Thickness determination method, thickness determination apparatus, thickness determination device, and storage medium

Abstract

A thickness determination method, a thickness determination apparatus, a thickness determination device and a storage medium, relating to the field of image processing and the field of optical inspection. The thickness determination method comprises: according to an image feature of a reference image of a sample under test, dividing the reference image into a plurality of image regions (S210), the reference image being an image of said sample acquired by an image acquisition apparatus at a reference acquisition angle and at a reference acquisition distance; acquiring a plurality of inspection images of said sample (S220), the plurality of inspection images being images acquired by the image acquisition apparatus at a plurality of inspection acquisition distances and at the reference acquisition angle, and the plurality of inspection acquisition distances not being equal to each other; on the basis of the proportions of the plurality of image regions in the reference image, dividing each inspection image among the plurality of inspection images into a plurality of inspection regions (S230); and, according to the corresponding relationship between respective definitions of the plurality of inspection regions in the plurality of inspection images and the plurality of inspection acquisition distances, determining the thicknesses of a plurality parts of said sample (S240).

Description

Thickness determination method, thickness determination device, thickness determination equipment and storage medium Technical Field

[0001] The present disclosure relates to the fields of image processing and optical testing, and in particular to a thickness determination method, a thickness determination apparatus, a thickness determination device, and a storage medium. Background Art

[0002] Automated Optical Inspection (AOI) of electronic components using optical devices is widely used in the electronics manufacturing industry due to its low cost, simple implementation, and rich functionality. However, due to the complex surface structure of electronic components and the inherent limitations of optical devices, images captured during AOI testing of electronic components can be out of focus, resulting in insufficient test results.

[0003] Summary of the Invention

[0004] The present disclosure provides a thickness determination method, a thickness determination apparatus, a thickness determination device, and a storage medium.

[0005] According to a first aspect, the present disclosure provides a thickness determination method, comprising: dividing the reference image into multiple image areas according to image features of the reference image of the sample to be tested, the reference image being an image of the sample to be tested acquired by an image acquisition device at a reference acquisition angle and a reference acquisition distance, the reference acquisition distance characterizing that the distance between the image acquisition device and a specified area in the sample to be tested is the focal length of the image acquisition device; acquiring multiple test images of the sample to be tested, the multiple test images being images acquired by the image acquisition device at multiple test acquisition distances and reference acquisition angles, the multiple test acquisition distances being unequal to each other; dividing each of the multiple test images into multiple test areas based on a proportion of the multiple image areas in the reference image, the multiple test areas of each test image corresponding to the multiple image areas respectively; and determining the thickness of multiple parts of the sample to be tested according to the correspondence between the clarity of each of the multiple test areas in the multiple test images and the multiple test acquisition distances.

[0006] For example, based on the image features of the reference image of the sample to be tested, dividing the reference image into multiple image areas includes: using multiple first preset pixel thresholds to binarize the reference image to obtain multiple preprocessed images; dividing the multiple preprocessed images according to the pixel value distribution of the multiple preprocessed images to obtain multiple image areas.

[0007] For example, dividing the multiple preprocessed images into multiple image regions based on the pixel value distribution of the multiple preprocessed images includes: dividing the multiple preprocessed images into multiple preprocessed regions respectively; for each preprocessed image in the multiple preprocessed images, replacing the pixel value in the corresponding preprocessed region with the average pixel value of the multiple preprocessed regions to obtain multiple initial images; binarizing the multiple initial images using a second preset pixel threshold to obtain multiple first images to be divided; and dividing the multiple first images to be divided based on the pixel value distribution of the multiple first images to be divided to obtain multiple image regions.

[0008] For example, dividing the multiple preprocessed images according to the pixel value distribution of the multiple preprocessed images to obtain multiple image regions includes: dividing the multiple preprocessed images according to the pixel value distribution of the multiple preprocessed images to obtain multiple initial regions of the multiple preprocessed images; and determining multiple image regions from the multiple initial regions according to the distribution of the multiple initial regions of the multiple preprocessed images on the reference image.

[0009] For example, based on the image features of the reference image of the sample to be tested, dividing the reference image into multiple image areas includes: dividing the reference image into multiple reference areas; binarizing the multiple reference areas according to the respective pixel average values ​​of the multiple reference areas to obtain a second image to be divided; and dividing the second image to be divided into multiple image areas according to the pixel value distribution of the second image to be divided.

[0010] For example, determining the thickness of multiple parts of the sample to be tested based on the correspondence between the clarity of each of multiple test areas in multiple test images and multiple test acquisition distances includes: for each of the multiple test areas, determining multiple contrast values ​​of the test area in multiple test images; determining the target acquisition distance corresponding to each of the multiple test areas from the multiple test acquisition distances, the target acquisition distance being the test acquisition distance corresponding to the maximum contrast value among the multiple contrast values ​​of the test area; and determining the thickness difference between the multiple parts based on the difference between the multiple target acquisition distances corresponding to the multiple test areas.

[0011] For example, the thickness determination method also includes: obtaining target area images of each of the multiple test areas from multiple test images, the target area image being an area image corresponding to the maximum clarity among multiple clarity of each of the multiple test areas; and combining the target area images of each of the multiple test areas into a target image of the sample to be tested based on the positional relationship between the multiple test areas.

[0012] For example, the thickness determination method further includes: measuring the size parameters of the sample to be measured in the target image.

[0013] For example, the thickness determination method also includes: setting an imaging image of the laser emitted by the laser source on the surface of the sample to be measured, the image size of the imaging image representing the distance between the image acquisition device and the specified area; and at a reference acquisition angle, when it is determined that the imaging image is shrunk to a point on the specified area, obtaining a reference image of the sample to be measured from the image acquisition device.

[0014] For example, determining the thickness of multiple parts of the sample to be tested based on the correspondence between the clarity of multiple test areas in multiple test images and multiple test acquisition distances includes: determining multiple image sizes of imaging images corresponding to the multiple test images; determining multiple target image sizes of the multiple test areas from the multiple image sizes, the target image size being the image size corresponding to the test image corresponding to the maximum clarity among the clarity of the multiple test areas; determining multiple target acquisition distances corresponding to the multiple target image sizes based on the relationship between the image size and the multiple test acquisition distances; and determining the thickness difference between the multiple parts based on the difference between the multiple target acquisition distances corresponding to the multiple test areas.

[0015] For example, determining the thickness of multiple parts of the sample to be tested based on the correspondence between the clarity of multiple test areas in multiple test images and multiple test acquisition distances also includes: determining the baseline thickness value of the specified part corresponding to the specified area in the sample to be tested and the baseline acquisition distance corresponding to the baseline image; determining the thickness value of each of the multiple parts based on the target acquisition distance, baseline thickness value and baseline acquisition distance corresponding to each of the multiple test areas.

[0016] According to a second aspect, the present disclosure provides a thickness determination device, including: a division module, used to divide the reference image into multiple image areas according to image features of the reference image of the sample to be tested, the reference image being an image of the sample to be tested acquired by an image acquisition device at a reference acquisition angle and a reference acquisition distance, the reference acquisition distance characterizing that the distance between the image acquisition device and a specified area in the sample to be tested is the focal length of the image acquisition device; a first acquisition module, used to acquire multiple test images of the sample to be tested, the multiple test images being images acquired by the image acquisition device at multiple test acquisition distances and reference acquisition angles, the multiple test acquisition distances being unequal to each other; a first determination module, used to divide each of the multiple test images into multiple test areas based on the proportion of the multiple image areas in the reference image, the multiple test areas of each test image corresponding to the multiple image areas respectively; and a second determination module, used to determine the thickness of multiple parts of the sample to be tested based on the correspondence between the clarity of each of the multiple test areas in the multiple test images and the multiple test acquisition distances.

[0017] For example, the division module includes: a first processing unit, used to binarize the reference image using multiple first preset pixel thresholds to obtain multiple preprocessed images; a first division unit, used to divide the multiple preprocessed images according to the pixel value distribution of the multiple preprocessed images to obtain multiple image areas.

[0018] For example, the first division unit is used to: divide multiple preprocessed images into multiple preprocessed areas respectively; for each preprocessed image in the multiple preprocessed images, use the average pixel value of each of the multiple preprocessed areas to replace the pixel value in the corresponding preprocessed area to obtain multiple initial images; use the second preset pixel threshold to binarize the multiple initial images to obtain multiple first images to be divided; and divide the multiple first images to be divided according to the pixel value distribution of the multiple first images to be divided to obtain multiple image areas.

[0019] For example, the first division unit is used to: divide the multiple preprocessed images according to the pixel value distribution of the multiple preprocessed images to obtain multiple initial regions of the multiple preprocessed images; and determine multiple image regions from the multiple initial regions according to the distribution of the multiple initial regions of the multiple preprocessed images on the reference image.

[0020] For example, the division module includes: a second division unit, used to divide the reference image into multiple reference areas; a second processing unit, used to binarize the multiple reference areas according to the respective pixel average values ​​of the multiple reference areas to obtain a second image to be divided; and a third division unit, used to divide the second image to be divided into multiple image areas according to the pixel value distribution of the second image to be divided.

[0021] For example, the second determination module includes: a first determination unit, used to determine, for each of the multiple test areas, multiple contrast values ​​of the test area in the multiple test images; a second determination unit, used to determine the target acquisition distance corresponding to each of the multiple test areas from the multiple test acquisition distances, the target acquisition distance being the test acquisition distance corresponding to the maximum contrast value among the multiple contrast values ​​of the test area; and a third determination unit, used to determine the thickness difference between the multiple parts based on the difference between the multiple target acquisition distances corresponding to the multiple test areas.

[0022] For example, the thickness determination device also includes: a second acquisition module, used to obtain the target area images of each of the multiple test areas from the multiple test images, where the target area images are area images corresponding to the maximum clarity among the multiple clarity of each of the multiple test areas; and a combination module, used to combine the target area images of each of the multiple test areas into a target image of the sample to be tested based on the positional relationship between the multiple test areas.

