Method, product, device, system and medium for detecting edge marker position

Abstract

The application provides a detection method for an identification position of an edge of a to-be-detected object, a computer program product, a detection device, a detection system and a computer readable storage medium. The method comprises: acquiring a to-be-detected image comprising an edge part of a target object; performing sliding detection on the to-be-detected image through a sample image; determining a matching relationship according to a matching value of the sample image at each detection position and the to-be-detected image; determining a candidate part where the identification position is located in the to-be-detected image according to a target matching value meeting a set requirement in the matching relationship; verifying whether the candidate part contains the identification position; and determining the position of the identification position in the edge part of the target object. Through the above method, the difficulty of identification position detection can be reduced, and the detection efficiency and detection accuracy can be improved.

Description

Technical Field

[0001] This application relates to the field of semiconductor testing technology, and in particular to a method for detecting the edge marking position of an object under test, a computer program product, a testing device, a testing system, and a computer-readable storage medium. Background Technology

[0002] Before a wafer is fed into a lithography machine for photolithography, it needs to be pre-aligned, specifically millimeter-level wafer center alignment and nanometer-level wafer notch alignment. Typically, wafer edge detection first identifies the wafer's centroid and notch positions, determining the wafer's position on the silicon wafer stage. Then, an actuator completes the wafer alignment operation. Specifically, wafer edge detection involves detecting the wafer's notch. However, wafer notch conditions are complex, including inconsistencies, the absence of physical notches, and invalid circuitry on the notches, making wafer notch detection challenging. Summary of the Invention

[0003] In view of this, this application provides a method, computer program product, detection equipment, detection system, and computer-readable storage medium for detecting the edge marking position of an object, which reduces the difficulty of marking position detection and improves detection efficiency and accuracy.

[0004] To achieve the above objectives, the technical solution of this application embodiment is implemented as follows:

[0005] In a first aspect, embodiments of this application provide a method for detecting the position of an edge marker on an object to be tested, including:

[0006] Acquire the image of the target object, including its edge regions;

[0007] Sliding detection is performed on the image to be tested using a sample image. The matching relationship is determined based on the matching value between the sample image and the image to be tested at each detection position. The sample image is a standard image that includes the edge parts of the target object, and the edge parts of the target object have marked positions.

[0008] Based on the target matching value that meets the set requirements in the matching relationship, the candidate part where the marked position is located in the image to be tested is determined;

[0009] The candidate part is verified to determine whether it contains the marker position, and the position of the marker position is determined at the edge of the target object.

[0010] Secondly, embodiments of this application provide a computer program product, wherein when the instructions in the computer program product are executed by the processor of a detection device, the detection device performs a detection method for the edge marker position of the object to be tested as described in any embodiment of this application.

[0011] Thirdly, embodiments of this application provide a detection device, including a processor and a memory. The memory stores a computer program that can be executed by the processor. When the computer program is executed by the processor, it implements the detection method for the edge marker position of the object to be tested as described in any embodiment of this application.

[0012] Fourthly, embodiments of this application provide a detection system, characterized in that it includes an image acquisition device and a detection device as described in any embodiment of this application.

[0013] The image acquisition device is used to rotate and scan around the edge of the target object, sequentially acquiring multiple test images of the edge portion of the target object, and sending the multiple test images to the detection device respectively. After acquiring each test image, the detection device performs the detection method for the edge marker position of the target object as described in any embodiment of this application; or,

[0014] The image acquisition device is used to rotate and scan around the edge of the target object, sequentially acquire test images of the edge parts of the target object, and send the test images to the detection device. After acquiring the test images, the detection device performs the detection method for the edge marker position of the test object as described in any embodiment of this application.

[0015] Fifthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a controller, implements the method for detecting the edge marker position of an object under test as described in any embodiment of this application.

[0016] The embodiments of this application provide a method, computer program product, detection equipment, detection system, and computer-readable storage medium for detecting the edge marker position of an object under test. By acquiring a test image including the edge portion of the target object, sliding detection is performed on the test image based on a sample image to obtain a matching value between the sample image and the test image at each detection position. A matching relationship is determined based on the matching value. Then, based on the target matching value that meets set requirements in the matching relationship, candidate locations of marker positions in the test image are determined. Finally, by verifying whether the candidate locations contain edges, the position of the marker position on the edge portion of the target object is determined. Thus, by designing a sample image to perform template matching on the test image, obtaining a matching relationship, and filtering out target matching values ​​from the matching relationship, candidate locations of marker positions in the test image can be quickly and accurately determined, improving the efficiency and accuracy of candidate location detection. Furthermore, by verifying the candidate locations, the position of the marker position on the edge portion of the target object is determined based on the verification results, thereby accurately detecting the marker position, improving the accuracy of marker location detection, reducing the difficulty of marker location detection, shortening the marker location detection time, and improving the efficiency of marker location detection. Attached Figure Description

[0017] Figure 1 This diagram illustrates the architecture of a detection system provided in an embodiment of this application.

[0018] Figure 2 This diagram illustrates a flowchart of a method for detecting the position of an edge marker of an object to be tested, according to an embodiment of this application.

[0019] Figure 3 This diagram illustrates an optional sliding detection method used in an embodiment of this application.

[0020] Figure 4 This diagram illustrates an image to be tested according to an embodiment of this application.

[0021] Figure 5 This diagram illustrates another optional sliding detection method used in an embodiment of this application.

[0022] Figure 6 This diagram illustrates a method for generating a mask image according to an embodiment of this application.

[0023] Figure 7 This diagram illustrates an image to be examined according to an embodiment of this application.

[0024] Figure 8 This diagram illustrates another image to be tested provided in an embodiment of this application;

[0025] Figure 9This diagram illustrates a flowchart of another method for detecting the edge marker position of an object to be tested, provided in an embodiment of this application.

