the present application.

[0021] Figure 3 It is an image of the photosensitive area of ​​the COMS chip in the embodiment of the present application.

[0022] Figure 4 It is a schematic flow chart of the chip detection method provided in the embodiment of the present application.

[0023] Figure 5 It is a schematic diagram of the structure of a chip detection device provided in an embodiment of the present application.

[0024] Figure 6 It is a schematic diagram of the server structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] In order to better understand the above technical scheme, the technical scheme of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0026] In the following detailed description, numerous specific details are set forth by way of example in order to provide a comprehensive understanding of the relevant guidance. However, it will be apparent to those skilled in the art that the present application may be practiced without these details. In other cases, well-known methods, procedures, systems, compositions and / or circuits have been described at a relatively high level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.

[0027] Flowcharts are used in the present application to illustrate the execution process performed by the system according to the embodiment of the present application. It should be clearly understood that the execution process of the flowchart may not be performed in order. On the contrary, these execution processes may be performed in reverse order or simultaneously. In addition, at least one other execution process may be added to the flowchart. One or more execution processes may be deleted from the flowchart.

[0028] Before further describing the embodiments of the present invention in detail, the nouns and terms involved in the embodiments of the present invention are described. The nouns and terms involved in the embodiments of the present invention are subject to the following interpretations.

[0029] (1) In response, it is used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or with a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0030] (2) Based on is used to indicate the conditions or states on which the executed operations depend. When the dependent conditions or states are met, one or more operations executed may be in real time or have a set delay. Unless otherwise specified, there is no restriction on the order in which the multiple operations executed are executed.

[0031] See also Figure 1 The present embodiment provides a chip detection system 10 for detecting the surface of a chip 20, comprising a dark field light source 11, a detector assembly 12 and a processing device 13, wherein the dark field light source is used to project an incident light beam to a target area of ​​the chip to be detected and reflect the incident light beam through the surface of the chip to be detected to form a reflected light beam, and the detector assembly receives the reflected light beam and performs imaging to obtain an image of the target area as a target area image.

[0032] The chip of this embodiment is specifically a COMS chip. The target area of ​​the detection of the COMS chip in the scene of this embodiment is the photosensitive area. For the structure of the COMS chip and the photosensitive area of ​​the COMS chip, please refer to Figure 2 As shown. The COMS chip mainly includes modules such as the photosensitive area array (Bayer array, or pixel array), timing control, analog signal processing, and analog-to-digital conversion. The photosensitive area is the part of the CMOS image sensor that completes the photoelectric conversion. It is composed of many pixels, each of which is responsible for capturing light and converting it into an electronic signal. Under the current CMOS high-precision process and 10x optical resolution lens detection, the chip bayer array format has been shown on the spatial image, showing a periodic lattice pattern, and defects are also hidden in the array format, which makes it difficult to detect extremely small defects on traditional spatial domain images.

[0033] Therefore, in order to solve the above technical problems, the present invention provides a chip detection method, which is applied to the processing device in the above chip detection system, and determines whether the chip, especially the COMS chip, has defects by acquiring the target area image, i.e., the photosensitive area image, collected by the sensor component for processing.

[0034] See also Figure 4 , the method includes the following steps:

[0035] Step S41: converting the pixel information of the target area image of the chip to be inspected into frequency domain information, and determining whether the target area has defects based on the abnormal distribution of the frequency domain information.

[0036] See also Figure 3 Regarding the schematic diagram of the COMS chip photosensitive area image collected by the chip detection system, it can be seen from the figure that it is difficult to detect defects in this scene in traditional spatial domain images, and sub-pixel detection is generally required. The processing resources and processing costs required for sub-pixel detection are relatively high. If sub-pixel detection is performed directly on each photosensitive area image, the detection cost will increase. Therefore, in this embodiment, it is first necessary to determine whether there is a possibility of defects in this target area. If there is a possibility, sub-pixel detection is performed for image detection. If not, it is ignored. Through the above process, the waste of algorithm resources can be reduced and the detection efficiency can be improved.

[0037] In this embodiment, it is first necessary to perform frequency domain conversion on the image of the target area, that is, convert the image from the spatial domain to the frequency domain, and determine whether there is a possibility of defects in the COMS chip based on the distribution of the frequency domain, thereby reducing the processing cost problem brought about by direct image detection.

[0038] In this embodiment, the abnormal distribution of frequency domain information includes a high-frequency signal that exceeds a preset frequency domain threshold. The abnormal features in the image are expressed as high-frequency signals in the frequency domain, so in this embodiment, after the image is converted from the spatial domain to the frequency domain, it is determined based on the distribution in the frequency domain whether it contains a high-frequency signal. If there is a high-frequency signal, it indicates that there may be a defect.