[0023] For example, the thickness determination device also includes: a setting module, used to set the imaging image of the laser emitted by the laser source on the surface of the sample to be tested, the image size of the imaging image representing the test acquisition distance between the image acquisition device and the specified area; and a third acquisition module, used to acquire a reference image of the sample to be tested from the image acquisition device at a reference acquisition angle, when it is determined that the imaging image is shrunk to a point on the specified area.

[0024] For example, the second determination module includes: a fourth determination unit, used to determine multiple image sizes of imaging images corresponding to multiple test images; a fifth determination unit, used to determine multiple target image sizes of multiple test areas from the multiple image sizes, the target image size being the image size corresponding to the test image corresponding to the maximum clarity among the multiple clarity of each of the multiple test areas; a sixth determination unit, used to determine multiple target acquisition distances corresponding to multiple target image sizes based on the relationship between the image size and the multiple test acquisition distances; and a seventh determination unit, used to determine the thickness difference between multiple parts based on the difference between the multiple target acquisition distances corresponding to the multiple test areas.

[0025] For example, the second determination module also includes: an eighth determination unit, used to determine the baseline thickness value of the specified part corresponding to the specified area in the sample to be tested and the baseline acquisition distance corresponding to the baseline image; a ninth determination unit, used to determine the thickness values ​​of multiple parts based on the target acquisition distance, baseline thickness value and baseline acquisition distance corresponding to each of the multiple test areas.

[0026] According to a third aspect, the present disclosure provides a thickness determination device, comprising: a laser source configured to emit laser light toward a sample to be measured; an image acquisition device configured to capture a reference image of the sample to be measured and multiple tests based on an imaging image of the laser light on the sample to be measured; a processor configured to control the distance between the image acquisition device and a specified area in the sample to be measured; and a memory communicatively connected to the processor and configured to store instructions executable by the processor, the instructions being executed by the processor so that the processor can execute the thickness determination method provided by the present disclosure.

[0027] For example, the position of the laser source and the position of the image acquisition device are relatively fixed.

[0028] According to a fourth aspect, the present disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the thickness determination method provided by the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] FIG1 is a schematic diagram of an exemplary sample image of a sample to be tested;

[0030] FIG2A is a flow chart of a thickness determination method according to an embodiment of the present disclosure;

[0031] FIG2B is a schematic diagram of an image area, a test area, and a sample site according to an embodiment of the present disclosure;

[0032] FIG3 is a schematic structural diagram of a thickness determination device according to an embodiment of the present disclosure;

[0033] FIG4A is a schematic diagram of an imaging image according to an embodiment of the present disclosure;

[0034] FIG4B is a schematic diagram of an imaging image according to another embodiment of the present disclosure;

[0035] FIG5A is a schematic diagram of a binarized reference image according to an embodiment of the present disclosure;

[0036] FIG5B is a schematic diagram of dividing a pre-processed image according to an embodiment of the present disclosure;

[0037] FIG5C is a schematic diagram of determining an image area according to an embodiment of the present disclosure;

[0038] FIG6 is a schematic diagram of determining thickness according to an embodiment of the present disclosure; and

[0039] FIG. 7 is a schematic diagram of a thickness determination device according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of them. Based on the described embodiments of the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure. It should be noted that throughout the drawings, the same elements are represented by the same or similar figure marks. In the following description, some specific embodiments are only for descriptive purposes and should not be understood as any limitation to the present disclosure, but are only examples of the embodiments of the present disclosure. Conventional structures or configurations will be omitted when they may cause confusion in the understanding of the present disclosure. It should be noted that the shapes and sizes of the components in the figures do not reflect the actual size and proportion, but only illustrate the contents of the embodiments of the present disclosure.

[0041] Unless otherwise defined, technical or scientific terms used in the embodiments of the present disclosure shall have the same general meaning as those skilled in the art. The terms "first," "second," and similar terms used in the embodiments of the present disclosure do not denote any order, quantity, or importance, but are merely used to distinguish different components.

[0042] In addition, in the description of the embodiments of the present disclosure, the term "connected" or "connected to" may refer to a direct connection between two components or a connection between two components via one or more other components. In addition, the two components may be connected or coupled via a wired or wireless manner.

[0043] 1A-1C are schematic structural diagrams of an exemplary sample image of a sample to be tested.

[0044] AOI imaging is performed on the sample to be tested to obtain a sample image 100 of the sample to be tested. The sample image 100 can be an image of the sample to be tested taken at a reference angle. For example, the sample to be tested is photographed directly above the sample to be tested to obtain the sample image 100. The sample image 100 can represent the surface morphology of the sample to be tested from a top-down angle. As shown in FIG1A , edge detection and image feature detection are performed on the sample image 100, and a standard line AA' is determined based on the detection results and human judgment experience. The standard line AA' can divide the sample to be tested into a circuit area and a barrier area. For example, the standard line AA' divides the sample image 100 into a first area 101 and a second area 102. The first area 101 represents the surface morphology of the circuit area of ​​the sample to be tested, and the first area 101 reflects the circuit distribution of the circuit area. The second area 102 represents the surface morphology of the barrier area of ​​the sample to be tested. There is no circuit distribution in the barrier area, and the barrier area is used to prevent water and oxygen from entering the circuit area.

[0045] As shown in FIG1B , edge detection and image feature detection are performed on the sample image 100, and a cutting line BB' is determined based on the detection results and human judgment experience. The cutting line BB' is a laser cutting line, and the sample to be tested can be cut based on the corresponding position of the cutting line BB' in the sample to be tested. For example, the cutting line BB' divides the sample image 100 into a third area 103 and a fourth area 104. For example, the third area 103 is a functional area of ​​the sample to be tested, and the fourth area 104 is a non-functional area of ​​the sample to be tested. For example, the non-functional area is located in the barrier area shown in FIG1A . For example, a functional element is provided in the portion of the sample to be tested represented by the third area 103. The portion of the sample to be tested represented by the fourth area 104 is the underlying substrate of the sample to be tested. Based on the cutting line BB', the redundant underlying substrate in the sample to be tested can be cut off, that is, the portion of the sample to be tested represented by the fourth area 104 can be cut off.

[0046] In the embodiment of the present disclosure, a standard line AA' and a cutting line BB' can be marked in the same sample image 100, as shown in FIG1C . Combining FIG1A , FIG1B , and FIG1C , it can be seen that area 105 shown in FIG1C is the circuit area of ​​the sample to be tested, areas 106 and 107 shown in FIG1C form the barrier area of ​​the sample to be tested, areas 105 and 106 shown in FIG1C form the functional area of ​​the sample to be tested, and area 107 shown in FIG1C is the non-functional area of ​​the sample to be tested.

[0047] However, the sample to be tested may be irregular, so the thickness of different parts of the sample to be tested may not be the same. For example, the sample image 100 can be a top view of the sample to be tested, and the sample image 100 can represent the morphological features of the upper surface of the sample to be tested. The upper surface of the sample represented by the sample image 100 is not a flat surface. The distance from any point on the upper surface of the sample to be tested to the bottom of the sample is considered to be the thickness of that point in the sample to be tested. Therefore, the thickness of the parts where the standard line AA' and the cutting line BB' are located in the sample to be tested may be different. For example, from the perspective of the main view, the heights of the corresponding positions of the standard line AA' and the cutting line BB' in the top view may be different in the sample to be tested.

[0048] During AOI imaging, because the distances between the corresponding positions of the standard line AA' and the cutting line BB' in the sample to be tested and the AOI imaging lens are different, this can cause the image of the position where the standard line AA' or the cutting line BB' is located in the sample image 100 to be out of focus, i.e., it is impossible to capture a clear image of the position where the standard line AA' or the cutting line BB' is located. For example, when the distance between the corresponding position of the standard line AA' in the sample to be tested and the AOI imaging lens is equal to the focal length of the AOI imaging device, the distance between the corresponding position of the cutting line BB' in the sample to be tested and the AOI imaging lens is greater than or less than the focal length of the AOI imaging device; or when the distance between the corresponding position of the cutting line BB' in the sample to be tested and the AOI imaging lens is equal to the focal length of the AOI imaging device, the distance between the corresponding position of the standard line AA' in the sample to be tested and the AOI imaging lens is greater than or less than the focal length of the AOI imaging device. Therefore, the images of the positions of the standard line AA' and the cutting line BB' in the sample image 100 are not clear at the same time, which may cause large errors in edge detection of the sample image 100, resulting in inaccurate positions of the standard line AA' and the cutting line BB' marked in the sample image.

[0049] Accordingly, the thickness of different parts of the sample to be tested may not be the same. Sample image 100 may only have a clear image in some areas, while other areas may be blurred. This results in large errors when measuring various dimensional parameters of the sample to be tested based on sample image 100, which may affect subsequent production processes.

[0050] According to one example, the lens or the sample under test can be moved to focus on different points on the sample's surface, thereby capturing clear images of the areas corresponding to each point on the sample. This testing method requires moving the lens or the sample under test, changing their position, making the AOI imaging device more complex and costly. Furthermore, the additional movement time required significantly reduces inspection efficiency.

[0051] Based on at least one of the above-mentioned problems, the present disclosure provides a sample testing method, including: dividing the reference image into multiple image areas according to image features of the reference image of the sample to be tested, the reference image being an image of the sample to be tested acquired by an image acquisition device at a reference acquisition angle and a reference acquisition distance, the reference acquisition distance characterizing that the distance between the image acquisition device and a specified area in the sample to be tested is the focal length of the image acquisition device; acquiring multiple test images of the sample to be tested, the multiple test images being images acquired by the image acquisition device at multiple test acquisition distances and reference acquisition angles, the multiple test acquisition distances being unequal to each other; dividing each test image of the multiple test images into multiple test areas based on the proportion of the multiple image areas in the reference image, the multiple test areas of each test image corresponding to the multiple image areas respectively; and determining the thickness of multiple parts of the sample to be tested according to the correspondence between the clarity of each of the multiple test areas in the multiple test images and the multiple test acquisition distances.

