Wafer Defect Detection Methods and Equipment

Abstract

This disclosure provides a wafer defect detection method and apparatus, relating to the field of semiconductor technology. The method includes: acquiring a first defect image detected before the wafer under test passes through a target site and a second defect image detected after passing through the target site; selecting a plurality of target defect points within the same area of ​​the first and second defect images; and determining the number of difference defect points between the first and second defect images based on the target defect points selected in the first and second defect images. This disclosure employs a sampling detection method, eliminating the need to compare all defect points, thus effectively reducing the computational load and detection cost during wafer defect detection.

Description

Technical Field

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

[0002] With the development of semiconductor device manufacturing processes, wafer defect detection has become an indispensable means to improve the yield of semiconductor devices.

[0003] Current wafer defect detection methods typically involve performing surface defect detection on the same wafer before and after a certain process station, and then comparing the locations of wafer defects found in the two detection results to determine the defects caused by that process station, thereby helping engineers to trace the cause of the defects.

[0004] However, since there are many defects that may appear on the same wafer during the inspection process, the amount of computation required to compare the location of wafer defects in two inspection results is very large, which increases the burden on the back-end computing server and results in high inspection costs. Summary of the Invention

[0005] This disclosure provides a wafer defect detection method and device, which can reduce the computational load in the wafer defect detection process and effectively reduce detection costs.

[0006] In a first aspect, embodiments of this disclosure provide a wafer defect detection method, including:

[0007] Acquire a first defect image detected before the wafer under test passes the target site and a second defect image detected after passing the target site;

[0008] Select several target defect points within the same area of ​​the first defect image and the second defect image;

[0009] The number of difference defect points between the first defect image and the second defect image is determined based on the target defect points selected in the first defect image and the second defect image.

[0010] In one feasible implementation, selecting a plurality of target defect points within the same region of the first defect image and the second defect image includes:

[0011] Using the same partitioning method, the first defect image and the second defect image are respectively divided into multiple blocks;

[0012] When the defect points in the first defect image and the second defect image both meet the preset uniform distribution condition or sparse distribution condition, at least one block is selected at the same position in the first defect image and the second defect image respectively, and the defect points in the selected blocks are taken as the target defect points.

[0013] When the defect points in the first defect image and the second defect image both meet the preset non-uniform distribution conditions, a portion of the defect points in each block of the first defect image and the second defect image are extracted as the target defect points according to a preset extraction ratio.

[0014] In one feasible implementation, dividing the first defect image and the second defect image into multiple blocks includes:

[0015] The outer rectangles of the first defect image and the second defect image are divided into multiple blocks.

[0016] In one feasible implementation, after dividing the circumscribed rectangles of the first defect image and the second defect image into multiple blocks, the method further includes:

[0017] Determine the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block;

[0018] The weighting coefficient for each block is determined based on the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block.

[0019] In one feasible implementation, determining the number of difference defect points between the first defect image and the second defect image based on target defect points selected in the first defect image and the second defect image includes:

[0020] When the defect points in the first defect image and the second defect image both satisfy the uniform distribution condition or the sparse distribution condition, a first target defect point is determined to be in the same position in the first block selected in the first defect image and the second block selected in the second defect image.

[0021] The target defect points in the second block other than the first target defect point are identified as the difference defect points between the first block and the second block;

[0022] The number of difference defect points between the first defect image and the second defect image is determined based on the number of difference defect points between the first block and the second block, the weighting coefficient corresponding to the second block, and the number of blocks into which the second defect image is divided.

[0023] In one feasible implementation, determining the number of difference defect points between the first defect image and the second defect image based on the number of difference defect points between the first block and the second block, the weighting coefficient corresponding to the second block, and the number of blocks into which the second defect image is divided includes:

[0024] The number of difference defect points B between the first defect image and the second defect image is calculated as follows:

[0025] B = A * D * (P / L) * (S1 / S2)

[0026] Where A represents the number of difference defect points between the first block and the second block, D represents the weighting coefficient corresponding to the second block, P represents the number of blocks in the second defect image, L is a positive integer and L≤P, L represents the number of the first block or the second block, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

[0027] In one feasible implementation, determining the number of difference defect points between the first defect image and the second defect image based on target defect points selected in the first defect image and the second defect image includes:

[0028] When the defect points in the first defect image and the second defect image both satisfy the preset non-uniform distribution condition, a first target defect point with the same position is determined among the target defect points extracted in each block of the first defect image and the second defect image.

[0029] The target defect points extracted from each block of the second defect image, excluding the first target defect points, are determined as the difference defect points between each block of the first defect image and the corresponding block of the second defect image.

[0030] The number of difference defect points between the first defect image and the second defect image is determined based on the number of difference defect points between each block of the first defect image and the corresponding block of the second defect image, the weighting coefficient corresponding to each block of the second defect image, and the preset extraction ratio.