[0026] Figure 10 This diagram illustrates the structure of a detection device provided in an embodiment of this application. Detailed Implementation

[0027] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to limit the ways in which this application may be implemented. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0029] In the description of this application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this application. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0030] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "joining" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0031] A wafer is a circular silicon chip used to fabricate silicon semiconductor integrated circuits. Various circuit element structures can be fabricated on silicon wafers to create integrated circuit products with specific electrical functions.

[0032] Currently, wafer notches are used to mark wafers and thus determine their orientation. Wafer notches come in two shapes: flat and V-shaped. Through research on a large number of wafer notches, the inventors discovered that the shape characteristics of wafer notches are relatively stable, exhibiting a semi-circular spatial structure. Based on this, embodiments of this application use specific wafer notch templates to match the image to be tested and verify candidate locations based on the shape characteristics of the notches. This allows for accurate detection of notches at the wafer edges, reducing the difficulty of wafer notch detection.

[0033] One aspect of this application provides a detection system. Figure 1 This diagram illustrates the architecture of a detection system provided in an embodiment of this application. Figure 1 As shown, the detection system may include an image acquisition device 11 and a detection device 12. The image acquisition device 11 may include an optical imaging sensor, such as an industrial camera. The detection device 12 may be various intelligent devices with storage and computing capabilities, such as computer equipment. The image acquisition device 11 may be a device with optical imaging capabilities, such as an industrial camera. In this embodiment, the detection device 12 may be integrated with the image acquisition device 11 or set up independently. The detection device 12 and the image acquisition device 11 can communicate with each other. For example, the detection device 12 may send an image acquisition command to the image acquisition device 11, and after receiving the image acquisition command, the image acquisition device 11 acquires an image and sends the acquired image to the detection device.

[0034] In some embodiments, the detection system can acquire an image of a segment of the object's edge, immediately detect that image, and then continue acquiring and detecting another segment of the object's edge until the last segment of the object's edge has been detected. By executing the acquisition and detection steps in parallel, the detection time for identifying the marked locations of the object's edges can be saved, thus improving detection efficiency.

[0035] Specifically, the image acquisition device 11 is used to rotate and scan around the edge of the target object, sequentially acquiring multiple test images of the edge portion of the target object, and sending the multiple test images to the detection device respectively. After acquiring each test image, the detection device 12 performs the detection method for the edge marker position of the test object as described in any embodiment of this application on the test image.

[0036] In other words, the image acquisition device 12 can rotate and scan around the edge of the target object to acquire a test image of the edge part of the target object. This test image is then sent to the detection device 12. After acquiring the test image, the detection device 12 performs the detection method for the edge marker position of the target object as described in any embodiment of this application on the test image. It repeats the acquisition of another test image of the edge part of the target object and the detection steps until the last test image of the edge part of the target object is detected.

[0037] In some embodiments, the detection system can acquire an image of the entire edge region before performing detection on that image. By executing the acquisition and detection steps sequentially, the marked positions of the edge regions of the object under test can be accurately detected, improving detection accuracy.

[0038] Specifically, the image acquisition device 11 is used to rotate and scan around the edge of the target object, sequentially acquiring images of the edge portion of the target object to be tested, and sending the images to be tested to the detection device. After acquiring the images to be tested, the detection device 12 performs the detection method for the edge marker position of the object to be tested as described in any embodiment of this application.

[0039] In other words, the image acquisition device 12 can rotate and scan around the edge of the target object, acquiring one or more test images of the edge portion of the target object, and sending all test images together to the detection device. After acquiring all test images, the detection device 12 performs the detection method for the edge marker position of the test object as described in any embodiment of this application for each test image.

[0040] It should be noted that, in the embodiments of this application, a test image may include a complete edge portion or a segment of an edge portion.

[0041] One aspect of this application provides a method for detecting the position of edge markers on an object to be measured, which can be applied to, for example... Figure 1 The detection device 12 shown. Figure 2 This illustration shows a flowchart of a method for detecting the edge marker position of an object to be tested, as provided in an embodiment of this application. Figure 2 As shown, the method for detecting the edge marker position of the object to be tested may include, but is not limited to, S21, S22, S23, and S24. Details are as follows:

[0042] S21, acquire the image to be tested, including the edge parts of the target object.

[0043] S22, perform sliding detection on the image to be tested using the sample image, and determine the matching relationship based on the matching value between the sample image and the image to be tested at each detection position.

[0044] S23, Based on the target matching value that meets the set requirements in the matching relationship, determine the candidate part where the marked position is located in the image to be tested.

[0045] S24, verify whether the candidate part contains the marker position, and determine the position of the marker position on the edge of the target object.

[0046] In the above embodiments, by acquiring a test image including the edge portion of the target object, sliding detection is performed on the test image based on a sample image to obtain a matching value between the sample image and the test image at each detection position. A matching relationship is determined based on the matching value. Then, based on the target matching value that meets the set requirements in the matching relationship, candidate regions where the marker position is located in the test image are determined. Finally, by verifying whether the candidate region contains an edge, the position of the marker position on the edge portion of the target object is determined. Thus, by using a designed sample image to perform template matching on the test image to obtain a matching relationship, and filtering out target matching values ​​from the matching relationship, candidate regions where the marker position is located in the test image can be quickly and accurately determined, improving the efficiency and accuracy of candidate region detection. Furthermore, by verifying the candidate regions, the position of the marker position on the edge portion of the target object is determined based on the verification results, thereby accurately detecting the marker position, improving the accuracy of marker position detection, reducing the difficulty of marker position detection, shortening the marker position detection time, and improving the efficiency of marker position detection.

[0047] Taking a wafer as the test object as an example, the following is an explanation... Figure 2 The implementation of each step in the embodiment is further explained as follows.

[0048] In step S21, the target object can be a wafer. The image to be tested can be an image including the edge portion of the wafer. The detection device can acquire the image directly from the image acquisition device. Alternatively, the imaging subsystem can transmit the image to a network, and the detection device can acquire it from the network. In this embodiment, the method by which the detection device acquires the image is not limited.