[0052] FIG2A is a flow chart of a thickness determination method according to an embodiment of the present disclosure.

[0053] As shown in FIG. 2A , the thickness determining method may include operations S210 to S240 .

[0054] In operation S210 , the reference image of the sample to be tested is divided into a plurality of image regions according to image features of the reference image.

[0055] In the embodiment of the present disclosure, the reference image is an image of the sample to be tested captured by the image acquisition device at a reference acquisition angle and a reference acquisition distance. The reference acquisition distance represents that the distance between the image acquisition device and a specified area in the sample to be tested is the focal length of the image acquisition device.

[0056] For example, the reference acquisition angle can be any acquisition angle. The acquisition angle represents the shooting angle of the image acquisition device relative to the sample to be tested. When the image acquisition device is located directly above the sample to be tested, the reference acquisition angle represents the image acquisition device's top-down view of the sample to be tested. When the image acquisition device is located directly in front of the sample to be tested, the reference acquisition angle represents the image acquisition device's front-view view of the sample to be tested. When the image acquisition device is located to the left of the sample to be tested, the reference acquisition angle represents the image acquisition device's left-view view of the sample to be tested.

[0057] In the embodiments of the present disclosure, the designated area may be any area on the surface of the sample to be tested. For example, the designated area is any area on the surface of the sample to be tested that can be captured by the image acquisition device at the reference acquisition angle. The designated area is related to the reference acquisition angle. For example, when the reference acquisition angle represents a top-down angle shot, the designated area is any area on the upper surface of the sample to be tested. When the reference acquisition angle represents a left-view angle shot, the designated area is any area on the left side surface of the sample to be tested.

[0058] For example, when the reference capture angle represents a top-down view, the designated area can be the central region of the top surface of the sample to be tested. When the image capture device is positioned directly above the central region of the top surface of the sample to be tested, the reference image captured by the image capture device is a top-down view of the sample to be tested. In the reference image, the central region of the top surface of the sample to be tested is the clearest, and the distance between the image capture device and the central region is the focal length of the image capture device.

[0059] In the disclosed embodiments, the image capture device may be a camera. When the distance between a designated area and the camera lens is exactly the focal length of the camera, the camera's autofocus component automatically focuses on the designated area, bringing the camera into focus. At this point, the camera can capture the clearest image of the designated area.

[0060] In the disclosed embodiments, the image features may be pixel features of the reference image. For example, the reference image may be color. Based on the distribution of pixel values, the reference image may be segmented based on the distribution of different colors, resulting in image regions with different colors. For example, the pixel features may be grayscale values. Based on the distribution of grayscale values, the reference image may be segmented based on the distribution of different brightness levels, resulting in image regions with different brightness levels.

[0061] In operation S220 , a plurality of test images of the sample to be tested are acquired.

[0062] In the embodiment of the present disclosure, the multiple test images are images captured by the image capture device at multiple test capture distances and reference capture angles, and the multiple test capture distances are not equal to each other.

[0063] The test acquisition distance is the distance between the image acquisition device and the designated area. The reference acquisition distance can be one of a plurality of test acquisition distances. For example, when the test acquisition distance is the reference acquisition distance, and the test acquisition distance is equal to the focal length of the camera of the image acquisition device, the image acquisition device is in focus on the designated area and can capture a clear image of the designated area. When the test acquisition distance is greater than or less than the focal length of the camera of the image acquisition device, the image acquisition device is out of focus on the designated area, and the image captured of the designated area is blurry. Because the thickness of different parts of the sample to be tested may vary, when the distance between the image acquisition device and the designated area is the focal length, the distance between the image acquisition device and the surface of other parts of the sample to be tested is greater than or less than the focal length. Therefore, in the reference image, the image of the designated area is clear, while the images of other parts may be blurry.

[0064] When the test acquisition distance is changed, the image acquisition device may lose focus on the designated area, but may maintain focus on other sample surface areas. In this case, the designated area in the captured image will be blurred, while other sample surface areas may be clear. Therefore, by continuously changing the test acquisition distance, the image acquisition device can ensure that the image capture device captures a clear image of each sample surface area.

[0065] In the disclosed embodiment, the image acquisition device captures the reference image at the same angle as the test image. For example, when capturing the reference image, the image acquisition device is positioned directly above the sample to be tested (above the center of the upper surface). When capturing the test image, the image acquisition device is still positioned directly above the sample to be tested.

[0066] For example, the image acquisition device and the sample to be tested can be located on the same vertical line. The test acquisition distance can be changed by moving the image acquisition device or the sample to be tested. However, during the movement, the image acquisition device and the sample to be tested remain on the same vertical line. Therefore, the image acquisition device's acquisition angle of the sample to be tested remains unchanged, while the test acquisition distance changes.

[0067] For example, the distance between the image acquisition device and the designated area of ​​the sample to be tested can be changed by moving the image acquisition device. For example, when the image acquisition device is moved within the depth of field of the image acquisition device, the distance of each movement is much smaller than the focal length of the image acquisition device. Therefore, the number of movements can be determined based on the depth of field, the focal length, and the distance of a single movement. For example, the product of the number of movements and the distance of a single movement is equal to the depth of field, and the distance of a single movement is much smaller than the focal length. For example, the number of movements and the distance of a single movement can also be set based on actual conditions, but it is necessary to ensure that the distance of a single movement is much smaller than the focal length of the image acquisition device (for example, the focal length of the image acquisition device is at least 100 times the distance of a single movement) and that the product of the number of movements and the distance of a single movement is less than or equal to the depth of field.

[0068] For example, the image acquisition device has a focal length of 65 mm and a depth of field of 2 mm. Therefore, the image acquisition device is moved while maintaining the distance between the image acquisition device and the designated area of ​​the sample under test within a range of 64 mm to 66 mm. When the distance between the image acquisition device and the designated area of ​​the sample under test is greater than 66 mm or less than 64 mm, the image acquisition device captures blurry images of the entire sample area. When the distance between the image acquisition device and the designated area of ​​the sample under test varies within the range of 64 mm to 66 mm, the image acquisition device can capture clear images of different surface areas of the sample under test at different test acquisition distances.

[0069] For example, the distance of a single movement of the image acquisition device can be 0.1 mm. Because the distance the image acquisition device moves each time is much smaller than the test acquisition distance, the surface dimensions representing the same sample portion in multiple test images acquired at different test acquisition distances can be considered identical. For example, when the test acquisition distance is 64 mm, test image 1 is acquired, and the length of a certain line measured in test image 1 is a μm. When the test acquisition distance is 66 mm, test image 2 is acquired, and the length of the same line measured in test image 2 is b μm. The difference between a μm and b μm is much smaller than the values ​​of a and b, so the difference in the length of the line in the two test images is negligible, and a can be considered to be equal to b. Therefore, the same sample portion has the same dimensions in multiple test images, and the ratio between the sample dimensions represented by the multiple test images and the actual dimensions of the sample to be tested can be considered to be fixed.

[0070] In some embodiments, when the sample size represented by the test image changes significantly after the test acquisition distance is changed, the size of the test image can be scaled so that the ratio between the sample size represented by multiple test images and the actual size of the sample to be tested is the same.

[0071] In operation S230, each of the plurality of test images is divided into a plurality of test areas based on ratios of the plurality of image areas in the reference image.

[0072] In the disclosed embodiment, the multiple test areas of each test image correspond to multiple image areas. The multiple test images and the reference image are captured from the same surface of the sample to be tested. For example, the multiple test images and the reference image can both be top views of the sample to be tested. The surface range of the sample to be tested represented by the multiple test images is consistent with the surface range of the sample to be tested represented by the reference image.

[0073] If the size of the reference image matches the size of the multiple test images, the multiple image regions in the reference image can be mapped to the multiple test images based on their proportions in the reference image, resulting in multiple test regions for each of the multiple test images. Therefore, the image range represented by each of the multiple image regions in the reference image corresponds one-to-one to the image range represented by each of the multiple test regions in each of the test images.

[0074] 2B , which is a schematic diagram of an image area, a test area, and a sample site according to an embodiment of the present disclosure.

[0075] As shown in FIG2B , the reference image I_ref is divided into four image regions 201, 202, 203, and 204. Based on image regions 201, 202, 203, and 204, four test regions 201', 202', 203', and 204' of the test image I_test are determined. For example, the area ratios between image regions 201, 202, 203, and 204 are the same as the area ratios between the four test regions 201', 202', 203', and 204' of the test image. When the widths of all image regions and test regions are the same, the length ratios between image regions 201, 202, 203, and 204 are the same as the length ratios between the four test regions 201', 202', 203', and 204' of the test image.

[0076] The positions of image regions 201, 202, 203, and 204 in reference image I_ref are the same as those of test regions 201', 202', 203', and 204' in test image I_test, respectively. Image region 201 and test region 201' represent the surface topography of the same portion of the sample under test. Correspondingly, image region 202 and test region 202' represent the surface topography of the same portion of the sample under test, image region 203 and test region 203' represent the surface topography of the same portion of the sample under test, and image region 204 and test region 204' represent the surface topography of the same portion of the sample under test.

[0077] For example, the sample to be tested S includes a portion S1, a portion S2, a portion S3, and a portion S4. When the reference image I_ref and the test image I_test are both top views of the sample to be tested S, the image region 201 and the test region 201' represent the top surface morphology of portion S1 of the sample to be tested, the image region 2032 and the test region 202' represent the top surface morphology of portion S2 of the sample to be tested, the image region 203 and the test region 203' represent the top surface morphology of portion S3 of the sample to be tested, and the image region 204 and the test region 204' represent the top surface morphology of portion S4 of the sample to be tested.

[0078] In operation S240 , thicknesses of multiple locations of the sample to be tested are determined based on correspondences between the respective sharpnesses of the multiple test areas in the multiple test images and the multiple test acquisition distances.

[0079] In an embodiment of the present disclosure, as shown in FIG2B , multiple locations of the sample to be tested correspond to multiple test regions, and the multiple test regions characterize the surface topography of the multiple locations. For example, the multiple test regions can be multiple regions of a top view of the sample to be tested. The multiple regions of the top view correspond to multiple locations of the sample to be tested divided from a top view angle.

