Detection method, processor and detection device
By judging and stitching together local images of the wafer to generate a complete image, the problem of low wafer inspection efficiency in existing technologies is solved, and efficient defect detection is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SKYVERSE TECH CO LTD
- Filing Date
- 2021-09-14
- Publication Date
- 2026-05-12
AI Technical Summary
In existing technologies, wafer defect detection requires two separate scans for each defect and the overall image, resulting in low detection efficiency.
A detection method is provided that obtains detection information of the area to be inspected by judging and processing local images, and stitches multiple images to be processed to generate a complete image of the wafer, thereby reducing repeated shooting.
This improves the efficiency of wafer inspection, reduces the number of times wafers need to be photographed repeatedly, and enables the rapid acquisition of complete images and inspection of the area to be inspected.
Smart Images
Figure CN115809975B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of industrial testing technology, and in particular to a testing method, processor and testing equipment. Background Technology
[0002] When inspecting wafers for defects, depending on the requirements, sometimes it's necessary to view images of each defect, and sometimes an overall image of the wafer is needed. Due to the limitations of the camera's field of view, no defect inspection equipment can capture an entire image of the wafer in a single shot. Therefore, to obtain an image of each defect, it's usually necessary to photograph each defect individually. Furthermore, to obtain an overall image of the wafer, it's typically necessary to scan each area of the wafer separately, and then stitch the scans together. Thus, in actual inspection, obtaining images of each defect and the overall wafer image requires at least two separate scans of the entire wafer area, resulting in low inspection efficiency. Summary of the Invention
[0003] This application provides a detection method, a processor, and a detection device.
[0004] The detection method of this application is used to detect a sample, the sample comprising multiple local regions, and the detection method includes:
[0005] Provide a local image, which is an image of a local region of the sample;
[0006] The image acquisition operation is performed on each local region of the sample to generate an image to be processed; the image acquisition operation includes:
[0007] The local image is used as the image to be processed;
[0008] The local image is processed to determine whether it includes the area of the sample to be inspected; and
[0009] If the result of the initial judgment processing is an image in which the local image includes the area to be inspected of the sample, the area to be inspected is identified and the detection information of the target to be inspected in the area to be inspected is obtained.
[0010] After the image acquisition operation, multiple images to be processed are stitched together to generate a complete image of the sample.
[0011] In some implementations, before stitching together multiple images to be processed to generate a complete image of the sample, the image acquisition operation further includes: performing local association processing on the detection information and local images, so that the image region of the target to be detected in the local image corresponds to the detection information of the target to be detected;
[0012] Alternatively, after stitching together multiple images to be processed to generate a complete image of the sample, the detection method further includes: performing overall association processing between the detection information and the overall image, so that the image region of the target to be detected in the overall image corresponds to the detection information of the target to be detected.
[0013] In some embodiments, the partial images of the region to be inspected of the sample are multiple; the second and subsequent image acquisition operations further include:
[0014] Determine whether the image to be processed formed by the current image acquisition operation contains the region to be inspected obtained in the previous image acquisition operation steps;
[0015] If so, the same region as the region to be inspected in the previous image acquisition operation is taken as the common region to be inspected; the detection information of the common region to be inspected is used to perform the local association processing on the local image of the current image acquisition operation; and
[0016] If not, perform the identification process on the detection area.
[0017] In some embodiments, the local association processing includes: obtaining an image region of the target to be inspected in the local image based on the location information of the target to be inspected; and annotating the detection information in the image region of the target to be inspected in the local image.
[0018] The overall association processing includes: obtaining the image region of the target to be inspected in the overall image based on the location information of the target to be inspected; and marking the detection information in the image region of the target to be inspected in the overall image.
[0019] In some embodiments, the detection information includes one or a combination of the presence or absence of the target to be inspected, the location of the target to be inspected, and the geometric parameters of the target to be inspected; the target to be inspected includes a defect, a thin film, or a hole.
[0020] In some embodiments, the step of performing a judgment process on the local image to determine whether the local image includes an image of the region to be inspected of the sample includes:
[0021] Based on the position and field of view of the image acquisition device when acquiring the local image, the indication range of the local image is determined, and the local image is an image formed based on the indication range of the sample;
[0022] Determine whether the indicated range and the location of the area to be inspected on the sample overlap;
[0023] If so, determine that the local image includes the area to be inspected of the sample; and
[0024] If not, determine that the local image does not include the area to be inspected of the sample.
[0025] In some implementations, stitching together multiple images to generate a complete image of the sample includes:
[0026] When at least two images to be processed include images of the same region of the sample, one of the at least two images to be processed that contain images of the same region is selectively retained to form an image to be stitched together;
[0027] When only one image to be processed includes an image of a region of the sample, the image of that region is retained to form the image to be stitched together; and
[0028] All images of the sample to be stitched together are combined to generate a complete image of the sample.