[0049] In step S22, the sample image can be a standard image including the edge regions of the target object. The edge regions of the target object have identified locations. In this embodiment, the sample image can be a wafer sample image, that is, a sample image of various wafer notches. In this embodiment, the sample image can be used as a template for template matching with the image to be tested.

[0050] The "sliding detection" involved in this application embodiment can refer to sliding a window of a preset size in the image to be tested with a preset step size, and performing template matching detection on the corresponding region within the sliding window. Here, the preset size can be the size of the sample image, for example, 128 pixels * 128 pixels. The preset step size can be set arbitrarily. In this application embodiment, sliding detection is used to make the sample image traverse the image to be tested. Furthermore, performing sliding detection on the image to be tested using the sample image can be understood as performing template matching on the image to be tested using the sample image.

[0051] It should be noted that, in the embodiments of this application, the template matching method may include, but is not limited to, similarity matching methods such as standard relevance matching method, relevance matching method, standard difference sum of squares matching method, and difference sum of squares matching method.

[0052] In this application's embodiments, "each detection bit" can refer to each sliding window. The matching value between the sample image and the image to be tested at each detection bit can be understood as the template matching value between the image region corresponding to each sliding window and the sample image. The matching relationship can be a matching result matrix of the sample image traversing the image to be tested, and the matching relationship can include the matching values ​​corresponding to all detection bits.

[0053] Figure 3 This diagram illustrates an optional sliding detection method used in an embodiment of this application. For example... Figure 3 As shown, multiple detection positions are obtained by sliding a sliding window across the image to be tested. Then, template matching is performed on the region of the image to be tested corresponding to each detection position using a sample image to obtain the template matching value corresponding to each detection position, thereby obtaining the template matching relationship between the entire image to be tested and the sample image.

[0054] In step S23, the set requirement can be a requirement that the test image region corresponding to the detection position is similar to the sample image. The target matching value can be a matching value that indicates the similarity between the test image region corresponding to the detection position and the sample image. Here, there can be one or more target matching values.

[0055] In this application's embodiments, the "marker location" can be the wafer's marker location, i.e., the wafer notch. The candidate region where the marker location is located in the image under test can be an image area where the wafer notch might exist in the image under test. Here, there can be one or more candidate regions.

[0056] In this embodiment of the application, target matching values ​​that meet the set requirements are filtered from the matching relationships, and the image region to be tested corresponding to at least one target matching value is determined as the candidate part where the marked position is located in the image to be tested.

[0057] In step S23, the verification of whether the candidate part contains the marker location can be understood as verifying whether the candidate part contains the marker location based on the features of the marker location. Here, the marker location features may include, but are not limited to, shape features, color features, etc.

[0058] In this embodiment, each candidate region is verified to determine whether it contains the wafer marker location. Verified candidate regions are considered as edges of the target object containing the marker location. Based on the positions of the verified candidate regions in the image under test, the location of the marker location within the edge of the target object is determined. Thus, by verifying the marker location, the true image region where the marker location is located can be determined, avoiding misjudgments.

[0059] To reduce the computational load of a single image to be tested, the edge region of the target object can be divided into multiple segments for image acquisition. In some embodiments, S21, acquiring the image to be tested including the edge region of the target object may include:

[0060] Multiple images of the target object, including the edge region, are acquired sequentially by an image acquisition device rotating along the edge of the target object.

[0061] Here, rotating along the edge of the target object can be understood as rotating at least one full revolution around the edge of the target object. Each image to be tested can include a corresponding image of a portion of the edge of the target object; that is, each image to be tested can be an image of a portion of the edge of the target object. Multiple images to be tested can be stitched together to form the complete edge of the target object. In other words, multiple images to be tested can be stitched together to form an image of the edge around the wafer. It should be noted that, in the embodiments of this application, the wafer edge portion in the image to be tested can be approximately vertical.

[0062] In this embodiment, the image acquisition device or detection device divides the edge portion of the target object into multiple segments. The image acquisition device rotates along the edge of the target object and acquires images of each edge segment, thereby obtaining multiple test images containing partial edge regions. The edge regions corresponding to two adjacent test images are adjacent at the edge region of the target object. Figure 4 This diagram illustrates an image to be tested according to an embodiment of this application. For example... Figure 4 As shown, the image acquisition device can rotate along the edge of the target object to acquire up to nine images. One of the images may include a marker position with the opening facing right.

[0063] Here, the detection device can acquire one image to be tested after the image acquisition device acquires one image to be tested, and so on, until all images to be tested are acquired. Alternatively, it can acquire all images to be tested after the image acquisition device has acquired all images to be tested. In this embodiment, the method by which the detection device acquires images to be tested is not limited.

[0064] In the above embodiments, by dividing the edge of the target object into multiple segments for acquisition, multiple test images are obtained. In this way, the local edge of the target object is magnified, which improves the fineness of the edge image, reduces the computational load of a single test image, and further improves the detection efficiency and accuracy of the wafer identification position.

[0065] It should be noted that when there are multiple images to be tested, step S22 is performed for each image to be tested, and then subsequent steps are performed to detect the position of the marker at the edge of the target object.

[0066] Since the marker location is situated at the edge of the target object, and the image to be tested may include not only the image region corresponding to the edge of the target object but also other background regions, in order to reduce sliding detection of unnecessary regions and improve sliding detection efficiency, in some embodiments, S22, sliding detection is performed on the image to be tested using a sample image, and the matching relationship is determined based on the matching value between the sample image and the image to be tested at each detection position. This may include:

[0067] The image to be tested is inspected to determine the contour lines of the edge parts of the target object;

[0068] Sliding detection is performed on the sample image within the image region corresponding to the contour line position;

[0069] The matching relationship is determined based on the matching value between the sample image and the image to be tested at each detection position.