[0080] Based on the clarity of each test area in the multiple test images, the test distance between the sample portion corresponding to that test area and the image acquisition device can be determined. It should be noted that the test distance between the sample portion and the image acquisition device referred to in this disclosure refers to the height difference between the sample portion and the image acquisition device. For example, if the image acquisition device is located directly above the designated area, the line connecting the image acquisition device and the center of the designated area can be considered a vertical line perpendicular to the horizontal plane. Therefore, the test acquisition distance between the designated area and the image acquisition device represents the height difference between the designated area and the image acquisition device. However, not all of the multiple sample portions are located directly below the image acquisition device, and the line connecting the image acquisition device and the center of the surface of the sample portion is not necessarily a vertical line perpendicular to the horizontal plane. Therefore, the test acquisition distance between the sample portion and the image acquisition device is slightly greater than the height difference between the sample portion and the image acquisition device. Therefore, in the embodiments of this disclosure, unless otherwise specified, the test acquisition distance between the sample portion and the image acquisition device is equivalent to the height difference between the surface of the sample portion and the image acquisition device. That is, the test acquisition distance between the sample portion and the image acquisition device represents the distance between the image acquisition device and the horizontal plane where the surface of the sample portion lies. For example, when the image acquisition device acquires a top view of the sample to be tested, the test acquisition distance between the sample portion and the image acquisition device is the height difference between the upper surface of the sample portion and the image acquisition device.

[0081] For example, the closer the test acquisition distance between the image acquisition device and the sample part is to the focal length of the image acquisition device, the clearer the image of the test area corresponding to the sample part will be. When the test area corresponding to the sample part in a test image is the clearest, it can be considered that the test acquisition distance between the image acquisition device and the sample part is consistent with the focal length of the image acquisition device when acquiring the test image. Since the test acquisition distance between the image acquisition device and the designated area is known when acquiring the test image, the thickness difference between the sample part and the designated part where the designated area is located can be determined based on the difference between the test acquisition distance corresponding to the test image and the reference acquisition distance (focal length). Accordingly, the thickness difference between the two sample parts corresponding to the two test areas can be determined by the difference between the test acquisition distances corresponding to the two test images including the clearest images of the two test areas.

[0082] In the embodiment of the present disclosure, by changing the test acquisition distance within a focusing cycle, multiple test images continuously acquired by the image acquisition device are obtained, and clear images of different sample parts of the sample to be tested are acquired in different regions. This eliminates the need to move the image acquisition device above different sample parts of the sample to be tested for image acquisition, thereby improving the efficiency of acquiring images and thus improving the efficiency of determining the sample thickness.

[0083] FIG3 is a schematic structural diagram of a thickness determination device according to an embodiment of the present disclosure.

[0084] As shown in FIG3 , the thickness determination device 300 performs AOI imaging on the sample S to be measured, obtains a reference image and a test image, and determines thickness information of different parts of the sample S to be measured based on the reference image and the test image.

[0085] In the embodiment of the present disclosure, the thickness determination device 300 includes a laser source 310 , an image acquisition device 320 , a processor 330 , and a memory 340 .

[0086] The laser source 310 emits laser light toward the sample S to be tested, and the laser light falls on a designated area S_R on the surface of the sample S to be tested. For example, the laser source 310 may be a laser generator.

[0087] The image acquisition device 320 captures a reference image and multiple test images of the sample S based on the image L formed by the laser on the sample S. For example, the reference image is an image of the sample S captured when the distance between the image acquisition device 320 and the designated area S_R is the focal length. The multiple test images are images of the sample S captured by the image acquisition device 320 at multiple test acquisition distances, where the test acquisition distance is the distance between the image acquisition device 320 and the designated area S_R.

[0088] The processor 330 controls the distance between the image acquisition device 320 and the designated area S_R of the sample S to be tested, and executes the method for determining the thickness of the sample S to be tested. For example, the processor 330 executes operations S210 to S240 described above. For example, the processor 330 can drive the laser source 310 and the image acquisition device 320 via a drive motor to control the distance between the image acquisition device 320 and the designated area of ​​the sample S to be tested, so that the image acquisition device 320 can capture the sample S to be tested at different test acquisition distances. The test acquisition distance can be determined by calculating the drive motor movement value.

[0089] The memory 340 is in communication with the processor 330. The memory 340 stores instructions that can be executed by the processor 330. The instructions are executed by the processor 330 so that the processor 330 can perform the above operations S210 to S240.

[0090] In the disclosed embodiment, the position of the laser source 310 is fixed relative to the position of the image acquisition device 320. For example, the laser source 310 and the image acquisition device 320 may be located at the same horizontal height or on the same vertical line. The laser source 310 and the image acquisition device 320 may also be integrated into one body.

[0091] Laser light emitted by laser source 310 falls on a designated area S_R on the surface of the sample S to be tested, forming an image L in the designated area S_R. For example, laser source 320 can emit a non-parallel laser beam, such as a conical beam. After passing through a hole in a specific pattern, the laser light that has passed through the hole converges in the designated area S_R, while the laser light that has not passed through the hole is blocked. The laser light that has passed through the hole gradually converges from the emission port toward the sample S to be tested. Based on the principle that light propagates in a straight line, the laser light can form an image in the designated area S_R after passing through the hole in the specific pattern.

[0092] When the distance between the laser source 310 and the designated area S_R is a specific distance, the non-parallel laser light can be shrunk to a single point in the designated area S_R. When the distance between the laser source 310 and the designated area S_R is less than the specific distance, the non-parallel laser light forms a specific pattern in the designated area S_R. In this case, the smaller the distance between the laser source 310 and the designated area S_R, the larger the size of the specific pattern formed by the non-parallel laser light in the designated area S_R. When the distance between the laser source 310 and the designated area S_R is greater than the specific distance, the non-parallel laser light forms a mirror image of the specific pattern in the designated area S_R. In this case, the larger the distance between the laser source 310 and the designated area S_R, the larger the size of the specific pattern formed by the non-parallel laser light in the designated area S_R.

[0093] In the disclosed embodiment, by setting the focal length of the image acquisition device 320 to coincide with the aforementioned specific distance, the test acquisition distance between the image acquisition device and the designated area S_R can be determined based on the size of the image L formed by the laser source 310 within the designated area S_R. Based on the size and shape of the image L, it is quickly possible to determine whether the test acquisition distance between the image acquisition device 320 and the designated area S_R is equal to, greater than, or less than the focal length. By synchronously moving the image acquisition device 320 and the laser source 310, when the laser light contracts to a single point within the designated area S_R, the distance between the image acquisition device 320 and the designated area S_R is equal to the focal length, and the image acquisition device 320 is in focus with respect to the designated area S_R.

[0094] For example, the processor 330 sets an imaging image of the laser emitted by the laser source 310 on the surface of the sample to be tested S, and the image size of the imaging image L represents the distance between the image acquisition device 320 and the specified area S_R; and at the reference acquisition angle, when it is determined that the imaging image L is shrunk to a point on the specified area S_R, the image acquisition device 320 acquires the reference image of the sample to be tested S, and the processor 330 obtains the reference image from the image acquisition device 320.

[0095] For example, the image L is formed by laser light passing through a hole in a specific pattern. The specific pattern is a non-centrally symmetrical image. For example, the specific pattern can be a triangle or a semicircle. Accordingly, the laser light emitted by the laser source 310 passing through the triangular hole can form a triangle on the surface of the sample S to be tested, and the laser light emitted by the laser source 310 passing through the semicircular hole can form a semicircle on the surface of the sample S to be tested.

[0096] When it is determined that the imaging image is shrunk to a point on the designated area, the distance between the image acquisition device 320 and the designated area S_R is the focal length of the image acquisition device 320. The image acquisition device 320 is in a focused state with respect to the designated area S_R. Therefore, in this state, the image acquisition device 320 can capture a reference image by photographing the sample to be tested 310.

[0097] In the disclosed embodiment, when the test acquisition distance is changed, the image L1 always falls within the designated area S_R. When the test acquisition distance is changed, if the image L does not shrink to a single point within the designated area S_R, the distance between the image acquisition device 320 and the designated area S_R is greater than or less than the focal length. The designated area S_R can be considered out of focus by the image acquisition device 320. However, other areas may be in focus by the image acquisition device 320, and thus clear images of the other areas can be captured.

[0098] The imaging process of an image is exemplarily described with reference to Figures 4A and 4B . Figure 4A is a schematic diagram of an image according to an embodiment of the present disclosure, and Figure 4B is a schematic diagram of an image according to another embodiment of the present disclosure.

[0099] As shown in Figure 4A , laser light emitted from a laser source passes through the hole in the specific pattern G and forms an image on a designated area S_R of the sample S to be tested. The distance from the laser source to the focal point P is the focal length F. When the distance between the laser source and the designated area S_R of the sample S to be tested is less than the focal length F of the image acquisition device, the image L1 formed by the laser light in the designated area S_R is consistent with the shape of the specific pattern G.

[0100] Within the range where the distance between the laser source and the designated area S_R of the sample S to be measured is less than the focal length F of the image acquisition device, the smaller the distance between the laser source and the designated area S_R, the larger the size of the image L1 formed by the laser in the designated area S_R. The larger the distance between the laser source and the designated area S_R, the smaller the size of the image L1 formed by the laser in the designated area S_R. When the distance between the laser source and the designated area S_R of the sample S to be measured is equal to the focal length F of the image acquisition device, the image L shrinks to a single point.

[0101] For example, the specific figure G is a triangle ABC, and the laser passes through the triangle ABC formed by the triangle ABC in the specified area S_R. Vertex A of triangle ABC forms vertex a of triangle ABC in the specified area S_R. Correspondingly, vertex B of triangle ABC forms vertex b of triangle ABC in the specified area S_R. Vertex C of triangle ABC forms vertex c of triangle ABC in the specified area S_R. Side AB of triangle ABC forms side ab of triangle ABC in the specified area S_R. Side BC of triangle ABC forms side bc of triangle ABC in the specified area S_R. Side AC of triangle ABC forms side ac of triangle ABC in the specified area S_R.