[0031] In one feasible implementation, determining the number of difference defect points between the first defect image and the second defect image based on the number of difference defect points between each block of the first defect image and the corresponding block of the second defect image, the weighting coefficient corresponding to each block of the second defect image, and the preset extraction ratio includes:

[0032] The number of difference defect points B between the first defect image and the second defect image is calculated as follows:

[0033]

[0034] Among them, Z i D represents the number of difference defect points between the target defect points extracted from the i-th block of the first defect image and the target defect points extracted from the i-th block of the second defect image. i The weighting coefficient corresponding to the i-th block of the second defect image is represented by , P represents the number of blocks in the second defect image, C% represents the preset extraction ratio, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

[0035] Secondly, embodiments of this disclosure provide a wafer defect detection device, comprising:

[0036] The acquisition module is used to acquire a first defect image detected before the wafer under test passes the target site and a second defect image detected after passing the target site;

[0037] The selection module is used to select several target defect points in the same area of ​​the first defect image and the second defect image;

[0038] The processing module is used to determine the number of difference defect points between the first defect image and the second defect image based on the target defect points selected in the first defect image and the second defect image.

[0039] In one feasible implementation, the selection module is specifically used for:

[0040] Using the same partitioning method, the first defect image and the second defect image are respectively divided into multiple blocks;

[0041] When the defect points in the first defect image and the second defect image both meet the preset uniform distribution condition or sparse distribution condition, at least one block is selected at the same position in the first defect image and the second defect image respectively, and the defect points in the selected blocks are taken as the target defect points.

[0042] When the defect points in the first defect image and the second defect image both meet the preset non-uniform distribution conditions, a portion of the defect points in each block of the first defect image and the second defect image are extracted as the target defect points according to a preset extraction ratio.

[0043] In one feasible implementation, the selection module is specifically used for:

[0044] The outer rectangles of the first defect image and the second defect image are divided into multiple blocks.

[0045] In one feasible implementation, the selection module is further configured to:

[0046] Determine the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block;

[0047] The weighting coefficient for each block is determined based on the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block.

[0048] In one feasible implementation, the processing module is specifically used for:

[0049] When the defect points in the first defect image and the second defect image both satisfy the uniform distribution condition or the sparse distribution condition, a first target defect point is determined to be in the same position in the first block selected in the first defect image and the second block selected in the second defect image.

[0050] The target defect points in the second block other than the first target defect point are identified as the difference defect points between the first block and the second block;

[0051] The number of difference defect points between the first defect image and the second defect image is determined based on the number of difference defect points between the first block and the second block, the weighting coefficient corresponding to the second block, and the number of blocks into which the second defect image is divided.

[0052] In one feasible implementation, the processing module is specifically used for:

[0053] The number of difference defect points B between the first defect image and the second defect image is calculated as follows:

[0054] B = A * D * (P / L) * (S1 / S2)

[0055] Where A represents the number of difference defect points between the first block and the second block, D represents the weighting coefficient corresponding to the second block, P represents the number of blocks in the second defect image, L is a positive integer and L≤P, L represents the number of the first block or the second block, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

[0056] In one feasible implementation, the processing module is specifically used for:

[0057] When the defect points in the first defect image and the second defect image both satisfy the preset non-uniform distribution condition, a first target defect point with the same position is determined among the target defect points extracted in each block of the first defect image and the second defect image.

[0058] The target defect points extracted from each block of the second defect image, excluding the first target defect points, are determined as the difference defect points between each block of the first defect image and the corresponding block of the second defect image.

[0059] The number of difference defect points between the first defect image and the second defect image is determined based on the number of difference defect points between each block of the first defect image and the corresponding block of the second defect image, the weighting coefficient corresponding to each block of the second defect image, and the preset extraction ratio.

[0060] In one feasible implementation, the processing module is specifically used for:

[0061] The number of difference defect points B between the first defect image and the second defect image is calculated as follows:

[0062]

[0063] Among them, Z i D represents the number of difference defect points between the target defect points extracted from the i-th block of the first defect image and the target defect points extracted from the i-th block of the second defect image. i The weighting coefficient corresponding to the i-th block of the second defect image is represented by , P represents the number of blocks in the second defect image, C% represents the preset extraction ratio, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

[0064] Thirdly, embodiments of this disclosure provide an electronic device, including: at least one processor and a memory;

[0065] The memory stores computer-executed instructions;

[0066] The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the wafer defect detection method as provided in the first aspect.

[0067] Fourthly, embodiments of this disclosure provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the wafer defect detection method provided in the first aspect.

[0068] The wafer defect detection method and equipment provided in this disclosure can effectively reduce the computational load and detection cost by using a sampling detection method when analyzing wafer defect images, thus eliminating the need to compare all defect points. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the accompanying drawings used in the description of the embodiments of this disclosure or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a schematic flowchart of a wafer defect detection method provided in an embodiment of this disclosure;

[0071] Figure 2 The images of wafer defects obtained by performing surface defect detection on the same wafer before and after a certain process station in this embodiment of the present disclosure are shown.

[0072] Figure 3 This is a schematic flowchart of a wafer defect detection method provided in an embodiment of the present disclosure;

[0073] Figure 4 This is a schematic diagram illustrating how a defect image is divided into multiple blocks according to an embodiment of this disclosure;

[0074] Figure 5 This is a partial illustration of a defect detection method provided in an embodiment of this disclosure. Figure 1 ;

[0075] Figure 6 This is a partial illustration of a defect detection method provided in an embodiment of this disclosure. Figure 2 ;

[0076] Figure 7 This is a schematic diagram of the program modules of a wafer defect detection device provided in an embodiment of this disclosure;

[0077] Figure 8 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, 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 this disclosure, not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure. Furthermore, although the disclosure in this disclosure is based on one or several exemplary examples, it should be understood that each aspect of these disclosures can also constitute a complete implementation method on its own.