[0070] Here, the contour line position of the target object's edge can be the position of the target object's edge contour line in the image to be tested. The image region corresponding to the contour line position can be understood as an image region that includes the target object and has as few background pixels as possible. In this embodiment, sliding detection is used to make the sample image traverse the image region corresponding to the contour line position.

[0071] Figure 5 This illustration shows another optional sliding detection method used in an embodiment of this application, such as... Figure 5As shown, by detecting the image under test, the contour lines of the target object's edge are identified, thereby determining the position of the target object's edge contour lines in the image under test. A sliding window is then used to slide within the image region corresponding to the contour line position to obtain multiple detection points. Then, template matching is performed on the image region corresponding to each detection point using a sample image to obtain the template matching value for each detection point. Finally, the template matching relationship between the target object portion in the image under test and the sample image is obtained. In this embodiment, the identification of the target object's edge contour can be performed using existing image contour recognition algorithms. Alternatively, since the contrast between the target object and background elements differs significantly, the contour line position of the target object's edge can be extracted using image segmentation methods.

[0072] In this embodiment of the application, by determining the contour line position of the edge part of the target object, background elements are removed from the image to be tested, and sliding detection is performed on the sample image within the image area corresponding to the contour line position. In this way, template matching of the background area can be minimized, the matching time can be shortened, the matching efficiency can be further improved, and the amount of computation in the matching process can be reduced.

[0073] To further improve the accuracy of template matching, in some embodiments, the matching relationship is determined based on the matching value between the sample image and the image to be tested at each detection bit, which may include:

[0074] The image region covered by the sample image at each detection position is determined as the current matching image. The matching value of the sample image at each detection position is determined according to the sum of squared standardized differences between each pixel in the sample image and each pixel in the current matching image.

[0075] A matching relationship is formed based on the matching value corresponding to each detection bit.

[0076] Here, the current matching image can be the image region covered by the sample image at each detection bit in the image to be tested, that is, the image region corresponding to the sliding window in the image to be tested. The matching value can represent the pixel matching degree, which can be used to measure the similarity between the current matching image and the sample image. The matching relation can be the matching result matrix of the sample image traversing the image to be tested. Each element in the matching relation can represent the matching value corresponding to each detection bit.

[0077] In this embodiment, the matching degree between the sample image and the current matching image can be calculated using the standard squared difference matching method. That is, the matching value between the sample image and the current matching image is determined by calculating the sum of the squared standardized differences between each pixel in the sample image and each pixel in the current matching image, thereby determining the matching value of the sample image at each detection position.

[0078] The matching value R(x,y) corresponding to a detection bit (x,y) can be expressed as:

[0079]

[0080] Where T(x',y') represents the grayscale value of pixel (x',y') in the sample image, and I(x'+x,y'+y) represents the grayscale value of pixel I(x'+x,y'+y) in the current matching image, which corresponds to pixel (x',y') in the sample image. It should be noted that the detection bit (x,y) can represent the current matching image with pixel position (x,y) at the top left corner of the image. R represents the matching relationship, where each element represents the matching value corresponding to a detection bit. The larger the value of R(x,y), the less similar the sample image is to the current matching image corresponding to the detection bit (x,y). When R(x,y) is 0, every pixel in the sample image is identical to every pixel in the current matching image corresponding to the detection bit (x,y).

[0081] In the above embodiments, the matching value corresponding to each detection position of the sample image is determined by the sum of the squared standardized differences between each pixel in the sample image and each pixel in the current matching image, which can further improve the accuracy of the matching results.

[0082] To facilitate the extraction of candidate regions where marker locations are located from the image under test, in some embodiments, S23, determining the candidate regions where marker locations are located in the image under test based on target matching values ​​that meet set requirements in the matching relationship includes:

[0083] Determine the target matching value in the matching relationship that is less than a preset value;

[0084] The mask image is determined based on the target matching value, and the image region of the mask image corresponding to the minimum matching value is used as the candidate region where the marked position is located in the image to be tested.

[0085] Here, a value less than a preset value is set as the requirement that the target matching value must meet. The target matching value is the matching value in the matching relationship that is less than the preset value. In this way, the target matching value can be accurately and quickly filtered out from the target matrix. Optionally, the preset value can be a positive number not greater than 0.3.

[0086] In this embodiment, the mask image can be an image in the test image that masks out all image regions except the image region corresponding to the target matching value. The mask image is obtained by using the image region corresponding to the target matching value in the test image as the selected region and masking out other image regions in the test image except for the selected region. It should be noted that one test image can correspond to multiple mask images.

[0087] Furthermore, since a smaller matching value indicates a greater similarity between the image region and the sample image, and the sample image is a standard image of the wafer marker location, the image region of the mask corresponding to the minimum matching value is used as a candidate location for the marker position in the image to be tested.

[0088] In the above embodiments, target matching values ​​are filtered out using preset values. This allows for the selection of candidate regions using a matching threshold, facilitating numerical selection of candidate regions. Furthermore, determining candidate regions from the image under test using a mask image allows for direct extraction of candidate regions, reducing interference from other areas in the image and minimizing interference from other regions that might otherwise identify the region corresponding to the candidate region.

[0089] To further improve the efficiency of candidate region selection, in some embodiments, a mask image is determined based on the target matching value, and the image region of the mask image corresponding to the minimum matching value is used as the candidate region where the identified location is located in the image to be tested, including:

[0090] Multiple target matching values ​​that are consecutive in the matching relationship are treated as the same connected component, and target matching values ​​that are separate in the matching relationship are treated as independent connected components. The corresponding mask map is determined according to the connected components.

[0091] The image region of the mask corresponding to the minimum matching value is used as the candidate region where the identifier is located in the image to be tested.

[0092] Here, multiple consecutive target matching values ​​can form a connected component, and target matching values ​​separated by location can form independent connected components. That is, a matching relationship can include at least one connected component, and a connected component can include multiple target matching values.