[0102] The size of image L1 is represented by the distance from a vertex to the opposite side of a triangle. For example, the size of image L1 is represented by the distance from vertex a to side bc in triangle abc. The larger the distance from a vertex to the opposite side of the triangle, the larger the size of image L1. The smaller the distance from a vertex to the opposite side of the triangle, the smaller the size of image L1.

[0103] As shown in Figure 4B , the laser passes through the hole in the specific pattern G and forms an image on the designated area S_R of the sample S. When the distance between the laser source and the designated area S_R of the sample S is greater than the focal length F of the image acquisition device, the image L2 formed by the laser in the designated area S_R is the mirror image of the specific pattern G.

[0104] For example, the specific shape is a triangle. Referring to FIG4A , the imaged image L1 has the same shape as the specific shape G. Vertex a of the imaged image L1 and vertex A of the specific shape G are both on the right, and side BC of the imaged image L1 and side bc of the specific shape G are both on the left. Therefore, the imaged image L1 and the specific shape G are consistent. In FIG4B , vertex A of the specific shape G is on the right, and side BC is on the left. In contrast, vertex a of the imaged image L2 is on the left, and side bc is on the right. Therefore, the imaged image L2 is consistent with the mirrored specific shape G.

[0105] Within the range where the distance between the laser source and the designated area S_R of the sample 410 to be tested is greater than the focal length F of the image acquisition device, the smaller the distance between the laser source and the designated area S_R, the smaller the size of the image L2 formed by the laser in the designated area S_R. The larger the distance between the laser source and the designated area S_R, the larger the size of the image L1 formed by the laser in the designated area S_R. When the distance between the laser source and the designated area 411 of the sample 410 to be tested is equal to the focal length F of the image acquisition device, the image L2 is contracted to a single point.

[0106] In the disclosed embodiment, to distinguish whether the distance between the laser source and the designated area S_R of the sample S to be measured is greater than or less than the focal length F, the size of the imaged image L1 or imaged image L2 can be defined to distinguish. For example, when the imaged image L1 is consistent with the shape of the specific figure G, the distance from a vertex to the opposite side of the triangle is recorded as a positive value. When the imaged image L2 is the mirror image of the specific figure G, the distance from a vertex to the opposite side of the triangle is recorded as a negative value.

[0107] For example, the imaged image L1 shown in FIG4A is consistent with the shape of the specific figure G, and the distance from the vertex a to the side bc of the triangle abc is recorded as a positive value. The imaged image L2 shown in FIG4B is the shape of the specific figure G after mirroring, and the distance from the vertex a to the side bc of the triangle abc is recorded as a negative value. Therefore, when collecting the sample image, the distance from the vertex a to the side bc of the triangle abc is recorded based on the imaging relationship (consistent or mirrored) between the imaged image L1 (imaged image L2) and the specific image G. When it is determined that the recorded distance is a positive value, the distance between the laser source and the designated area S_R of the sample to be measured S is less than the focal length F. When it is determined that the recorded distance is a negative value, the distance between the laser source and the designated area S_R of the sample to be measured S is greater than the focal length F.

[0108] In an embodiment of the present disclosure, the above-mentioned operation S240 of determining the thickness of multiple parts of the sample to be tested based on the correspondence between the clarity of each of the multiple test areas in the multiple test images and the multiple test acquisition distances may include: determining multiple image sizes of the imaging images corresponding to the multiple test images; determining multiple target image sizes of the multiple test areas from the multiple image sizes, the target image size being the image size corresponding to the test image corresponding to the maximum clarity among the multiple clarity of each of the multiple test areas; determining multiple target acquisition distances corresponding to the multiple target image sizes based on the relationship between the image size and the multiple test acquisition distances; and determining the thickness difference between the multiple parts based on the difference between the multiple target acquisition distances corresponding to the multiple test areas.

[0109] For example, when acquiring multiple test images, the image sizes of the images corresponding to the multiple test acquisition distances are recorded. A target test image corresponding to the clearest image of each test area is determined from the multiple test images, and the image size corresponding to the target test image of each of the multiple test areas is determined as the target image size. Based on the correspondence between the recorded image sizes and the test acquisition distances, the target acquisition distance corresponding to each target image size is determined, thereby determining the target acquisition distance corresponding to each test area. At the target acquisition distance, the image acquisition device can capture the clearest image of the sample portion corresponding to the test area.

[0110] Based on the target acquisition distance corresponding to each test area, the thickness difference between the corresponding sample parts can be calculated. The calculation method is similar to the operation S240 described above and will not be repeated for simplicity.

[0111] In the disclosed embodiments, thickness measurements of different sample areas can be achieved using only a single industrial camera and laser source. Compared to devices that use multiple cameras and 3D structured light, the thickness determination device provided by the disclosed embodiments is lower in cost and simpler to operate.

[0112] The present disclosure provides an embodiment of dividing a reference image.

[0113] In some embodiments, operation S210 may include dividing the reference image into multiple image regions based on the image features of the reference image of the sample to be tested: binarizing the reference image using multiple first preset pixel thresholds to obtain multiple pre-processed images; and dividing the multiple pre-processed images based on the pixel value distribution of the multiple pre-processed images to obtain multiple image regions.

[0114] An embodiment of dividing a reference image is described with reference to Figures 5A, 5B, and 5C. Figure 5A is a schematic diagram of a binarized reference image according to an embodiment of the present disclosure, Figure 5B is a schematic diagram of dividing a pre-processed image according to an embodiment of the present disclosure, and Figure 5C is a schematic diagram of determining an image region according to an embodiment of the present disclosure.

[0115] As shown in FIG5A , a plurality of first preset pixel thresholds K1 , K2 , K3 , K4 and K5 are used to perform binarization processing on a grayscale image 510 of a reference image to obtain a plurality of pre-processed images 520 .

[0116] For example, grayscale processing is performed on the reference image to obtain a grayscale image 510. The grayscale image 510 can be used to represent the brightness values ​​of different regions in the reference image, and the reference image can be divided based on the brightness values.

[0117] For example, the multiple first preset pixel thresholds K1, K2, K3, K4, and K5 can be preset grayscale values ​​that vary in a gradient. For example, the multiple first preset pixel thresholds K1, K2, K3, K4, and K5 can be determined based on the grayscale value average or grayscale value median of the grayscale image 510. For example, when the grayscale value average of the grayscale image 510 is 200 and the gradient is 50, the multiple first preset pixel thresholds K1, K2, K3, K4, and K5 can be set to 50, 100, 150, 200, and 250, respectively. The present disclosure does not limit the number or specific values ​​of the multiple first preset pixel thresholds.

[0118] For example, the first preset pixel threshold K1 is 50. When the grayscale value of a pixel is greater than 50, the grayscale value of the pixel is recorded as 255. When the grayscale value of the pixel is less than or equal to 50, the grayscale value of the pixel is recorded as 0. After binarization, the grayscale values ​​of the preprocessed image only include 0 and 255, that is, the preprocessed image only includes white and black.

[0119] The plurality of pre-processed images 520 include a pre-processed image 521, a pre-processed image 522, a pre-processed image 523, a pre-processed image 524, and a pre-processed image 525. The pre-processed image 521 is obtained by binarizing the grayscale image 510 of the reference image using a plurality of first preset pixel thresholds K1. Accordingly, the pre-processed image 522 is obtained by binarizing the grayscale image 510 of the reference image using a plurality of first preset pixel thresholds K2. The pre-processed image 523 is obtained by binarizing the grayscale image 510 of the reference image using a plurality of first preset pixel thresholds K3. The pre-processed image 524 is obtained by binarizing the grayscale image 510 of the reference image using a plurality of first preset pixel thresholds K4. The pre-processed image 525 is obtained by binarizing the grayscale image 510 of the reference image using a plurality of first preset pixel thresholds K5.

[0120] The brightness distribution of the reference image can be represented to varying degrees by preprocessed image 521, preprocessed image 522, preprocessed image 523, preprocessed image 524, and preprocessed image 525. Preprocessed image 520 is segmented based on the distribution of black and white areas within preprocessed image 520. For example, edge detection methods can be used to detect the edges of black and white areas, and preprocessed image 520 can be segmented based on the edges of white and black areas.

[0121] As shown in FIG5B , the plurality of pre-processed images 520 are divided based on the distribution of black and white areas in the plurality of pre-processed images 520. The dotted line shown in FIG5B is a dividing line, and the areas on either side of the dividing line are different regions. Based on the dividing line, the pre-processed images are divided into a plurality of initial regions.

[0122] It should be noted that the black borders of pre-processed images 521, 522, 523, 524, and 525 shown in FIG5B are only for clearly indicating the boundaries of each pre-processed image. In actual pre-processed images, there are no black borders.

[0123] For example, pre-processed image 521 is divided into four initial regions using three dividing lines. Pre-processed image 522 is divided into three initial regions using two dividing lines. Pre-processed image 523 is divided into three initial regions using two dividing lines. Pre-processed image 524 is divided into four initial regions using three dividing lines. Pre-processed image 525 is divided into two initial regions using one dividing line.

[0124] Because the distribution of black and white areas in the multiple pre-processed images 520 varies, the division of each pre-processed image also varies. For example, the multiple pre-processed images 520 can be divided into multiple initial regions based on the edges of the black and white areas in each of the multiple pre-processed images 520. The segmentation lines in each of the multiple pre-processed images 520 are mapped to the grayscale image 510, thereby mapping the multiple initial regions in the multiple pre-processed images 520 to the grayscale image 510, thereby forming multiple image regions of the reference image.