[0079] It should be noted that the brief descriptions of terms in this disclosure are only for the convenience of understanding the embodiments described below, and are not intended to limit the embodiments of this disclosure. Unless otherwise stated, these terms should be understood in their ordinary and common meaning.

[0080] The terms "first," "second," etc., used in this disclosure, the specification, claims, and the accompanying drawings are used to distinguish similar or related objects or entities and do not necessarily imply a specific order or sequence, unless otherwise specified. It should be understood that such terms can be used interchangeably where appropriate, for example, in situations where implementation can proceed in an order other than those given in the illustrations or description of embodiments of this disclosure.

[0081] Furthermore, the terms “comprising” and “having”, and any variations thereof, are intended to cover but not exclusively include, for example, a product or device that includes a series of components is not necessarily limited to those that are explicitly listed, but may include other components that are not explicitly listed or that are inherent to such product or device.

[0082] As used in this disclosure, the term "module" means any known or subsequently developed hardware, software, firmware, artificial intelligence, fuzzy logic, or combination of hardware and / or software code capable of performing the functions associated with that element.

[0083] In the semiconductor field, the requirements for wafer surface defect detection are generally high efficiency and accuracy, the ability to capture effective defects, and real-time detection. Common surface inspection technologies can be broadly categorized into two types: contact methods and non-contact methods.

[0084] Among them, contact methods are represented by the needle-touch method; non-contact methods can be further divided into atomic force methods and optical methods. In practical applications, they can be further divided into imaging and non-imaging methods.

[0085] As the name suggests, the stylus method involves contact between a stylus and the material being inspected, and it is one of the earliest surface inspection methods in manufacturing. The shape and contour information of the surface being measured is transmitted to the sensor via the stylus, making the size and shape of the stylus particularly important. According to the principle of the stylus method, the radius of the stylus tip must be close to zero to detect the true contour of the object being inspected. However, the finer the stylus tip, the greater the pressure exerted on the surface being inspected, making the stylus more susceptible to wear and scratches on the surface of the object.

[0086] Currently, commonly used imaging detection methods mainly include automated optical detection, X-ray detection, and electron beam detection. Scanning electron microscopy (SEM) uses an electron beam to scan the sample, causing secondary electron emission. These secondary electrons produce a magnified image of the sample surface, which is a point-by-point magnification with a specific order. The advantage of SEM is its extremely high resolution.

[0087] The combination of X-ray nondestructive testing technology and digital image processing technology can enable high-resolution inspection of internal wiring of devices.

[0088] Automated Optical Inspection (AOI) is an optical-based inspection technology that uses the movement of a precision instrument platform, image acquisition devices, and digital image processing technology to detect defects on the sample surface. Its advantage is that it is fast.

[0089] There are many types of defects on the surface of wafers, which may be caused by the manufacturing process or by defects inherent in the material itself. Different defect detection methods may lead to different classifications of defects. Considering the physical properties of defects and the specificity of subsequent defect detection algorithms, defects can be broadly classified into surface redundancies (particles, contaminants, etc.), crystal defects (slip line defects, stacking faults), scratches, and pattern defects (for patterned wafers).

[0090] This disclosure relates to the detection of surface defects in memory chips using defect inspection equipment during the manufacturing process, and particularly to the comparison of defect locations between two or more inspection results. By using the concept of sampling to reduce computational complexity, it can be applied to a large number of comparative calculations of defect detection results.

[0091] Currently, common wafer defect detection methods generally involve performing surface defect detection on the same wafer before and after a certain process station, and then comparing the locations of wafer defects found in the two detection results to determine the defects caused by that process station. Alternatively, by comparing the detection results before and after multiple process stations, the source process station where defects are obvious can be compared, thereby helping engineers to trace the cause of the defects and helping to improve and solve the current problems at each process station.

[0092] The comparison of defect locations in two wafer inspection results typically involves directly comparing the two results in a real two-dimensional coordinate system. This comparison determines which defect locations are present in both results and which are unique to each. Because real coordinate systems have the property of infinite decimals, a coordinate system precision and an allowable range of difference are generally defined. If defects are present in both results within this range, the defect location is considered to be present in both results; otherwise, it is unique to each result.

[0093] To better understand the embodiments of this disclosure, please refer to... Figure 1 , Figure 1 This is a schematic flowchart of a wafer defect detection method provided in an embodiment of this disclosure.

[0094] In some embodiments, for each process station and for each wafer, surface defect detection is performed on the wafer before it passes through a certain process station to obtain a defect detection image A, and surface defect detection is performed on the wafer after it passes through a certain process station to obtain a defect detection image B; by comparing the differences between defect detection image A and defect detection image B, the difference defect points between defect detection image A and defect detection image B can be obtained, and the obtained difference defect points are saved to the database.

[0095] Reference Figure 2 , Figure 2 These are wafer defect images obtained by performing surface defect detection on the same wafer before and after a certain process station in an embodiment of this disclosure.