[0093] Figure 6 This illustration shows a schematic diagram of a mask generation method provided in an embodiment of this application, such as... Figure 6 As shown, a connected component can correspond to a mask image, and the image region corresponding to the connected component is used as the selected region in the mask image. Here, the minimum matching value is selected from a connected component, and the image region of the mask image corresponding to the minimum matching value is used as the candidate location of the identified position in the image to be tested.

[0094] In the above embodiments, by treating multiple consecutive target matching values ​​as a connected component, the number of mask images generated can be reduced, the candidate part selection efficiency can be further improved, the identification location detection efficiency can be further improved, and the identification location can be made to be within the image area corresponding to the mask image as much as possible.

[0095] To improve the accuracy of verifying candidate parts, in some embodiments, S24, the verification of whether the candidate part contains a marker position is performed, and the position of the marker position on the edge of the target object is determined, including:

[0096] The image region corresponding to the candidate part is segmented from the image to be tested to obtain the image to be tested;

[0097] The location of the reference pixel with the maximum gray-level gradient in each row of the image to be tested is detected;

[0098] The number of reference pixels that satisfy the preset relationship is determined by comparing the coordinate values ​​of each reference pixel in the image to be tested with the coordinate values ​​of the corresponding comparison pixels.

[0099] The location of the marker on the edge of the target object is determined based on the quantity.

[0100] Here, the image to be tested can be the image region corresponding to the candidate part in the image to be tested. The gray-level gradient can measure the rate of change of the image's gray level. The reference pixels for each row can be the pixels corresponding to the maximum gray-level gradient in each row of the image to be tested. The comparison pixels can be reference pixels that are separated from the reference pixels in each row by at least one row or one column.

[0101] It should be noted that in the embodiments of this application, the horizontal direction to the right is the column direction, and the vertical direction to the bottom is the row direction. That is, the horizontal direction to the right is the column coordinate axis (i.e., the x-axis), and the vertical direction to the bottom is the row coordinate axis (i.e., the y-axis).

[0102] In this embodiment, the preset relationship can be a preset relationship representing that the pixel is located in a semi-circular shape. Optionally, the preset relationship can be that the coordinate value of the reference pixel is greater than or less than the coordinate value of the corresponding comparison pixel. Specifically, the preset relationship can be that, in the row direction or column direction, the coordinate value of the reference pixel is greater than the coordinate value of the corresponding comparison pixel by a preset value, or the coordinate value of the reference pixel is less than the coordinate value of the corresponding comparison pixel by a preset value. The preset value can be set as needed. For example, the column coordinate value of the reference pixel is 2 greater than the column coordinate value of the corresponding comparison pixel. It should be noted that the preset relationship can be related to the opening direction of the marked position in the sample image.

[0103] In this embodiment, reference pixels that satisfy a preset relationship can be considered as pixels that may be located in a semi-circular shape. When a certain number of reference pixels satisfy the preset relationship are reached, it can be determined that these reference pixels are located at the edge and form a semi-circular shape. In this way, the candidate part can be verified to include the identification position.

[0104] In this embodiment, the image to be tested is segmented from the image to be tested. For the image to be tested corresponding to each candidate part, the gray-level gradient of the image to be tested is calculated. The reference pixel corresponding to the maximum gray-level gradient in each row of the image to be tested is found. The coordinate values ​​of each reference pixel are compared with the coordinate values ​​of the corresponding comparison pixel. The number of reference pixels that satisfy the preset relationship is determined. Based on the number, it is verified whether the candidate part contains the marker position. If a candidate part passes the verification, the position of the marker position on the edge of the target object can be determined based on the verified candidate part.

[0105] In the above embodiments, edge pixels can be identified by the maximum grayscale gradient and used as reference pixels. By comparing the coordinate values ​​of each row of reference pixels with the comparison pixels, the number of reference pixels that satisfy a preset relationship is determined. Based on the number, it is verified whether the candidate region contains the marker location. In this way, candidate regions can be verified by simple calculation, and by counting the number of reference pixels that satisfy the preset relationship in each candidate region, the location of the marker location on the edge of the target object can be quickly determined, simplifying the marker location detection step.

[0106] Because the marker position is semi-circular, it is necessary to set preset relationships for different areas. In some embodiments, the number of reference pixels that satisfy the preset relationship is determined based on the coordinate values ​​of each reference pixel in the image to be inspected and the coordinate values ​​of the corresponding comparison pixel. This may include:

[0107] The count value of the reference pixels that satisfy the preset relationship is initialized to 0, where n is a positive integer;

[0108] For the upper half of the image to be tested corresponding to the candidate part, determine in sequence whether the coordinate value of each reference pixel is greater than the coordinate value of the comparison pixel n rows away from it.

[0109] If so, the count value is incremented by 1, and the updated count value is determined as the number of reference pixels that satisfy the preset relationship;

[0110] For the lower half of the image to be tested corresponding to the candidate part, sequentially determine whether the coordinate value of the comparison pixel point n rows away from each reference pixel point is greater than the coordinate value of the reference pixel point.

[0111] If so, the count value is incremented by 1, and the updated count value is determined as the number of reference pixels that satisfy the preset relationship.

[0112] Here, the count value can represent the number of reference pixels that satisfy a preset relationship. In this embodiment, a counter can be used to count the reference pixels that satisfy the preset relationship to obtain the count value of the reference pixels that satisfy the preset relationship. In this embodiment, the count value can be incremented by 1 when a reference pixel satisfies the preset relationship.

[0113] In this embodiment, the sample image is an image of the wafer noctch with the opening facing horizontally to the right. The comparison pixels for each row of reference pixels can be spaced n rows apart from each row of reference pixels. The coordinate values ​​of the reference pixels can be their column coordinate values, and the coordinate values ​​of the comparison pixels can also be their column coordinate values. n can be a positive integer not less than 2 and not greater than half the total number of rows in the image to be tested.