[0125] In some embodiments, dividing the multiple preprocessed images into multiple image regions based on the pixel value distribution of the multiple preprocessed images may include: dividing the multiple preprocessed images 520 into multiple preprocessed regions respectively; for each preprocessed image in the multiple preprocessed images 520, replacing the pixel value in the corresponding preprocessed region with the average pixel value of each of the multiple preprocessed regions to obtain multiple initial images; binarizing the multiple initial images using a second preset pixel threshold to obtain multiple first images to be divided; and dividing the multiple first images to be divided based on the pixel value distribution of the multiple first images to be divided to obtain multiple image regions.

[0126] In the embodiment of the present disclosure, after the grayscale image 510 is binarized using the first preset pixel threshold, noise may exist in the pre-processed image 520. For example, for a pixel with a grayscale value of 0, if the grayscale values ​​of the pixels within a certain range surrounding the pixel are all 255, the pixel with a grayscale value of 0 is considered a noise point. Correspondingly, for a pixel with a grayscale value of 255, if the grayscale values ​​of the pixels within a certain range surrounding the pixel are all 0, the pixel with a grayscale value of 255 is considered a noise point.

[0127] In the embodiment of the present disclosure, the plurality of preprocessed images 520 are divided into a plurality of preprocessed regions, and the grayscale average value of each preprocessed region is used to replace the grayscale values ​​of all pixels in the preprocessed region, thereby removing noise in the preprocessed images.

[0128] For example, each preprocessed image is divided into 5×5 preprocessed regions, the average grayscale value G of each preprocessed region is calculated, and the average grayscale value G is used to replace the grayscale values ​​of all pixels in the corresponding preprocessed region to obtain multiple initial images after noise removal. This disclosure does not limit the number of divided preprocessed regions.

[0129] After noise removal, the initial image will contain pixels with grayscale values ​​other than 0 or 255. Therefore, the multiple initial images are binarized again using the second preset pixel threshold to obtain multiple first images to be segmented. Sub-regions with a high number of bright spots in the initial image are now set to completely white, while regions with a high number of dark spots are set to completely black. The second preset pixel threshold can be 127, and this disclosure does not limit the value of the second preset pixel threshold.

[0130] After the binarization process, the plurality of first images to be divided only include pixels with grayscale values ​​of 0 and 255. For example, an edge detection method is used to detect edges of black areas and white areas of the plurality of first images to be divided, and the plurality of first images to be divided are divided based on the edges of the white areas and the black areas.

[0131] In some embodiments, dividing the multiple preprocessed images according to the pixel value distribution of the multiple preprocessed images to obtain multiple image regions may include: dividing the multiple preprocessed images according to the pixel value distribution of the multiple preprocessed images to obtain multiple initial regions of the multiple preprocessed images; and determining multiple image regions from the multiple initial regions according to the distribution of the multiple initial regions of the multiple preprocessed images on the reference image.

[0132] Because the multiple pre-processed images 520 have different divisions, the segmentation lines of each of the multiple pre-processed images 520 are mapped to the reference image. The segmentation lines included in the reference image are relatively complex. For example, if the image range represented by the multiple pre-processed images 520 coincides with the image range of the reference image, the segmentation lines are mapped to the same positions in the grayscale image 510 of the reference image based on their positions in the multiple pre-processed images 520.

[0133] As shown in FIG5C , based on the position of segmentation line CC′ on pre-processed image 524, segmentation line CC′ is moved to the same position on grayscale image 510. Using a similar method, segmentation lines on pre-processed image 521, pre-processed image 522, pre-processed image 523, pre-processed image 524, and pre-processed image 525 are all moved to grayscale image 510.

[0134] The segmentation lines of the preprocessed images 521, 522, 523, 524, and 525 divide each into a plurality of initial regions. The segmentation lines of the preprocessed images 521, 522, 523, 524, and 525 are mapped to the grayscale image 510. The segmentation lines on the grayscale image 510 divide the grayscale image 510 into a plurality of image regions, thereby also achieving the mapping of the plurality of initial regions of the preprocessed images 521, 522, 523, 524, and 525 to the grayscale image 510.

[0135] Since the distribution density of the segmentation lines in some areas of the grayscale image 510 is relatively high, there is a problem of inaccurate segmentation when dividing the grayscale image 510 based on the segmentation lines in the grayscale image 510. Therefore, a minimum segmentation line spacing can be set to optimize the number of segmentation lines.

[0136] For example, a minimum dividing line spacing D is set. If multiple dividing lines exist within the spacing D, only the dividing lines on the two sides of the outermost edge within the spacing D are retained, and the remaining dividing lines are discarded. For example, according to the size of the grayscale image 510, the minimum dividing line spacing D is set. The grayscale image 510 is 20 cm long and 10 cm wide, and the minimum dividing line spacing D is set to 2 cm. When it is determined that there are dividing lines within any 2 cm range of the length range of the grayscale image 510, only the dividing lines on the two sides of the outermost edge within the 2 cm range are retained, and the remaining dividing lines are discarded. This ensures that the distance between any two dividing lines in the multiple dividing lines in the final grayscale image 510 is greater than or equal to 2 cm.

[0137] As shown in FIG5C , after optimizing the segmentation line in the grayscale image 510 , a plurality of image regions 511 , 512 , 513 , 514 , 515 , and 516 may be obtained.

[0138] The present disclosure provides another embodiment of dividing a reference image.

[0139] In some embodiments, operation S210 may include dividing the reference image into multiple image areas based on the image features of the reference image of the sample to be tested: dividing the reference image into multiple reference areas; binarizing the multiple reference areas based on the respective pixel average values ​​of the multiple reference areas to obtain a second image to be divided; and dividing the second image to be divided into multiple image areas based on the pixel value distribution of the second image to be divided.

[0140] In the embodiment of the present disclosure, the brightness distribution of the reference image may be relatively uniform, and the brightness difference between different areas is relatively small. Therefore, the reference image can be divided according to the line density reflected in the reference image.

[0141] For example, grayscale processing is performed on the reference image to obtain a grayscale image, which is then binarized to obtain the second image to be segmented. Edge detection methods are used to detect edges in the black and white regions of the second image to be segmented. More edges are detected in areas with dense circuits in the second image to be segmented. Fewer edges are detected in areas with no circuits or with fewer circuits. Thus, the grayscale image can be segmented based on the number of detected edges.

[0142] For example, a grayscale image is divided into multiple (n×m) reference regions, and the size of the reference regions is set according to the characteristics of the reference image. The present disclosure does not limit the number and area size of the reference regions. The average grayscale value of all pixels in each reference region is calculated. When the grayscale value of a pixel is greater than the grayscale average value of the corresponding reference region, the grayscale value of the pixel is set to 255. When the grayscale value of a pixel is less than or equal to the grayscale average value of the corresponding reference region, the grayscale value of the pixel is set to 0.

[0143] For example, a deviation value can be set as needed. When the grayscale values ​​of the pixels within the reference area are generally too large or too small, the accuracy of binarization using the average grayscale value is low. Therefore, the sum of the deviation value and the average grayscale value is used to binarize the pixel grayscale values.

[0144] For example, when the grayscale values ​​of the pixels in the reference area are mostly close to 255, the calculated average grayscale value is 240. To avoid setting the pixels close to 255 to 0, the deviation value can be set to 100, thereby binarizing the grayscale values ​​of the pixels in the reference area based on 240-100=140.

[0145] Accordingly, when the grayscale values ​​of the pixels in the reference area are mostly close to 0, the calculated average grayscale value is 20. To avoid setting the grayscale values ​​of the pixels close to 0 to 25, the deviation value can be set to 100, thereby binarizing the grayscale values ​​of the pixels in the reference area based on 20+100=120. The present disclosure does not limit the number of deviation values.

[0146] In the embodiment of the present disclosure, the reference image can represent the features of the surface of the sample to be tested. The reference image can be divided using the image features of the reference image, and the sample to be tested can be divided according to the features of the surface of the sample to be tested.

[0147] In the embodiment of the present disclosure, the reference image may also be segmented based on image features such as contrast and saturation.

[0148] FIG6 is a schematic diagram of determining thickness according to an embodiment of the present disclosure.

[0149] As shown in FIG6 , the sample to be tested 610 may include a first portion 611, a second portion 612, and a third portion 613. It should be noted that the sample to be tested 610 shown in FIG6 is shown at a cross-sectional angle. The first portion 611, the second portion 612, and the third portion 613 have different thicknesses. The image acquisition device can capture the topographical features of the surfaces of the first portion 611, the second portion 612, and the third portion 613. For example, when the image acquisition device is located directly above the sample to be tested, the image acquisition device can capture the topographical features of the upper surfaces of the first sub-portion 611, the second sub-portion 612, and the third sub-portion 613.

[0150] For example, the upper surface area corresponding to the second portion 612 may be a designated area of ​​the sample to be tested 610 .

[0151] When the image acquisition device is located at the horizontal plane of line H, the distance between the image acquisition device and the upper surface area of ​​the second portion 612 (the second sample area) is equal to the image acquisition device's focal length F. At this point, the image acquisition device is in focus on the second sample area, and the image acquisition device can capture a clear image of the second sample area. When the image acquisition device moves to the horizontal plane of line H1, the distance between the image acquisition device and the upper surface area of ​​the first portion 611 (the first sample area) is equal to the image acquisition device's focal length F. At this point, the image acquisition device is in focus on the first sample area, and the image acquisition device captures a clear image of the first sample area. When the image acquisition device moves to the horizontal plane of line H2, the distance between the image acquisition device and the upper surface area of ​​the third portion 613 (the third sample area) is equal to the image acquisition device's focal length F. At this point, the image acquisition device is in focus on the third sample area, and the image acquisition device captures a clear image of the third sample area.

[0152] Determining the thickness of multiple portions of the sample to be tested based on the correspondence between the multiple sharpness values ​​of each of the multiple test areas and the multiple test acquisition distances. For example, operation S240 may include: determining, for each of the multiple test areas, multiple contrast values ​​of the test area in the multiple test images; determining, from the multiple test acquisition distances, a target acquisition distance corresponding to each of the multiple test areas, where the target acquisition distance is the test acquisition distance corresponding to the maximum contrast value among the multiple contrast values ​​of the test area; and determining thickness differences between the multiple portions based on differences between the multiple target acquisition distances corresponding to the multiple test areas.