[0039] Specifically, the spatial domain-time domain conversion is achieved through Fourier transform, wherein the processing process is to traverse the target area, i.e., the photosensitive area image, through a sliding window and perform Fourier transform on the image to obtain a power spectrum of the target area image, and then screen the power spectrum according to a preset power threshold to determine whether there is a frequency domain feature higher than the power threshold. If so, it means that there is a high-frequency signal, and it is determined that there is an abnormality in the area.

[0040] The two-dimensional discrete Fourier transform is used for the Fourier transform, and the following formula is shown for this transform: Where f(x,y) is the target area image of size M*N, x=0,1,2,…,M-1, y=0,1,2,…,N-1, u=0,1,2…,M-1, v=0,1,2,…,N-1. Then, high-pass, band-pass and low-pass filters are used to separate the different frequency components in the spectrum image. Finally, the inverse discrete Fourier transform is performed on the obtained high-frequency, medium-frequency and low-frequency components to restore the frequency components back to the spatial domain and obtain the semantic information of the corresponding high-frequency, medium-frequency and low-frequency channels. The inverse discrete Fourier transform is as follows:

[0041] In this embodiment, the above process can be used to preliminarily screen abnormalities in the target area to determine whether subsequent detection is necessary. If there are high-frequency features, the subsequent image detection process is performed, avoiding the problem of increased detection costs caused by directly using image detection in the prior art.

[0042] Step S42. When the high-frequency signal is present, the grayscale changes of the target area image are traversed. When a grayscale change occurs and the grayscale change is greater than a change threshold, it indicates that the grayscale change position is the edge position of a defect and is marked.

[0043] If there is a high-frequency signal exceeding the preset threshold in step S41, it means that there is a defect abnormality, and in order to determine the type of defect abnormality and the corresponding feature information such as size, it is necessary to perform defect detection on the image. Because the defect in the present invention is a sub-pixel defect, the present invention performs sub-pixel defect detection on the initially screened abnormal image to determine the characteristics corresponding to the defect.

[0044] In this embodiment, the grayscale change in the image is used to determine whether it is the edge position of the defect for defect detection. In order to further improve the expression ability of defect information, in this embodiment, the grayscale change is more obvious by enhancing the contrast of the target area image, thereby improving the accuracy of detection.

[0045] Specifically, a fusion enhancement method is proposed for the contrast enhancement method in this embodiment. The processing logic of this method is to enhance the target area image separately through two enhancement processing methods, wherein the above two enhancement processing methods are the GUM algorithm and the MSRCR algorithm. Among them, the GUM algorithm focuses on obtaining the edge information of the image, enhancing the brightness and clarity of the image while enhancing the contrast; because noise will be introduced into the image during the fusion process, which will cause the local details of the image to be lost, this embodiment improves the image quality through the MSRCR algorithm. This embodiment fuses the enhanced image enhanced by the GUM algorithm and the enhanced image enhanced by the MSRCR algorithm to obtain the image before and after the overall enhancement. Among them, the processing process of enhancing the target area image by the GUM algorithm and the MSRCR algorithm can be implemented by the algorithm in the prior art, which will not be repeated in this embodiment.

[0046] The images enhanced by the above method are respectively the first enhanced image and the second enhanced image. G (x,y) and F M (x, y). Although this method effectively maintains the high fidelity of the image and improves the contrast of the local domain of the image, it also has the problem of large image noise and insufficient brightness. Therefore, in order to further improve the contrast of the image, this embodiment fuses and reconstructs the above two enhanced images, and uses the reconstructed image as the target enhanced image.

[0047] Among them, a pyramid reconstruction scheme is adopted for the two fusion reconstruction processes. The specific process is to obtain the weight maps corresponding to the first enhanced image and the second enhancement respectively, wherein the weights of the weight maps are determined based on the corresponding exposure levels. Specifically, the pixel values ​​are first normalized within [0,1], and then the above pixel values ​​are set to the average value. At this time, the pixels have a relatively good exposure rate. In order to obtain a suitable weight value, the square of the distance between the input first enhanced image and the second enhanced image and the average value of each pixel is calculated. The calculation of the weight maps corresponding to the first enhanced image and the second enhanced image is based on the following expression: Where A represents the average value, k represents the selection operation of the output index, and F k (x,y) represents the input variable F k The value of , σ represents the standard deviation threshold of the selected setting.