[0096] In this context, hollow dots in defect detection image A and defect detection image B represent defects shared by both images; solid dots in defect detection image B represent defects unique to image B, i.e., the difference between defect detection image A and defect detection image B.

[0097] Because the memory cells of memory chips are extremely small per unit area, the allowable range of difference is generally very small. This results in a large amount of computation required to compare two wafer defect detection images in a two-dimensional real coordinate system, which increases the burden on the back-end computing server. When multiplied by the number of sites and the number of wafers, the computing cost of the server becomes very high.

[0098] To address the aforementioned technical problems, this disclosure provides a wafer defect detection method and device. When analyzing wafer defect detection images, a sampling detection method is used, eliminating the need to compare all defect points. This effectively reduces the computational load and detection costs during the wafer defect detection process.

[0099] Reference Figure 3 , Figure 3 This is a schematic flowchart illustrating the steps of a wafer defect detection method provided in an embodiment of this disclosure. In one feasible implementation, the wafer defect detection method includes:

[0100] S301. Obtain the first defect image detected before the wafer under test passes the target site and the second defect image detected after passing the target site.

[0101] In this embodiment of the present disclosure, the testing equipment can perform surface defect detection on the wafer before it passes through the target site to obtain a first defect image; and then perform surface defect detection on the wafer again after it passes through the target site to obtain a second defect image.

[0102] During defect detection, a first defect image is obtained by performing surface defect detection on the wafer before it passes the target site, and a second defect image is obtained by performing surface defect detection on the wafer after it passes the target site.

[0103] S302. Select several target defect points within the same area of ​​the first defect image and the second defect image.

[0104] In this embodiment of the disclosure, a random sampling principle can be used to select several target defect points in the same area of ​​the first defect image and the second defect image.

[0105] Since the defects on a wafer may be uniformly distributed, non-uniformly distributed, or sparsely distributed, in order to ensure that the selected target defects are representative and improve the accuracy of the detection results, in some embodiments, after obtaining the first defect image and the second defect image, the distribution of the defects in the first defect image and the second defect image is determined respectively.

[0106] When the defect points in the first defect image and the second defect image meet the preset uniform distribution condition or sparse distribution condition, the first defect image and the second defect image are divided into multiple blocks respectively. Then, several blocks are randomly selected at the same position in the first defect image and the second defect image respectively, and all defect points in the selected blocks are taken as the target defect points.

[0107] When the defect points in the first defect image and the second defect image both meet the preset non-uniform distribution conditions, the first defect image and the second defect image are also divided into multiple blocks, and then a portion of the defect points in each block of the first defect image and the second defect image are randomly selected as the target defect points according to the preset extraction ratio.

[0108] When the first defect image and the second defect image are different and satisfy the above-mentioned uniform distribution condition, sparse distribution condition, or non-uniform distribution condition (such as the first defect image satisfying the uniform distribution condition while the second defect image satisfies the non-uniform distribution condition, or the first defect image satisfying the sparse distribution condition while the second defect image satisfies the non-uniform distribution condition), it can be directly determined that there is a difference between the defect points in the first defect image and the second defect image, and it is not necessary to detect the difference defect points.

[0109] S303. Based on the target defect points selected in the first defect image and the second defect image, determine the number of difference defect points between the first defect image and the second defect image.

[0110] In this embodiment of the present disclosure, after selecting target defect points in the first defect image and the second defect image, the number of difference defect points between the first defect image and the second defect image can be determined by comparing the distribution of the selected target defect points in the first defect image and the selected target defect points in the second defect image.

[0111] It is understandable that when the defect points in the first defect image and the second defect image both meet the preset uniform distribution condition or sparse distribution condition, the distribution of difference defect points between several randomly selected blocks in the first defect image and several randomly selected blocks at the same position in the second defect image can represent the overall distribution of difference defect points between the first defect image and the second defect image. Therefore, by comparing the target defect points selected in the first defect image and the second defect image, and then combining the number of divided blocks, the number of difference defect points between the first defect image and the second defect image can be determined.

[0112] When the defect points in both the first and second defect images meet the preset non-uniform distribution conditions, the distribution of the difference between the defect points randomly selected from each block of the first defect image according to a preset sampling ratio and the defect points randomly selected from each block of the second defect image according to a preset sampling ratio can represent the overall distribution of the difference between the first and second defect images. Therefore, by comparing the target defect points selected in the first and second defect images and combining them with the above sampling ratio, the number of difference between the first and second defect images can still be determined.

[0113] The wafer defect detection method provided in this embodiment of the present disclosure adopts a sampling detection method when analyzing wafer defect images, which eliminates the need to compare all defect points. Therefore, it can effectively reduce the amount of computation in the wafer defect detection process and reduce detection costs.

[0114] Based on the content described in the above embodiments, in a feasible implementation, when selecting several target defect points in the same area of ​​the first defect image and the second defect image, the same division method can be used to divide the first defect image and the second defect image into multiple blocks respectively.

[0115] For example, the circumscribed rectangles of the first defect image and the second defect image can be divided into multiple blocks respectively.

[0116] To better understand the embodiments of this disclosure, please refer to... Figure 4 , Figure 4 This is a schematic diagram illustrating how a defect image is divided into multiple blocks, as provided in an embodiment of this disclosure.

[0117] exist Figure 4 In the image, the outer rectangle of the defect image is divided into 25 equal blocks.