[0153] In the disclosed embodiment, a contrast value is calculated for each test area in each test image. The contrast value can represent the clarity of the test area in the test image. For multiple test images, the division of the test area in each test image is the same. Therefore, each test area has multiple contrast values.

[0154] For example, each test image is divided into three test areas, which correspond to the first sample area, the second sample area, and the third sample area, respectively. For example, when 10 test images are collected, the test area corresponding to the first sample area has 10 contrast values, the test area corresponding to the second sample area has 10 contrast values, and the test area corresponding to the third sample area has 10 contrast values.

[0155] Since each test image corresponds to a test acquisition distance, each contrast value in the test area corresponds to a test acquisition distance. This yields a curve showing the contrast value of each test area and the corresponding test acquisition distance. In the curve, the contrast value of each test area changes as the test acquisition distance changes. When the contrast value reaches its peak, the test acquisition distance corresponding to that contrast value is the target acquisition distance, and the image acquisition device is in focus on the test area at this time. The peak contrast value in the curve represents the maximum contrast value among the multiple contrast values ​​for that test area.

[0156] In an embodiment of the present disclosure, the maximum contrast value of the test area corresponds to the target test image of the test area. When the image acquisition device acquires the target test image, the test distance between the image acquisition device and the corresponding sample area is equal to the focal length.

[0157] For example, when the image acquisition device moves to the horizontal plane of line H1, the distance between the image acquisition device and the first sample area is equal to the focal length F of the image acquisition device. In this case, the target acquisition distance is D1. When the target acquisition distance D1 is less than the focal length F, the thickness of the first portion 611 is considered to be less than the thickness of the second portion 612. The difference between the target acquisition distance D1 and the focal length F can be used to determine the thickness difference between the first portion 611 and the second portion 612, where the focal length F is the target acquisition distance corresponding to the second sample area.

[0158] For example, when the image acquisition device moves to the horizontal plane of line H2, the distance between the image acquisition device and the third sample area is equal to the focal length F of the image acquisition device. In this case, the target acquisition distance is D2. When the target acquisition distance D2 is greater than the focal length F, the thickness of the third portion 613 is considered to be greater than the thickness of the second portion 612. The difference between the target acquisition distance D2 and the focal length F can be used to determine the thickness difference between the third portion 613 and the second portion 612.

[0159] Accordingly, the thickness difference between the sample area 613 and the first portion 611 can be determined according to the difference between the target collection distance D2 and the target collection distance D1.

[0160] In some embodiments, operation S240 determines the thickness of multiple parts of the sample to be tested based on the correspondence between the clarity of multiple test areas in multiple test images and multiple test acquisition distances, and can also include: determining the baseline thickness value of the specified part corresponding to the specified area in the sample to be tested and the baseline acquisition distance corresponding to the baseline image; determining the thickness value of each of the multiple parts based on the target acquisition distance, baseline thickness value and baseline acquisition distance corresponding to each of the multiple test areas.

[0161] For example, the reference acquisition distance corresponding to the reference image is the target acquisition distance corresponding to the designated area. For example, the target acquisition distance corresponding to the second sample area is the reference acquisition distance, ie, the focal length F.

[0162] When determining the thickness value T of the second portion 612, the thickness of the first portion 611 can be determined as T-(F-D1) based on the thickness difference between the first portion 611 and the second portion 612. The thickness of the third portion 613 can be determined as T+(D2-F) based on the thickness difference between the third portion 613 and the second portion 612.

[0163] In some embodiments, after determining the target test image corresponding to each test area, a target image of the sample to be tested is generated using the multiple target test images. In the target image, the image of each test area is the clearest.

[0164] For example, target area images of each of the multiple test areas are obtained from multiple test images, where the target area images are area images corresponding to the maximum clarity among the multiple clarity levels of each of the multiple test areas; and based on the positional relationship between the multiple test areas, the target area images of each of the multiple test areas are combined into a target image of the sample to be tested.

[0165] In the disclosed embodiment, a sub-image corresponding to the test area is segmented from a target test image of the test area as the target area image. For example, a target test image with the clearest image of the test area is determined from multiple test images, and the sub-image of the test area is segmented from the target test image.

[0166] The target image is a combination of the target area images of each test area according to the arrangement of the test areas in the test image. In the target image, the image corresponding to each sample area is collected in a focused state.

[0167] For example, the target test images corresponding to the first, second, and third sample regions are segmented into their respective target region images. The target region images of the first, second, and third regions 611, 612, and 613 are arranged according to the positions of the first, second, and third regions 613 in the sample to be tested 610 to obtain a target image. The target image can clearly represent the image content of the first, second, and third sample regions.

[0168] In the disclosed embodiments, the dimensional parameters of the sample under test can be measured within the target image. Focused target area images are acquired for different parts of the sample under test. Multiple target area images are integrated to produce a clear image of the uneven surface of the sample under test. This allows for precise measurement of multiple dimensional parameters of the sample under test within the target image. For example, the distance between a cutting line and a standard line can be accurately measured.

[0169] By measuring the distance at which each sample area is in focus, the image acquisition device can determine the thickness difference between different sample regions. This can be used to measure sample thickness in production processes. For example, measuring the thickness of a sample's barrier zone can be challenging. A barrier that is too thick can easily crack, while a barrier that is too thin can't prevent water and oxygen from entering. Furthermore, by determining the thickness difference between sample regions, a 3D image of the sample can be constructed, enabling 3D image creation within an industrial camera solution.

[0170] FIG. 7 is a schematic diagram of a sample testing device according to an embodiment of the present disclosure.

[0171] As shown in FIG. 7 , the sample testing device 700 may include a dividing module 710 , a first acquiring module 720 , a first determining module 730 , and a second determining module 740 .

[0172] The segmentation module 710 is configured to segment the reference image of the sample under test into multiple image regions based on image features of the reference image. The reference image is an image of the sample under test captured by an image acquisition device at a reference acquisition angle and a reference acquisition distance. The reference acquisition distance represents the distance between the image acquisition device and a designated region of the sample under test, which is equal to the focal length of the image acquisition device. In one embodiment, the segmentation module 710 can be used to perform operation S210 described above and will not be further described here.

[0173] The first acquisition module 720 is configured to acquire multiple test images of the sample to be tested. The multiple test images are images captured by the image acquisition device at multiple test acquisition distances and reference acquisition angles, where the multiple test acquisition distances are different from each other. In one embodiment, the first acquisition module 720 can be used to perform operation S220 described above, and will not be further described here.

[0174] The first determination module 730 is configured to divide each of the multiple test images into multiple test regions based on the ratio of the multiple image regions in the reference image, where the multiple test regions of each test image correspond to the multiple image regions. In one embodiment, the first determination module 730 can be used to perform operation S230 described above, and will not be further described here.

[0175] The second determination module 740 is configured to determine the thickness of multiple locations of the sample to be tested based on the correspondence between the clarity of each of the multiple test areas in the multiple test images and the multiple test acquisition distances. In one embodiment, the second determination module 740 can be used to perform operation S240 described above, which will not be repeated here.

[0176] In the embodiment of the present disclosure, the division module 710 includes: a first processing unit, used to binarize the reference image using multiple first preset pixel thresholds to obtain multiple pre-processed images; a first division unit, used to divide the multiple pre-processed images according to the pixel value distribution of the multiple pre-processed images to obtain multiple image areas.

[0177] In an embodiment of the present disclosure, the first division unit is used to: divide a plurality of preprocessed images into a plurality of preprocessed regions; for each of the plurality of preprocessed images, replace the pixel value in the corresponding preprocessed region with the average pixel value of each of the plurality of preprocessed regions to obtain a plurality of initial images; binarize the plurality of initial images using a second preset pixel threshold to obtain a plurality of first images to be divided; and divide the plurality of first images to be divided according to the pixel value distribution of the plurality of first images to be divided to obtain a plurality of image regions.

[0178] In an embodiment of the present disclosure, the first division unit is used to: divide the multiple preprocessed images according to the pixel value distribution of the multiple preprocessed images to obtain multiple initial regions of the multiple preprocessed images; and determine multiple image regions from the multiple initial regions according to the distribution of the multiple initial regions of the multiple preprocessed images on the reference image.

[0179] In the embodiment of the present disclosure, the division module 710 includes: a second division unit, used to divide the reference image into multiple reference areas; a second processing unit, used to binarize the multiple reference areas according to the respective pixel average values ​​of the multiple reference areas to obtain a second image to be divided; and a third division unit, used to divide the second image to be divided into multiple image areas according to the pixel value distribution of the second image to be divided.

[0180] In an embodiment of the present disclosure, the second determination module 730 includes: a first determination unit, used to determine, for each of the multiple test areas, multiple contrast values ​​of the test area in the multiple test images; a second determination unit, used to determine, from the multiple test acquisition distances, the target acquisition distance corresponding to each of the multiple test areas, where the target acquisition distance is the test acquisition distance corresponding to the maximum contrast value among the multiple contrast values ​​of the test area; and a third determination unit, used to determine the thickness difference between the multiple parts based on the difference between the multiple target acquisition distances corresponding to the multiple test areas.

[0181] In the embodiment of the present disclosure, the sample testing device 700 also includes: a second acquisition module, used to obtain target area images of each of the multiple test areas from the multiple test images, where the target area images are area images corresponding to the maximum clarity among the multiple clarity of each of the multiple test areas; and a combination module, used to combine the target area images of each of the multiple test areas into a target image of the sample to be tested based on the positional relationship between the multiple test areas.

[0182] In the embodiment of the present disclosure, the sample testing device 700 further includes: a measuring module, configured to measure the size parameters of the sample to be tested in the target image.

[0183] In the embodiment of the present disclosure, the sample testing device 700 further includes: a setting module for setting an imaging image of the laser emitted by the laser source on the surface of the sample to be tested, wherein the image size of the imaging image represents the distance between the image acquisition device and the specified area; and a third acquisition module for acquiring a reference image of the sample to be tested from the image acquisition device at a reference acquisition angle, upon determining that the imaging image is shrunk to a point on the specified area.