[0048] The weight graphs obtained above are normalized to obtain the corresponding weight graphs: The above processing is based on Gaussian pyramid processing, and the information of each layer of the corresponding weight map is multiplied by the information of each layer of the image after Laplacian pyramid processing at the pixel level. The output image corresponding to the first enhanced image F(x, y) is: The calculation formula is as follows: Where l represents the total number of pyramid levels, G{W} is the image obtained by Gaussian pyramid transformation of the normalized weight map W, and L{F} is the multi-scale spatial structure constructed by Laplace pyramid transformation of the input image F.

[0049] Similarly, for the second enhanced image F M (x, y) is also calculated according to the above processing method, and the calculation formula is as follows:

[0050] Finally, for the above two output images Pyramid reconstruction is performed to obtain the target enhanced image, which is expressed based on the following formula: Where V(x,y) represents the final output target enhanced image.

[0051] After the above image enhancement, the target enhanced image is subjected to sub-pixel detection, wherein the regional grayscale method is used for edge detection in this embodiment. The regional grayscale method traverses the target enhanced image region in a sliding window manner to calculate whether there is a grayscale change. If there is a grayscale change and the grayscale change is greater than a threshold, it means that the changed position is the edge position of the defect.

[0052] Specifically, the processing logic of the regional grayscale method of this embodiment is to use a sliding window to perform grayscale indexing on the target enhanced image, and determine whether there is an edge based on the grayscale change of the sliding window edge. The grayscale values ​​measured for the sliding window edge are A and B.

[0053] Among them, the area proportions represented by A and B are S A and S B , thus the gray value of pixel (i, j) on the target enhanced image can be expressed by measuring the gray value and the area occupied as follows: Where h represents the side length of the pixel unit, h 2 Represents the area of ​​the pixel unit, and h 2 =S A +S B , by P i,j It represents the proportion of the gray value area of ​​the detected object in the (i, j) pixel unit.

[0054] In this embodiment, the initial sliding window sampling is linear sampling, and the edge line slope configured first is configured with a slope greater than 1, and the window size used is 5×3, so that it can completely penetrate the window, and its expression is y=a+bx. According to the regional grayscale algorithm, the total grayscale value of the detected object in the window can be calculated, and S L , S M and S R Represent the sum of the grayscale values ​​of the three columns in the sliding window, then the expression is: Where L, M, and R represent the area below the edge line in each column. These areas can be solved by integrating the straight line expression, which is specifically expressed based on the following formula:

[0055]

[0056] Based on the above two sets of functional relationships, the straight line expression parameters a and b can be determined as follows:

[0057]

[0058] Select the point farthest from the edge line and take the average to calculate A and B: Among them, m represents the inclination direction of the edge of the straight line, and its specific value is determined by the relationship between the partial derivatives:

[0059] The grayscale values ​​of A and B can be determined through the above processing, and whether it is an edge position can be determined based on the comparison between the grayscale value difference of A and B and the threshold. In the prior art, the threshold is set manually.

[0060] In other embodiments, in order to make the comparison threshold setting more accurate, the Otsu algorithm can be used to adaptively calculate the threshold. This process can be implemented using an algorithm in the prior art and will not be described in detail in this embodiment.

[0061] Through the above steps, the image can be completely traversed to determine the area with abnormal grayscale changes and the edge of the area. This edge is the defect edge position to be detected in this embodiment. Based on this defect edge position, the defect morphology can be determined. For further measurement of the defect morphology, algorithms in the prior art such as the least squares method can be used for fitting to achieve defect size detection and other detection tasks, which will not be repeated in this embodiment.

[0062] In summary, for the chip detection algorithm provided in step S41-step S42, especially a detection algorithm for the photosensitive area of ​​the COMS chip, the spatial domain of the image is converted into the frequency domain and the presence of a defect is determined according to the distribution of the frequency domain characteristics; if there is a defect, the defect edge is determined based on the regional grayscale calculation of the image, and the defect morphology is obtained according to the defect edge. The method provided in this embodiment can solve the problem of high cost of detecting small target defects or sub-pixel level target defects in the prior art, and improves the detection efficiency and reduces the detection cost on the basis of ensuring the detection accuracy.

[0063] In order to better implement the above method, the embodiment of the present application also provides a chip detection device, which can be integrated in an electronic device, and the electronic device can be a terminal, a server, etc. Among them, the terminal can be a mobile phone, a tablet computer, a smart Bluetooth device, a laptop, a personal computer, etc.; the server can be a single server or a server cluster composed of multiple servers.

[0064] For example, in this embodiment, the method of the embodiment of the present application will be described in detail by taking the chip detection device specifically integrated in an electronic device as an example.