[0118] In some embodiments, after dividing the circumscribed rectangles of the first defect image and the second defect image into multiple blocks, the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block is determined; and the weighting coefficient corresponding to each block is determined based on the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block.

[0119] Taking the aforementioned first defect image as an example, assume that the circumscribed rectangle of the aforementioned first defect image is equally divided into P( Figure 4 If there are P=25 blocks of the same size, and the area of ​​each block is S, and the area of ​​the first defect image contained in the first block is A1, then the weighting coefficient D corresponding to the first block can be determined as: D=S / A1.

[0120] Using the above calculation method, the weighting coefficient corresponding to each block can be calculated.

[0121] It is understandable that the above Figure 4 Among the multiple blocks shown, the weighting coefficients for blocks 7, 8, 9, 12, 13, 14, 17, 18, and 19 are all 1, while the weighting coefficients for the remaining blocks are all greater than 1.

[0122] The wafer defect detection method provided in this embodiment uses the same division method when analyzing wafer defect images. The first defect image and the second defect image are divided into multiple blocks respectively. The weighting coefficient corresponding to each block is determined according to the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block. This can eliminate the error caused by the block where the wafer edge is located not completely covering the defect image.

[0123] It is understood that in some embodiments, other division methods may be used to divide the first defect image and the second defect image into multiple blocks respectively, and no limitation is made in the embodiments of this application.

[0124] In some embodiments, when the defect points in both the first defect image and the second defect image satisfy a preset uniform distribution condition or sparse distribution condition, the difference defect point detection is performed in the following manner:

[0125] Step 1: Select at least one block at the same position in the first defect image and the second defect image, and take the defect point in the selected block as the target defect point.

[0126] Step 2: Determine the first target defect point that is located in the first block selected in the first defect image and the second block selected in the second defect image.

[0127] Step 3: Identify the target defect points in the second block other than the first target defect point as the difference defect points between the first block and the second block.

[0128] Step 4: Determine the number of difference defect points between the first defect image and the second defect image based on the number of difference defect points between the first block and the second block, the weighting coefficient corresponding to the second block, and the number of blocks divided in the second defect image.

[0129] Optionally, the number B of difference defect points between the first defect image and the second defect image can be calculated as follows:

[0130] B = A * D * (P / L) * (S1 / S2)

[0131] Where A represents the number of difference defect points between the first block and the second block, D represents the weighting coefficient corresponding to the second block, P represents the number of blocks in the second defect image, L is a positive integer and L≤P, L represents the number of the first block or the second block, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

[0132] To better understand the embodiments of this disclosure, please refer to... Figure 5 , Figure 5 This is a partial illustration of a defect detection method provided in an embodiment of this disclosure. Figure 1 .

[0133] exist Figure 5 In this example, it is assumed that defect points in block 7 of the first defect image and the second defect image are selected as target defect points. Block 7 of the first defect image has 10 defect points, and block 7 of the second defect image has 12 defect points. After comparison, there are 5 first target defect points in the same position between block 7 of the first defect image and block 7 of the second defect image.

[0134] Calculations show that there are 12 difference defect points between block 7 of the first defect image and block 7 of the second defect image.

[0135] Since both the first and second defect images are divided into P = 25 blocks, and the weighting coefficient of the second block is 1, the number of difference defect points B between the first and second defect images can be calculated as follows:

[0136] B = 12 * 1 * 25 * (S1 / S2) = 300 * (S1 / S2) items

[0137] In some embodiments, defect points in multiple blocks can be selected as target defect points in the first defect image and the second defect image, respectively. For example, defect points in blocks 7 and 13 can be selected as target defect points in the first defect image and in the second defect image, where L = 2.

[0138] The wafer defect detection method provided in this embodiment of the present disclosure, when the defect points in the first defect image and the second defect image both meet the preset uniform distribution condition or sparse distribution condition, selects at least one block at the same position in the first defect image and the second defect image respectively, and uses the defect points in the selected block as target defect points; by using the defect points in the selected block as comparison samples, the number of difference defect points between the first defect image and the second defect image can be determined, which can effectively reduce the amount of computation in the wafer defect detection process and reduce the detection cost.

[0139] In some embodiments, when the defect points in both the first defect image and the second defect image satisfy a preset non-uniform distribution condition, the difference defect point detection is performed in the following manner:

[0140] Step 1: Extract a portion of the defect points from each block of the first and second defect images according to a preset extraction ratio as target defect points.

[0141] Step 2: Determine the first target defect point that is in the same position among the target defect points extracted from each block of the first defect image and the second defect image.

[0142] Step 3: Determine the target defect points other than the first target defect points extracted from each block of the second defect image as the difference defect points between each block of the first defect image and the corresponding block of the second defect image.

[0143] Step 4: Determine the number of difference defect points between the first defect image and the second defect image based on the number of difference defect points between each block of the first defect image and the corresponding block of the second defect image, the weighting coefficient corresponding to each block of the second defect image, and the preset extraction ratio.