[0184] In the embodiment of the present disclosure, the second determination module 740 includes: a fourth determination unit, used to determine multiple image sizes of imaging images corresponding to multiple test images; a fifth determination unit, used to determine multiple target image sizes of multiple test areas from the multiple image sizes, the target image size being the image size corresponding to the test image corresponding to the maximum clarity among the multiple clarity of each of the multiple test areas; a sixth determination unit, used to determine multiple target acquisition distances corresponding to multiple target image sizes based on the relationship between the image size and the multiple test acquisition distances; and a seventh determination unit, used to determine the thickness difference between multiple parts based on the difference between the multiple target acquisition distances corresponding to the multiple test areas.

[0185] In the embodiment of the present disclosure, the second determination module 740 also includes: an eighth determination unit, used to determine the baseline thickness value of the specified part corresponding to the specified area in the sample to be tested and the baseline acquisition distance corresponding to the baseline image; a ninth determination unit, used to determine the thickness values ​​of multiple parts based on the target acquisition distances, baseline thickness values ​​and baseline acquisition distances corresponding to each of the multiple test areas.

[0186] In some embodiments, the present disclosure also provides a readable storage medium.

[0187] In embodiments of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided. This computer-readable storage medium may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0188] In the embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, and the computer-readable storage medium may be a tangible medium that may contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. The computer-readable storage medium may be a computer-readable signal medium or a machine-readable storage medium. The computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. More specific examples of computer-readable storage media may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0189] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0190] Those skilled in the art will appreciate that various combinations and / or combinations of features described in the various embodiments and / or claims of this disclosure may be made, even if such combinations or combinations are not explicitly described in this disclosure. In particular, various combinations and / or combinations of features described in the various embodiments and / or claims of this disclosure may be made, without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0191] The embodiments of the present disclosure are described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for determining thickness, comprising: dividing the reference image of the sample to be measured into a plurality of image regions according to the image features of the reference image, where the reference image is an image of the sample to be measured collected by an image acquisition device at a reference acquisition angle and a reference acquisition distance, and the reference acquisition distance represents that the distance between the image acquisition device and a specified region in the sample to be measured is the focal length of the image acquisition device; acquiring a plurality of test images of the sample to be measured, where the plurality of test images are images collected by the image acquisition device at a plurality of test acquisition distances and the reference acquisition angle, and the plurality of test acquisition distances are not equal to each other; dividing each of the plurality of test images into a plurality of test regions based on the proportion of the plurality of image regions in the reference image, and the plurality of test regions of each test image respectively correspond to the plurality of image regions; and determining the thicknesses of a plurality of parts of the sample to be measured according to the corresponding relationship between the sharpness of the plurality of test regions in the plurality of test images and the plurality of test acquisition distances.

2. The method according to claim 1, wherein the step of dividing the reference image into a plurality of image regions according to the image features of the reference image of the sample to be measured includes: performing binarization processing on the reference image by using a plurality of first preset pixel thresholds to obtain a plurality of preprocessed images; and dividing the plurality of preprocessed images according to the pixel value distribution of the plurality of preprocessed images to obtain the plurality of image regions.

3. The method according to claim 2, wherein the step of dividing the plurality of preprocessed images into the plurality of image regions according to the pixel value distribution of the plurality of preprocessed images includes: dividing each of the plurality of preprocessed images into a plurality of preprocessed regions; for each of the plurality of preprocessed images, replacing the pixel values in the corresponding preprocessed regions with the average pixel values of the plurality of preprocessed regions respectively to obtain a plurality of initial images; performing binarization on the plurality of initial images by using a second preset pixel threshold to obtain a plurality of first images to be divided; and dividing the plurality of first images to be divided according to the pixel value distribution of the plurality of first images to be divided, to obtain the plurality of image regions.

4. The method according to claim 2, wherein the step of dividing the plurality of preprocessed images according to the pixel value distribution of the plurality of preprocessed images to obtain the plurality of image regions includes: dividing the plurality of preprocessed images according to the pixel value distribution of the plurality of preprocessed images to obtain a plurality of initial regions of each of the plurality of preprocessed images; and determining the plurality of image regions from the plurality of initial regions according to the distribution of the plurality of initial regions of each of the plurality of preprocessed images on the reference image.

5. The method according to claim 1, wherein the step of dividing the reference image into a plurality of image regions according to the image features of the reference image of the sample to be measured includes: dividing the reference image into a plurality of reference regions; Binarize the multiple reference regions according to the respective pixel average values of the multiple reference regions to obtain a second image to be partitioned; and Partition the second image to be partitioned into the multiple image regions according to the pixel value distribution of the second image to be partitioned.

6. The method according to claim 1, wherein, the determining the thicknesses of multiple parts of the sample to be measured according to the corresponding relationship between the respective sharpnesses of the multiple test regions in the multiple test images and the multiple test acquisition distances includes: For each of the multiple test regions, determining multiple contrast values of the test region in the multiple test images; Determining the respective target acquisition distances corresponding to the multiple test regions from the multiple test acquisition distances, where the target acquisition distance is the test acquisition distance corresponding to the maximum contrast value among the multiple contrast values of the test region; and Determining the thickness differences between the multiple parts according to the differences between the multiple target acquisition distances corresponding to the multiple test regions.

7. The method according to claim 1, further including: Obtaining the respective target region images of the multiple test regions from the multiple test images, where the target region image is the region image corresponding to the maximum sharpness among the multiple sharpnesses of each of the multiple test regions; and Combining the respective target region images of the multiple test regions into a target image of the sample to be measured according to the positional relationship between the multiple test regions.

8. The method according to claim 7, further including: Measuring the size parameters of the sample to be measured in the target image.

9. The method according to claim 1, further including: Setting an imaging image of the laser emitted by the laser source on the surface of the sample to be measured, where the image size of the imaging image represents the distance between the image acquisition device and the specified region; and At the reference acquisition angle, when it is determined that the imaging image shrinks to a point on the specified region, obtaining the reference image of the sample to be measured from the image acquisition device.

10. The method according to claim 9, wherein, the determining the thicknesses of multiple parts of the sample to be measured according to the corresponding relationship between the respective sharpnesses of the multiple test regions in the multiple test images and the multiple test acquisition distances includes: Determining multiple image sizes of the imaging images corresponding to the multiple test images; Determining multiple target image sizes of the multiple test regions from the multiple image sizes, where the target image size is the image size corresponding to the test image corresponding to the maximum sharpness among the multiple sharpnesses of each of the multiple test regions; Determining multiple target acquisition distances corresponding to the multiple target image sizes according to the relationship between the image size and the multiple test acquisition distances; and Determining the thickness differences between the multiple parts according to the differences between the multiple target acquisition distances corresponding to the multiple test regions.

11. The method according to claim 6 or 10, wherein, ​ Determining the thicknesses of multiple parts of the sample to be measured according to the corresponding relationship between the clarity of each of the multiple test regions in the multiple test images and the multiple test acquisition distances further includes: Determining a reference thickness value of a specified part corresponding to the specified region in the sample to be measured and a reference acquisition distance corresponding to the reference image; Determining the thickness value of each of the multiple parts according to the target acquisition distance corresponding to each of the multiple test regions, the reference thickness value, and the reference acquisition distance.

12. A thickness determination device, comprising: A division module, configured to divide the reference image into multiple image regions according to the image features of the reference image of the sample to be measured, where the reference image is an image of the sample to be measured acquired by an image acquisition device at a reference acquisition angle and a reference acquisition distance, and the reference acquisition distance represents that the distance between the image acquisition device and a specified region in the sample to be measured is the focal length of the image acquisition device; A first acquisition module, configured to acquire multiple test images of the sample to be measured, where the multiple test images are images acquired by the image acquisition device at multiple test acquisition distances and the reference acquisition angle, and the multiple test acquisition distances are not equal to each other; A first determination module, configured to divide each of the multiple test images into multiple test regions based on the proportion of the multiple image regions in the reference image, and the multiple test regions of each test image respectively correspond to the multiple image regions; and A second determination module, configured to determine the thicknesses of multiple parts of the sample to be measured according to the corresponding relationship between the clarity of each of the multiple test regions in the multiple test images and the multiple test acquisition distances.

13. The device according to claim 12, wherein, The second determination module includes: A first determination unit, configured to determine multiple contrast values of the test region in the multiple test images for each of the multiple test regions; A second determination unit, configured to determine the target acquisition distance corresponding to each of the multiple test regions from the multiple test acquisition distances, where the target acquisition distance is the test acquisition distance corresponding to the maximum contrast value among the multiple contrast values of the test region; and A third determination unit, configured to determine the thickness difference between the multiple parts according to the difference between the multiple target acquisition distances corresponding to the multiple test regions.

14. The device according to claim 12, further comprising: A second acquisition module, configured to acquire the target region image of each of the multiple test regions from the multiple test images, where the target region image is the region image corresponding to the maximum clarity among the multiple clarities of each of the multiple test regions; and A combination module, configured to combine the target region images of each of the multiple test regions into a target image of the sample to be measured according to the positional relationship between the multiple test regions.

15. A thickness determination device, comprising: A laser source, configured to emit laser light towards a sample to be measured; An image acquisition device configured to capture a reference image and a plurality of test images of a sample to be measured based on an imaging image of the laser on the sample to be measured; A processor configured to control a distance between the image acquisition device and a specified area in the sample to be measured; And A memory communicatively connected to the processor and configured to store instructions executable by the processor, the instructions being executed by the processor to enable the processor to execute the method according to any one of claims 1-11.

16. The apparatus according to claim 15, Wherein, The position of the laser source is relatively fixed with respect to the position of the image acquisition device.

17. A non-transitory computer-readable storage medium storing computer instructions, Wherein, The computer instructions are for causing the computer to execute the method according to any one of claims 1-11.

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