[0114] Figure 7 This illustration shows a schematic diagram of an image to be inspected according to an embodiment of this application, such as... Figure 7 As shown, in the inspection image, the marker position has a semi-circular spatial structure. The column coordinates of pixels in the upper half of the marker position gradually decrease, while the column coordinates of pixels in the lower half of the marker position gradually increase. In this embodiment, the image to be inspected corresponding to the candidate part is divided into an upper half and a lower half, and the reference pixels in each row of the upper and lower half are judged to meet the preset relationship. Specifically, the column coordinate values ​​of each row of reference pixels are compared with the column coordinate values ​​of the comparison pixels n rows apart, according to the row arrangement order. For the upper half, reference pixels with column coordinate values ​​greater than the column coordinate values ​​of the comparison pixels are considered to meet the preset relationship. For the lower half, reference pixels with column coordinate values ​​greater than their own column coordinate values ​​are considered to meet the preset relationship. Finally, the total number of reference pixels in the image to be inspected that meet the preset conditions is counted.

[0115] For example, for each image to be tested, a counter is initialized to 0. For the upper half of the image corresponding to a candidate region, the column coordinate of the reference pixel in row y1 is x1, and the column coordinate of the reference pixel in row y1+2 is x2. x1 and x2 are compared; if x1 is greater than x2, the count is incremented by 1. For the lower half of the image corresponding to a candidate region, the column coordinate of the reference pixel in row y2 is x3, and the column coordinate of the reference pixel in row y2+3 is x4. x3 and x4 are compared; if x4 is greater than x3, the count is incremented by 1.

[0116] In the embodiments of this application, the method of dividing the image to be tested corresponding to the candidate part into an upper half and a lower half may include, but is not limited to, dividing the image to be tested into an upper half and a lower half according to the midline of the image to be tested.

[0117] In the above embodiments, based on the shape features of the marker position, different judgment conditions for reference pixels that satisfy the preset relationship are set for different regions of the image to be inspected, which can further improve the accuracy of judging reference pixels that satisfy the preset relationship.

[0118] It should be noted that, when the opening at the marked position in the sample image is horizontal to the left, for the lower half, the reference pixel whose column coordinate value is greater than the coordinate value of the comparison pixel is used as the reference pixel that satisfies the preset relationship; for the upper half, the reference pixel whose coordinate value of the comparison pixel is greater than its own coordinate value is used as the reference pixel that satisfies the preset relationship.

[0119] When the opening at the marked location in the sample image is vertical, the image to be tested can be divided into a left half and a right half. If the opening at the marked location in the sample image is vertically upward, for the right half, reference pixels whose column coordinates are greater than the row coordinates of the comparison pixel are used as reference pixels that satisfy the preset relationship. For the left half, reference pixels whose row coordinates are greater than their own row coordinates are used as reference pixels that satisfy the preset relationship.

[0120] When the opening at the marked position in the sample image is vertically downward, for the left half, the reference pixel whose column coordinate value is greater than the row coordinate value of the comparison pixel is used as the reference pixel that satisfies the preset relationship. For the right half, the reference pixel whose row coordinate value of the comparison pixel is greater than its own row coordinate value is used as the reference pixel that satisfies the preset relationship.

[0121] In addition, in this embodiment, besides dividing the image to be inspected according to the axis of symmetry, it can also be divided according to the central axis of symmetry. The pixel coordinates can be projected before judgment. The judgment strategy for reference pixels that satisfy the preset relationship is similar to the aforementioned judgment strategy, and will not be repeated here.

[0122] In some embodiments, determining the location of the marker position on the edge of the target object based on the quantity includes:

[0123] The candidate region corresponding to the image to be detected with the largest number of reference pixels that satisfy the preset relationship is determined as the final position;

[0124] Based on the final location, determine the position of the marker on the edge of the target object.

[0125] Here, the final position can be the location of the marker position in the image to be tested. The image to be tested with the largest number of reference pixels that satisfy the preset relationship can be considered as having the highest probability that the reference pixels in the image to be tested form a semi-circle, that is, the candidate part corresponding to the image to be tested has the highest probability of including the marker position.

[0126] In this embodiment of the application, by comparing the number of reference pixels that satisfy the preset relationship in each image to be tested, the candidate part corresponding to the image to be tested with the largest number is determined as the location of the marker position. Thus, by determining the location of the candidate part in the image to be tested, the position of the marker position on the edge of the target object can be obtained.

[0127] In the above embodiments, the candidate part with the largest number is selected as the final location of the marker. In this way, the candidate part with the most obvious shape features can be selected from each candidate part by using the number feature and used as the location of the marker. This makes it easier to determine the location of the marker and improves the accuracy of the marker location determination.

[0128] Figure 8 This diagram illustrates another image to be tested provided in an embodiment of this application. For example... Figure 8 As shown, since the target object's image is acquired in segments, the marker position may be located in two different images. Therefore, it is necessary to stitch adjacent images together and perform marker position detection again. In some embodiments, determining the position of the marker position at the edge of the target object based on the number of markers may include:

[0129] The candidate region corresponding to the image to be detected with the largest number of reference pixels that satisfy the preset relationship is determined as the first confirmation position.

[0130] The steps involve stitching adjacent test images together to form updated test images, and then performing sliding detection on the test images using sample images, determining the matching relationship based on the matching value between the sample images and the test images at each detection position.

[0131] Determine the supplementary candidate regions where the marked locations are located in the updated test image, and determine the number of reference pixels in the test image corresponding to each supplementary candidate region that satisfy the preset relationship;

[0132] The supplementary candidate region corresponding to the image to be detected with the largest number of reference pixels that satisfy the preset relationship is determined as the second confirmation position;

[0133] The first or second confirmed position is determined as the final position based on the number of reference pixels that satisfy the preset relationship within the first and second confirmed positions, respectively.

[0134] Based on the final location, determine the position of the marker on the edge of the target object.