[0144] Optionally, the number B of difference defect points between the first defect image and the second defect image can be calculated in the following manner:

[0145]

[0146] Among them, Z i D represents the number of difference defect points between the target defect points extracted from the i-th block of the first defect image and the target defect points extracted from the i-th block of the second defect image. i The weighting coefficient corresponding to the i-th block of the second defect image is represented by , the number of blocks in the second defect image is represented by , C% represents the preset extraction ratio, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

[0147] Understandable This represents the number of difference defect points between the i-th block of the first defect image and the i-th block of the second defect image.

[0148] The preset extraction ratio C% can be customized by the tester.

[0149] Among them, D i =S / A i Where S represents the area of ​​each block in the second defect image described above, and A i This represents the area of ​​the second defect image contained within the i-th block.

[0150] To better understand the embodiments of this disclosure, please refer to... Figure 6 , Figure 6 This is a partial illustration of a defect detection method provided in an embodiment of this disclosure. Figure 2 .

[0151] exist Figure 6 For example, assuming that block 8 of the first defect image has 10 defect points and block 8 of the second defect image has 15 defect points, and the extraction ratio is 40%, then 4 defect points are randomly extracted from block 8 of the first defect image and 6 defect points are randomly extracted from block 8 of the second defect image. Comparing the positions of the 4 randomly extracted defect points from block 8 of the first defect image and the 6 randomly extracted defect points from block 8 of the second defect image, it can be concluded that there are 2 first target defect points with the same position, and 6 difference defect points with different positions. Therefore, the number B8 of difference defect points between block 8 of the first defect image and block 8 of the second defect image is:

[0152]

[0153] Following the above method, each block Z of the first defect image can be calculated. i Block Z corresponding to the second defect image i The number of difference defect points between the first and second defect images is calculated, and then the number of difference defect points between the first and second defect images is determined.

[0154] The wafer defect detection method provided in this embodiment of the present disclosure, when the defect points in the first defect image and the second defect image both meet the preset non-uniform distribution conditions, extracts a portion of the defect points as target defect points in each block of the first defect image and the second defect image according to a preset extraction ratio, and then determines the number of difference defect points between the first defect image and the second defect image by using the selected target defect points as comparison samples. This can effectively reduce the amount of computation in the wafer defect detection process and reduce the detection cost.

[0155] Based on the description in the above embodiments, this disclosure also provides a wafer defect detection device. (Refer to...) Figure 7 , Figure 7 This is a schematic diagram of the program modules of a wafer defect detection device provided in an embodiment of the present disclosure. The wafer defect detection device 70 includes:

[0156] The acquisition module 701 is used to acquire a first defect image detected before the wafer under test passes the target site and a second defect image detected after passing the target site.

[0157] The selection module 702 is used to select a plurality of target defect points in the same area of ​​the first defect image and the second defect image.

[0158] The processing module 703 is used to determine the number of difference defect points between the first defect image and the second defect image based on the target defect points selected in the first defect image and the second defect image.

[0159] The wafer defect detection device provided in this embodiment of the present disclosure uses a sampling detection method when analyzing wafer defect images, which eliminates the need to compare all defect points. Therefore, it can effectively reduce the amount of computation in the wafer defect detection process and reduce detection costs.

[0160] In one feasible implementation, the selection module 702 is specifically used for:

[0161] Using the same partitioning method, the first defect image and the second defect image are respectively divided into multiple blocks;

[0162] When the defect points in the first defect image and the second defect image both meet the preset uniform distribution condition or sparse distribution condition, at least one block is selected at the same position in the first defect image and the second defect image respectively, and the defect points in the selected blocks are taken as the target defect points.

[0163] When the defect points in the first defect image and the second defect image both meet the preset non-uniform distribution conditions, a portion of the defect points in each block of the first defect image and the second defect image are extracted as the target defect points according to a preset extraction ratio.

[0164] In one feasible implementation, the selection module 702 is specifically used for:

[0165] The outer rectangles of the first defect image and the second defect image are divided into multiple blocks.

[0166] In one feasible implementation, the selection module 702 is further configured to:

[0167] Determine the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block;

[0168] The weighting coefficient for each block is determined based on the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block.

[0169] In one feasible implementation, the processing module 703 is specifically used for:

[0170] When the defect points in the first defect image and the second defect image both satisfy the uniform distribution condition or the sparse distribution condition, a first target defect point is determined to be in the same position in the first block selected in the first defect image and the second block selected in the second defect image.

[0171] The target defect points in the second block other than the first target defect point are identified as the difference defect points between the first block and the second block;

[0172] The number of difference defect points between the first defect image and the second defect image is determined based on the number of difference defect points between the first block and the second block, the weighting coefficient corresponding to the second block, and the number of blocks into which the second defect image is divided.

[0173] In one feasible implementation, the processing module 703 is specifically used for:

[0174] The number of difference defect points B between the first defect image and the second defect image is calculated as follows:

[0175] B = A * D * (P / L) * (S1 / S2)

[0176] Where A represents the number of difference defect points between the first block and the second block, D represents the weighting coefficient corresponding to the second block, P represents the number of blocks in the second defect image, L is a positive integer and L≤P, L represents the number of the first block or the second block, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

[0177] In one feasible implementation, the processing module 703 is specifically used for:

[0178] When the defect points in the first defect image and the second defect image both satisfy the preset non-uniform distribution condition, a first target defect point with the same position is determined among the target defect points extracted in each block of the first defect image and the second defect image.