[0029] In some embodiments, the detection method further includes:
[0030] Place the sample in the support device;
[0031] The relative positional relationship between the image acquisition device and the supporting device is obtained when acquiring local images; and an acquisition path is generated based on the multiple relative positional relationships corresponding to the acquisition of multiple local images.
[0032] Providing a local image includes: the image acquisition device and the carrier moving relative to each other along the acquisition path; and the image acquisition device acquiring the local image.
[0033] The processor in this embodiment is used to process an image of a sample, the sample comprising multiple local regions, and the processor includes:
[0034] The input module is used to provide a local image, which is an image of a local region of the sample;
[0035] The image acquisition module is used to perform image acquisition operations on each local region of the sample to generate an image to be processed; the image acquisition module includes:
[0036] A setting unit is used to set the local image as the image to be processed;
[0037] A judgment unit is used to perform judgment processing on the local image to determine whether the local image includes an image of the region to be inspected of the sample; and
[0038] The identification unit is used to identify the region to be inspected and obtain the detection information of the target in the region to be inspected when the result of the initial judgment processing is an image of the region to be inspected that includes the sample in the local image.
[0039] The stitching module is used to stitch together multiple images to be processed to generate a complete image of the sample.
[0040] In some implementations, the processor further includes an association module;
[0041] The association module includes a local association module, which is used to associate the detection information with local images before stitching together multiple images to generate a complete image of the sample, so that the image region of the target to be detected in the local image corresponds to the detection information of the target to be detected.
[0042] Alternatively, the association module may include an overall association module, which is used to associate the detection information with the overall image after stitching together multiple images to be processed to generate a complete image of the sample, so that the image region of the target to be detected in the overall image corresponds to the detection information of the target to be detected.
[0043] In some implementations, the partial images including the image of the area to be inspected of the sample are multiple;
[0044] The judgment unit is further configured to: determine whether the image to be processed formed by the current image acquisition operation contains the area to be inspected acquired in the previous image acquisition operation steps;
[0045] The image acquisition module further includes a processing unit, which is configured to: if so, take the same area as the area to be inspected in the previous image acquisition operation as the common area to be inspected in the current image acquisition operation; and perform the local association processing on the detection information of the common area to be inspected and the local image of the current image acquisition operation.
[0046] The identification unit is further configured to: if not, perform the identification process on the detection area.
[0047] In some embodiments, the local association module is specifically used to: obtain the image region of the target to be inspected in the local image based on the location information of the target to be inspected; and mark the detection information in the image region of the target to be inspected in the local image;
[0048] The overall association module is specifically used to: obtain the image region of the target to be inspected in the overall image based on the location information of the target to be inspected; and mark the detection information in the image region of the target to be inspected in the overall image.
[0049] In some embodiments, the determining unit includes:
[0050] A determining subunit is used to determine the indication range of the local image based on the position and field of view of the image acquisition device when acquiring the local image, wherein the local image is an image formed based on the indication range of the sample;
[0051] The judgment subunit is used to determine whether the position of the indicated range and the area to be inspected on the sample intersects; if so, it determines that the local image includes the image of the area to be inspected of the sample; and if not, it determines that the local image does not include the image of the area to be inspected of the sample.
[0052] In some embodiments, the splicing module includes:
[0053] The first retention unit, when at least two images to be processed include images of the same region of the sample, selectively retains one of the at least two images to be processed that represent the same region to form an image to be stitched together;
[0054] The second retention unit retains the image of a region of the sample when only one image to be processed includes that region, forming the image to be stitched together; and
[0055] The stitching unit stitches together all the images of the sample to be stitched together to generate a complete image of the sample.
[0056] The detection device according to the embodiments of this application is used to detect a sample, the sample including multiple local regions, the detection device including: an image acquisition device for acquiring local images of each local region of the sample; and a processor according to any embodiment of this application.
[0057] In some embodiments, the sample is placed on a support device, and the processor further includes an acquisition module for acquiring the relative positional relationship between the image acquisition device and the support device when acquiring local images; and generating an acquisition path based on the multiple relative positional relationships corresponding to the acquisition of multiple local images; the input module is specifically used for: relative movement between the image acquisition device and the support device along the acquisition path; and acquisition of the local image by the image acquisition device.
[0058] In the detection method, processor, and detection device of this application, after providing a local image, a processing image acquisition operation is performed on the local image to generate a processing image. When there is no area to be detected in the local image, the local image is directly used as the processing image. When there is an area to be detected in the local image, the detection information of the target to be detected is acquired. A processing image is generated for each local area of the sample, and the multiple processing images are then stitched together to generate a complete image of the sample. Therefore, by taking a picture of each local area of the sample only once, the purpose of detecting the area to be detected and obtaining a complete image of the sample can be achieved without repeatedly taking pictures of the sample, thus improving the efficiency of sample detection.