[0065] For example, Figure 5 As shown, the chip detection device 50 may include an initial detection unit 51 and an image detection unit 52, wherein:

[0066] The initial detection unit 51 is used to convert the pixel information of the target area image of the chip to be detected into frequency domain information, and determine whether the target area has defects based on the abnormal distribution of the frequency domain information.

[0067] In some specific embodiments, the abnormal distribution of frequency domain information includes a high-frequency signal exceeding a preset frequency domain threshold. The acquisition of the high-frequency signal first traverses the image through a sliding window configured in the device, and performs Fourier transform on the image to obtain a power spectrum of the target area image.

[0068] In some specific implementations, the power spectrum is an expression of frequency domain information, which records the frequency domain distribution state on the target area image. The power spectrum is then screened according to a preset power threshold, and when the frequency domain features are higher than the power threshold, it indicates that the area has defects.

[0069] The image detection unit 52, when having the high-frequency signal, traverses the grayscale changes of the target area image, and when a grayscale change occurs and the grayscale change is greater than a change threshold, it indicates that the grayscale change position is the edge position of a defect, and marks it.

[0070] In some specific embodiments, when the initial detection unit 51 determines through frequency domain processing that an area has an abnormal high-frequency signal, it means that the target area has an abnormality, and the image is transmitted to the image detection unit for image detection to determine the specific characteristics of the defect.

[0071] In some specific embodiments, before the initial detection unit performs image detection, the unit also needs to enhance the contrast of the target area image, thereby increasing the grayscale contrast of the image and thus improving the unit detection efficiency and detection accuracy.

[0072] Specifically, in the process of enhancing the contrast of the target area image in the image detection unit, the target area image is enhanced twice to obtain a first enhanced image and a second enhanced image, and the first enhanced image and the second enhanced image are pyramid reconstructed to obtain a target enhanced image.

[0073] In some specific embodiments, pyramid reconstruction is performed on the first enhanced image and the second enhanced image by respectively obtaining exposure degree weight maps corresponding to the first enhanced image and the second enhanced image, and obtaining high-frequency information layer maps corresponding to the first enhanced image and the second enhanced image, and splicing the weight map and the layer map to obtain a first spliced ​​map and a second spliced ​​map, respectively, and reconstructing the target enhanced image based on the first spliced ​​map and the second spliced ​​map.

[0074] In some specific implementations, the first enhanced image and the second enhanced image are processed based on a Gaussian pyramid to obtain a first weight map and a second weight map; the first enhanced image and the second enhanced image are processed based on a Laplacian pyramid to obtain a first layered map and a second layered map.

[0075] The embodiment of the present application also provides an electronic device, which can be a terminal, a server, etc. The terminal can be a mobile phone, a tablet computer, a smart Bluetooth device, a laptop, a personal computer, etc. The server can be a single server or a server cluster composed of multiple servers, etc.

[0076] In some embodiments, the chip detection device may also be integrated into multiple electronic devices. For example, the chip detection device may be integrated into multiple servers, and the chip detection method of the present application may be implemented by multiple servers.

[0077] In this embodiment, the electronic device of this embodiment is a server as an example for detailed description, for example, Figure 6 As shown, it shows a schematic diagram of the structure of the server involved in the embodiment of the present application, specifically:

[0078] The server may include one or more processing core processors 601, one or more computer-readable storage media memories 602, a power supply 603, an input module 604, and a communication module 605. Those skilled in the art will appreciate that Figure 6 The server structure shown in the figure does not constitute a limitation on the server, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Among them:

[0079] The processor 601 is the control center of the server, and uses various interfaces and lines to connect various parts of the entire server. It executes various functions of the server and processes data by running or executing software programs and / or modules stored in the memory 602, and calling data stored in the memory 602. In some embodiments, the processor 601 may include one or more processing cores; in some embodiments, the processor 601 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 601.

[0080] The memory 602 can be used to store software programs and modules. The processor 601 executes various functional applications and data processing by running the software programs and modules stored in the memory 602. The memory 602 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the server, etc. In addition, the memory 602 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 602 may also include a memory controller to provide the processor 601 with access to the memory 602.

[0081] The server also includes a power supply 603 for supplying power to various components. In some embodiments, the power supply 603 may be logically connected to the processor 601 through a power management system, so that the power management system can manage charging, discharging, power consumption, and other functions. The power supply 603 may also include one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0082] The server may further include an input module 604, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0083] The server may also include a communication module 605. In some embodiments, the communication module 605 may include a wireless module. The server may perform short-range wireless transmission through the wireless module of the communication module 605, thereby providing wireless broadband Internet access for users. For example, the communication module 405 may be used to help users send and receive emails, browse web pages, and access streaming media.