[0179] The target defect points extracted from each block of the second defect image, excluding the first target defect points, are determined as the difference defect points between each block of the first defect image and the corresponding block of the second defect image.

[0180] The number of difference defect points between the first defect image and the second defect image is determined based on the number of difference defect points between each block of the first defect image and the corresponding block of the second defect image, the weighting coefficient corresponding to each block of the second defect image, and the preset extraction ratio.

[0181] In one feasible implementation, the processing module 703 is specifically used for:

[0182] The number of difference defect points B between the first defect image and the second defect image is calculated as follows:

[0183]

[0184] Among them, Z i D represents the number of difference defect points between the target defect points extracted from the i-th block of the first defect image and the target defect points extracted from the i-th block of the second defect image. i The weighting coefficient corresponding to the i-th block of the second defect image is represented by , P represents the number of blocks in the second defect image, C% represents the preset extraction ratio, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

[0185] It should be noted that the specific execution of the acquisition module 701, selection module 702, and processing module 703 in this embodiment can be found in the [reference needed]. Figures 1 to 6 The relevant content in the illustrated embodiments will not be repeated here.

[0186] Furthermore, based on the content described in the above embodiments, this disclosure also provides an electronic device, which includes at least one processor and a memory; wherein the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory to implement the various steps of the wafer defect detection method as described in the above embodiments, which will not be repeated here.

[0187] To better understand the embodiments of this disclosure, please refer to... Figure 8 , Figure 8 This is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of this disclosure.

[0188] like Figure 8As shown, the electronic device 80 of this embodiment includes: a processor 801 and a memory 802; wherein:

[0189] Memory 802 is used to store instructions executed by the computer;

[0190] The processor 801 is used to execute computer execution instructions stored in the memory to implement the various steps of the wafer defect detection method described in the above embodiments, and for details, please refer to the relevant descriptions in the foregoing method embodiments.

[0191] Alternatively, the memory 802 can be either standalone or integrated with the processor 801.

[0192] When the memory 802 is set up independently, the device also includes a bus 803 for connecting the memory 802 and the processor 801.

[0193] Furthermore, based on the content described in the above embodiments, this disclosure also provides a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the various steps of the wafer defect detection method described in the above embodiments. For details, please refer to the relevant descriptions in the foregoing method embodiments.

[0194] In the several embodiments provided in this disclosure, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0195] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0196] Furthermore, the functional modules in the various embodiments of this disclosure can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit integrating the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0197] The integrated modules implemented as software functional modules described above can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods described in the various embodiments of this disclosure.

[0198] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in this disclosure can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0199] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0200] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0201] The aforementioned storage medium can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium accessible to general-purpose or special-purpose computers.

[0202] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. Both the processor and the storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic device or host device.

[0203] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0204] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure, and are not intended to limit them. Although this disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this disclosure.

Claims

1. A method for detecting wafer defects, characterized in that, include: Acquire a first defect image detected before the wafer under test passes the target site and a second defect image detected after passing the target site; Select several target defect points within the same area of ​​the first defect image and the second defect image; Based on the target defect points selected in the first defect image and the second defect image, determine the number of difference defect points between the first defect image and the second defect image; The step of determining the number of difference defect points between the first defect image and the second defect image based on the target defect points selected in the first defect image and the second defect image includes: When the defect points in the first defect image and the second defect image both satisfy the preset uniform distribution condition or sparse distribution condition, a first target defect point is determined to be in the same position in the first block selected in the first defect image and the second block selected in the second defect image. The target defect points in the second block other than the first target defect point are identified as the difference defect points between the first block and the second block; The number of difference defect points between the first defect image and the second defect image is determined based on the number of difference defect points between the first block and the second block, the weighting coefficient corresponding to the second block, and the number of blocks into which the second defect image is divided.

2. The method according to claim 1, characterized in that, Selecting several target defect points within the same region of the first defect image and the second defect image includes: Using the same partitioning method, the first defect image and the second defect image are respectively divided into multiple blocks; When the defect points in the first defect image and the second defect image both satisfy the uniform distribution condition or the sparse distribution condition, at least one block is selected at the same position in the first defect image and the second defect image respectively, and the defect points in the selected blocks are taken as the target defect points. When the defect points in the first defect image and the second defect image both meet the preset non-uniform distribution conditions, a portion of the defect points in each block of the first defect image and the second defect image are extracted as the target defect points according to a preset extraction ratio.

3. The method according to claim 2, characterized in that, The step of dividing the first defect image and the second defect image into multiple blocks includes: The outer rectangles of the first defect image and the second defect image are divided into multiple blocks.

4. The method according to claim 3, characterized in that, After dividing the circumscribed rectangles of the first defect image and the second defect image into multiple blocks, the method further includes: Determine the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block; The weighting coefficient for each block is determined based on the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block.