[0135] Here, the first confirmed location can be the possible location of the marker position determined by marker position detection on all the images to be tested. Adjacent images to be tested can be two images acquired in adjacent order. The updated image to be tested can be a stitched image formed by stitching adjacent images pairwise. The second confirmed location can be the possible location of the marker position determined by marker position detection on all the updated images to be tested.

[0136] In this embodiment, the updated test image is subjected to marker location detection again, i.e., step S22 is executed again, thereby determining the supplementary candidate regions where the marker locations are located in the updated test image, and determining the number of reference pixels in the test image corresponding to each supplementary candidate region that satisfy a preset relationship. The supplementary candidate region corresponding to the test image with the largest number of reference pixels is determined as the second confirmed position. Finally, by comparing the number of reference pixels satisfying the preset relationship in the first confirmed position and the second confirmed position, the confirmed position with the largest number of reference pixels is determined as the final position. Thus, the position of the marker location on the edge of the target object can be determined based on the final position.

[0137] In the above embodiments, by stitching adjacent test images pairwise to obtain an updated test image, and then performing marker position detection on the updated test image, the marker position can be detected quickly and accurately even if the marker position is located in both test images. Furthermore, by comparing the number of reference pixels satisfying a preset relationship within the first and second confirmation positions, the location of the marker position can be accurately detected regardless of whether it is located in either test image.

[0138] It should be noted that, in the embodiments of this application, the identification position detection of the individual image to be tested and the updated image to be tested can be performed simultaneously.

[0139] To gain a more comprehensive understanding of the method for detecting the edge marker position of an object provided in this application, another method for detecting the edge marker position of an object can be applied to, for example... Figure 1 The detection equipment shown. In this embodiment of the application, the target object can be a wafer, and the image of the wafer to be tested is as follows: Figure 4 As shown, the sample image can be a wafer Notch template. Figure 9 This diagram illustrates a flowchart of another method for detecting the edge marker position of an object to be tested, provided in an embodiment of this application. Figure 9 As shown, the method for detecting the edge marker position of the object to be tested includes the following steps.

[0140] S91, acquire the image to be tested containing the edge portion of the wafer, and determine the wafer edge region from the image to be tested.

[0141] Here, the image to be tested can be multiple images acquired sequentially while rotating along the edge of the wafer. In the image to be tested, the background elements outside the wafer are relatively dark, while the wafer brightness is relatively brighter than the background elements. The wafer edge region can be determined from the image to be tested using image segmentation methods.

[0142] S92, obtain the wafer Notch template.

[0143] S93, based on the wafer Notch template, template matching is performed in the wafer edge region to obtain the matching relationship between each image to be tested and the wafer Notch template.

[0144] Here, template matching can be performed based on standardized differences.

[0145] S94, which constructs multiple connected components from the target matching values ​​in the matching relationship that are below the matching threshold.

[0146] Here, multiple consecutive target matching values ​​form a connected component, and target matching values ​​that are separated in the matching relationship are each treated as an independent connected component. Optionally, the matching threshold can be 0.3.

[0147] S95, construct a mask map based on each connected component, and take the image region corresponding to the minimum matching value in the mask map as the candidate Notch (i.e., the candidate part where the identifier is located).

[0148] Here, a mask map of the image to be tested is constructed based on connected components. The image regions corresponding to the connected components are the selected regions of the mask map.

[0149] S96, verify whether each candidate Notch has a semi-circular spatial structure feature.

[0150] Here, the verification of candidate sites can be performed using the method provided in the foregoing embodiments.

[0151] S97, identify the most prominent semi-circular shape as the final Notch, and determine the position of the final Notch at the edge of the wafer.

[0152] In the above embodiments, template detection is performed on the test image containing the edge portion of the wafer using a wafer notch template. Connected regions that meet the matching threshold are selected, and the image region corresponding to the smallest matching value in each connected region is selected as a candidate notch. By verifying the shape features of the candidate notches, the candidate notch with the most obvious features is selected as the final notch for localization, thereby determining the position of the notch on the wafer edge. In this way, the position of the notch can be accurately located, thereby improving the accuracy of wafer pre-alignment, reducing the difficulty of localization, reducing the amount of computation, and improving localization efficiency.

[0153] In some embodiments, after S91, the method for detecting the edge marker position of the object to be tested may further include:

[0154] Adjacent test images are stitched together in pairs to form updated test images.

[0155] Here, marker position detection is performed on both the image under test and the updated image under test. This allows for accurate location of the notch even when the marker position is located in both images under test, thereby improving the accuracy of wafer pre-alignment, reducing positioning difficulty, computational load, and increasing positioning efficiency.

[0156] In another aspect, this application provides a computer program product, including computer instructions. When executed by a processor, the computer instructions implement the detection method for the edge marker position of an object under test as described in any embodiment of this application, and can achieve the same technical effect as the detection method for the edge marker position of an object under test provided in the foregoing embodiments.