[0084] Although not shown, the server may also include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 601 in the server will load the executable files corresponding to the processes of one or more application programs into the memory 602 according to the following instructions, and the processor 601 will run the application programs stored in the memory 602, thereby implementing the steps in the methods of the embodiments of the present application.

[0085] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0086] From the above, it can be seen that the problem of high cost of detecting small target defects or sub-pixel level target defects in the prior art can be solved, and the detection efficiency is improved and the detection cost is reduced on the basis of ensuring the detection accuracy.

[0087] A person of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0088] To this end, an embodiment of the present application provides a computer-readable storage medium, in which a plurality of instructions are stored, and the instructions can be loaded by a processor to execute the steps in any chip detection method provided in the embodiment of the present application. For example, the instructions can execute the following steps:

[0089] Converting the pixel information of the target area image of the chip to be inspected into frequency domain information, and determining whether the target area has defects based on the abnormal distribution of the frequency domain information;

[0090] When the high-frequency signal is present, the grayscale changes of the target area image are traversed, and when a grayscale change occurs and the grayscale change is greater than a change threshold, it indicates that the grayscale change position is the edge position of a defect, and is marked.

[0091] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0092] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including a computer program / instruction, the computer program / instruction being stored in a computer-readable storage medium. A processor of an electronic device reads the computer program / instruction from the computer-readable storage medium, and the processor executes the computer program / instruction, so that the electronic device executes the method provided in various optional implementations of the chip detection aspect provided in the above embodiments.

[0093] Since the instructions stored in the storage medium can execute the steps in any chip detection method provided in the embodiments of the present application, the beneficial effects that can be achieved by any chip detection method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0094] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0095] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0096] The above is a detailed introduction to a chip detection method, system, device, electronic device, storage medium and program product provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A chip detection method, characterized in that: The method comprises: Converting the pixel information of the target area image of the chip to be inspected into frequency domain information, and determining whether the target area has defects based on the abnormal distribution of the frequency domain information; the abnormal distribution of the frequency domain information includes a high-frequency signal exceeding a preset frequency domain threshold; When the high-frequency signal is present, the grayscale changes of the target area image are traversed, and when a grayscale change occurs and the grayscale change is greater than a change threshold, it indicates that the grayscale change position is the edge position of the defect and is marked.

2. The chip detection method according to claim 1, characterized in that: Converting the signal type of the pixel information into frequency domain information includes: traversing the target area image through a sliding window, and performing Fourier transform on the target area image to obtain a power spectrum of the target area image; the power spectrum is an expression form of the frequency domain information.

3. The chip detection method according to claim 2, characterized in that: The determining whether the target area has defects based on the abnormal distribution of frequency domain information includes: screening the power spectrum according to a preset power threshold, and when the frequency domain characteristics are higher than the power threshold, it indicates that the target area has defects.

4. The chip detection method according to claim 1, characterized in that: The method further comprises: after the high-frequency signal is present, the contrast of the target area image is enhanced to obtain a target enhanced image, and grayscale changes of the target enhanced image are traversed through a sliding window.

5. The chip detection method according to claim 4, characterized in that: The contrast enhancement of the target area image includes: performing two enhancement processes on the target area image respectively to obtain a first enhanced image and a second enhanced image, and performing pyramid reconstruction on the first enhanced image and the second enhanced image to obtain a target enhanced image.

6. The chip detection method according to claim 5, characterized in that: The first enhanced image and the second enhanced image are subjected to pyramid reconstruction, comprising: respectively obtaining exposure degree weight maps corresponding to the first enhanced image and the second enhanced image, and obtaining high-frequency information layered maps corresponding to the first enhanced image and the second enhanced image, and splicing the weight maps and the layered maps to obtain a first spliced ​​map and a second spliced ​​map respectively, and reconstructing the target enhanced image based on the first spliced ​​map and the second spliced ​​map.

7. The chip detection method according to claim 6, characterized in that: The first enhanced image and the second enhanced image are processed based on the Gaussian pyramid to obtain a first weight map and a second weight map; the first enhanced image and the second enhanced image are processed based on the Laplacian pyramid to obtain a first layered map and a second layered map.

8. A chip detection system, characterized in that: The system includes a dark field light source, a detector assembly and a processing device. The dark field light source is used to project an incident light beam to a target area of ​​a chip to be detected. The detector assembly is used to collect a reflected light beam and image the target area to obtain an image of the target area. The processing device is used to receive the image of the target area and execute the chip detection method described in any one of claims 1 to 7.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the chip detection method according to any one of claims 1 to 7 when executing the computer program.

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the chip detection method according to any one of claims 1 to 7 is implemented.

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