5. The method according to claim 1, characterized in that, Determining the number of difference defect points between the first defect image and the second defect image based on the number of difference defect points between the first block and the second block, the weighting coefficient corresponding to the second block, and the number of blocks divided in the second defect image includes: The number of difference defect points B between the first defect image and the second defect image is calculated as follows: B = A * D * (P / L) * (S1 / S2) Where A represents the number of difference defect points between the first block and the second block, D represents the weighting coefficient corresponding to the second block, P represents the number of blocks in the second defect image, L is a positive integer and L≤P, L represents the number of the first block or the second block, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

6. The method according to claim 4, characterized in that, The step of determining the number of difference defect points between the first defect image and the second defect image based on the target defect points selected in the first defect image and the second defect image includes: When the defect points in the first defect image and the second defect image both satisfy the preset non-uniform distribution condition, a first target defect point with the same position is determined among the target defect points extracted in each block of the first defect image and the second defect image. The target defect points extracted from each block of the second defect image, excluding the first target defect points, are determined as the difference defect points between each block of the first defect image and the corresponding block of the second defect image. The number of difference defect points between the first defect image and the second defect image is determined based on the number of difference defect points between each block of the first defect image and the corresponding block of the second defect image, the weighting coefficient corresponding to each block of the second defect image, and the preset extraction ratio.

7. The method according to claim 6, characterized in that, The step of determining the number of difference defect points between the first defect image and the second defect image based on the number of difference defect points between each block of the first defect image and the corresponding block of the second defect image, the weighting coefficient corresponding to each block of the second defect image, and the preset extraction ratio includes: The number of difference defect points B between the first defect image and the second defect image is calculated as follows: Among them, Z i D represents the number of difference defect points between the target defect points extracted from the i-th block of the first defect image and the target defect points extracted from the i-th block of the second defect image. i The weighting coefficient corresponding to the i-th block of the second defect image is represented by , P represents the number of blocks in the second defect image, C% represents the preset extraction ratio, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

8. A wafer defect detection device, characterized in that, include: The acquisition module is used to acquire a first defect image detected before the wafer under test passes the target site and a second defect image detected after passing the target site; The selection module is used to select several target defect points in the same area of ​​the first defect image and the second defect image; The processing module is used to determine the number of difference defect points between the first defect image and the second defect image based on the target defect points selected in the first defect image and the second defect image; The processing module is specifically used for: When the defect points in the first defect image and the second defect image both satisfy the preset uniform distribution condition or sparse distribution condition, a first target defect point is determined to be in the same position in the first block selected in the first defect image and the second block selected in the second defect image. The target defect points in the second block other than the first target defect point are identified as the difference defect points between the first block and the second block; The number of difference defect points between the first defect image and the second defect image is determined based on the number of difference defect points between the first block and the second block, the weighting coefficient corresponding to the second block, and the number of blocks into which the second defect image is divided.

9. The apparatus according to claim 8, characterized in that, The selection module is specifically used for: Using the same partitioning method, the first defect image and the second defect image are respectively divided into multiple blocks; When the defect points in the first defect image and the second defect image both satisfy the uniform distribution condition or the sparse distribution condition, at least one block is selected at the same position in the first defect image and the second defect image respectively, and the defect points in the selected blocks are taken as the target defect points. When the defect points in the first defect image and the second defect image both meet the preset non-uniform distribution conditions, a portion of the defect points in each block of the first defect image and the second defect image are extracted as the target defect points according to a preset extraction ratio.

10. The apparatus according to claim 9, characterized in that, The selection module is specifically used for: The outer rectangles of the first defect image and the second defect image are divided into multiple blocks.

11. The apparatus according to claim 10, characterized in that, The selection module is further specifically used for: Determine the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block; The weighting coefficient for each block is determined based on the proportion of the area of ​​the first defect image or the second defect image contained in each block to the total area of ​​each block.

12. The apparatus according to claim 8, characterized in that, The processing module is specifically used for: The number of difference defect points B between the first defect image and the second defect image is calculated as follows: B = A * D * (P / L) * (S1 / S2) Where A represents the number of difference defect points between the first block and the second block, D represents the weighting coefficient corresponding to the second block, P represents the number of blocks in the second defect image, L is a positive integer and L≤P, L represents the number of the first block or the second block, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

13. The apparatus according to claim 11, characterized in that, The processing module is specifically used for: When the defect points in the first defect image and the second defect image both satisfy the preset non-uniform distribution condition, a first target defect point with the same position is determined among the target defect points extracted in each block of the first defect image and the second defect image. The target defect points extracted from each block of the second defect image, excluding the first target defect points, are determined as the difference defect points between each block of the first defect image and the corresponding block of the second defect image. The number of difference defect points between the first defect image and the second defect image is determined based on the number of difference defect points between each block of the first defect image and the corresponding block of the second defect image, the weighting coefficient corresponding to each block of the second defect image, and the preset extraction ratio.

14. The apparatus according to claim 13, characterized in that, The processing module is specifically used for: The number of difference defect points B between the first defect image and the second defect image is calculated as follows: Among them, Z i D represents the number of difference defect points between the target defect points extracted from the i-th block of the first defect image and the target defect points extracted from the i-th block of the second defect image. i The weighting coefficient corresponding to the i-th block of the second defect image is represented by , P represents the number of blocks in the second defect image, C% represents the preset extraction ratio, S1 represents the area of ​​the second defect image, and S2 represents the area of ​​the circumscribed rectangle of the second defect image.

15. An electronic device, characterized in that, include: At least one processor and memory; The memory stores computer-executed instructions; The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the wafer defect detection method as described in any one of claims 1 to 7.

16. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the wafer defect detection method as described in any one of claims 1 to 7.

Images