[0157] It should be noted that the computer program product provided in the above embodiments is only illustrated by the division of the above program modules in the process of detecting the position of object markers. In practical applications, the above instructions can be assigned to different program modules as needed, that is, the computer program product can be divided into different program modules to complete all or part of the method steps described above. In addition, the method embodiments for detecting the position of the edge marker of the object to be measured provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0158] In another aspect, this application also provides a detection device. Figure 10 This diagram illustrates the structure of a detection device provided in an embodiment of this application. Figure 10As shown, the detection device includes a processor 101 and a memory 102. The memory 102 stores a computer program that can be executed by the processor. When the computer program is executed by the processor, it implements the detection method for the edge marker position of the object to be tested as described in any embodiment of this application. The detection device and the detection method for the edge marker position of the object to be tested provided in the foregoing embodiments can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0159] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the various processes of the above-described method for detecting the edge marker position of an object under test, and achieves the same technical effect. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0160] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for detecting the position of an edge marker on an object to be tested, applied to a testing device, characterized in that, include: Acquire the image to be tested, including the edge regions of the target object; The image to be tested is inspected to determine the contour line position of the edge portion of the target object; Sliding detection is performed on the sample image within the image region corresponding to the contour line position; the image region covered by the sample image at each detection position is determined as the current matching image; the matching value corresponding to each detection position of the sample image is determined according to the sum of squared normalized differences between each pixel in the sample image and each pixel in the current matching image; a matching relationship is formed according to the matching values ​​corresponding to each detection position; wherein, the sample image is a standard image including the edge part of the target object, and the edge part of the target object has a marked position; Based on the target matching value that meets the set requirements in the matching relationship, the candidate part where the marked position is located in the image to be tested is determined; The image region corresponding to the candidate part is segmented from the image to be tested to obtain the image to be tested; the location of the reference pixel with the maximum gray level gradient in each row of the image to be tested is detected; the number of reference pixels that satisfy a preset relationship is determined according to the coordinate values ​​of each reference pixel in the image to be tested and the coordinate values ​​of the corresponding comparison pixel, wherein the preset relationship is that the coordinate values ​​of the reference pixels are greater than or less than the coordinate values ​​of the corresponding comparison pixel; the position of the marker position is determined at the edge of the target object according to the number.

2. The method for detecting the edge marker position of the object to be tested as described in claim 1, characterized in that, The acquisition of the test image, including the edge regions of the target object, includes: Multiple test images, including the edge portion, are acquired sequentially by an image acquisition device rotating along the edge of a target object; wherein each test image includes a corresponding image of a portion of the edge portion of the target object.

3. The method for detecting the edge marker position of the object to be tested as described in claim 1, characterized in that, The step of determining the candidate region where the identified location is located in the image to be tested based on the target matching value that meets the set requirements in the matching relationship includes: Determine the target matching value in the matching relationship that is less than a preset value; A mask image is determined based on the target matching value, and the image region of the mask image corresponding to the minimum matching value is used as the candidate region where the marked position is located in the image to be tested.

4. The method for detecting the edge marker position of the object to be tested as described in claim 3, characterized in that, The step of determining the mask image based on the target matching value, and using the image region of the mask image corresponding to the minimum matching value as the candidate region where the identified position is located in the image to be tested, includes: Multiple target matching values ​​that are consecutive in the matching relationship are taken as the same connected component, and target matching values ​​that are separate in the matching relationship are taken as independent connected components. The corresponding mask diagrams are determined according to the connected components. The image region of the mask corresponding to the minimum matching value is taken as the candidate region where the marked position is located in the image to be tested.

5. The method for detecting the edge marker position of an object to be tested as described in claim 1, characterized in that, The step of determining the number of reference pixels that satisfy a preset relationship based on the coordinate values ​​of each reference pixel in the image to be inspected and the coordinate values ​​of the corresponding comparison pixel includes: The count value of the reference pixel that satisfies the preset relationship is initialized to 0; For the upper half of the image to be tested corresponding to the candidate region, it is sequentially determined whether the coordinate value of each reference pixel is greater than the coordinate value of the comparison pixel at a distance of n rows, where n is a positive integer; If so, the count value is incremented by 1, and the updated count value is determined as the number of reference pixels that satisfy the preset relationship; For the lower half of the image to be tested corresponding to the candidate part, sequentially determine whether the coordinate value of the comparison pixel point that is n rows apart from each reference pixel point is greater than the coordinate value of the reference pixel point; If so, the count value is incremented by 1, and the updated count value is determined as the number of reference pixels that satisfy the preset relationship.

6. The method for detecting the edge marker position of an object to be tested as described in claim 1, characterized in that, Determining the position of the marker at the edge of the target object based on the quantity includes: The candidate region corresponding to the image to be tested with the largest number of reference pixels that satisfy the preset relationship is determined as the final position; Based on the final position, the location of the marker is determined at the edge of the target object.

7. The method for detecting the edge marker position of an object to be tested as described in claim 1, characterized in that, Determining the position of the marker at the edge of the target object based on the quantity includes: The candidate region corresponding to the image to be inspected with the largest number of reference pixels that satisfy the preset relationship is determined as the first confirmation position; The adjacent test images are stitched together pairwise to form updated test images, and the process of performing sliding detection on the test images using sample images and determining the matching relationship based on the matching value between the sample images and the test images at each detection position is returned. Determine the supplementary candidate regions where the identified locations are located in the updated image to be tested, and determine the number of reference pixels in the image to be tested corresponding to each supplementary candidate region that satisfy the preset relationship; The supplementary candidate region corresponding to the image to be tested with the largest number of reference pixels that satisfy the preset relationship is determined as the second confirmation position; Based on the number of reference pixels that satisfy a preset relationship within the first and second confirmed positions, the first confirmed position or the second confirmed position is determined as the final position. Based on the final position, the location of the marker is determined at the edge of the target object.

8. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the detection device, the detection device performs the detection method for the edge marker position of the object to be tested as described in any one of claims 1 to 7.

9. A testing device, characterized in that, Including processor and memory, The memory stores computer programs that can be executed by the processor; When the computer program is executed by the processor, it implements the method for detecting the edge marker position of the object under test as described in any one of claims 1 to 7.

10. A detection system, characterized in that, Includes an image acquisition device and the detection device as described in claim 9. The image acquisition device is used to rotate and scan around the edge of the target object, sequentially acquiring multiple test images of the edge portion of the target object, and sending the multiple test images to the detection device respectively. After acquiring each test image, the detection device performs the detection method for the edge marker position of the target object as described in any one of claims 1 to 7; or, The image acquisition device is used to rotate and scan around the edge of the target object, sequentially acquire test images of the edge parts of the target object, and send the test images to the detection device. After acquiring the test images, the detection device performs the detection method for the edge marker position of the test object as described in any one of claims 1 to 7 on the test images.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by the controller, implements the method for detecting the edge marker position of the object under test as described in any one of claims 1 to 7.

Images