[0059] Additional aspects and advantages of the embodiments of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description
[0060] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein:
[0061] Figure 1 This is a schematic diagram of the structure of the detection device according to some embodiments of this application;
[0062] Figure 2 This is a schematic diagram of the structure of a sample according to certain embodiments of this application;
[0063] Figure 3 and Figure 4 This is a flowchart illustrating the detection method of some embodiments of this application;
[0064] Figure 5 and Figure 6 This is a schematic diagram illustrating the implementation principle of the detection method in some embodiments of this application;
[0065] Figure 7 and Figure 8 This is a flowchart illustrating the detection method of some embodiments of this application;
[0066] Figure 9 This is a schematic diagram illustrating the implementation principle of the detection method in some embodiments of this application;
[0067] Figures 10 to 12 This is a flowchart illustrating the detection method of some embodiments of this application;
[0068] Figure 13 This is a schematic diagram illustrating the implementation principle of the detection method in some embodiments of this application;
[0069] Figures 14 to 16This is a flowchart illustrating the detection method of certain embodiments of this application; and
[0070] Figures 17 to 22 This is a schematic diagram of a processor module according to certain embodiments of this application. Detailed Implementation
[0071] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the embodiments of this application, and should not be construed as limiting the embodiments of this application.
[0072] Please see Figure 1 , Figure 1 This is a schematic diagram of the structure of a detection device 100 according to certain embodiments of the present application. The detection device 100 can be used to detect sample A. The detection device 100 includes an image acquisition device 10 and a processor 20.
[0073] Specifically, the sample A to be inspected can be a wafer, chip, substrate, thin film, display panel, casing of electronic device, cover plate of electronic device, electronic component of electronic device, or other workpieces or semi-finished products thereof. This application specification uses a wafer as an example of the sample A to be inspected; however, it is understood that the specific type of sample A can also be other, and no limitation is made here. Please refer to... Figure 2 The example shown, Figure 2 This is a schematic diagram of the structure of sample A in some embodiments of this application. Taking sample A as an example, sample A includes multiple chip units (dies), and it is necessary to detect the chip units in a specified area.
[0074] The testing equipment 100 can be an industrial testing machine. It can be used to detect parameters such as the position and size of the target to be inspected on sample A, or to detect whether the target to be inspected on sample A has defects, in order to determine whether sample A meets quality requirements. In one example, after any one of the multiple processes of sample A is completed, the testing equipment 100 is used to check whether sample A is qualified, thereby improving the final product yield of sample A and reducing production costs.
[0075] Please continue reading. Figure 1The image acquisition device 10 is used to receive the light signal reflected or scattered by sample A and generate an image of sample A based on the light signal. The image acquisition device 10 can be a two-dimensional camera or a three-dimensional camera, or it can be equipped with both a two-dimensional camera and a three-dimensional camera. Alternatively, the image acquisition device 10 can be a black-and-white camera or a color camera, or it can be equipped with both a black-and-white camera and a color camera; no limitation is made here. Of course, in order to enable the image acquisition device 10 to have a better imaging effect, the detection device 100 can also be equipped with auxiliary imaging devices such as light sources, bright-field light sources or dark-field light sources, and focusing modules.
[0076] Since sample A is usually a product or semi-finished product with extremely high precision, in order to obtain more details of sample A, the image acquisition device 10 generally acquires images with high precision and a small field of view. It cannot acquire a complete image of the entire sample A in one imaging, but can only acquire images of local areas of sample A. By taking pictures of different local areas of sample A separately, each local area of sample A can be detected.
[0077] Please continue reading. Figure 1 The processor 20 is communicatively connected to the image acquisition device 10. Images acquired by the image acquisition device 10 can be transmitted to the processor 20 for processing. Control commands generated by the processor 20 can control the working state of the image acquisition device 10, such as adjusting working parameters and generating shooting commands.
[0078] exist Figure 1 In the example shown, the detection device 100 also includes a carrier device 30 and a transport device 40. The carrier device 30 can be used to fix sample A, and the carrier device 30 can also move and drive sample A to move relative to the image acquisition device 10, such as by moving or rotating. In one example, the carrier device 30 can fix sample A by vacuum adsorption or electrostatic adsorption; in another example, the carrier device 30 can also fix sample A by mechanical clamping or other means. The transport device 40 can be used to transport the sample A to be detected onto the carrier device 30 for detection by the image acquisition device 10. The transport device 40 can also be used to remove sample A from the carrier device 30 after detection and transport it to the qualified product collection point or the unqualified product collection point. The transport device 40 can specifically be a robotic arm, such as a two-jointed robotic arm, a three-jointed robotic arm, etc., without limitation.
[0079] The following will detail the detection method used by detection equipment 100 to detect sample A:
[0080] Please see Figure 3 and Figure 4 , Figure 3 and Figure 4This is a flowchart illustrating a detection method according to certain embodiments of this application. The detection method includes the following steps:
[0081] 01: Provide a local image, which is an image of a local region of sample A;
[0082] 02: Perform the image acquisition operation on each local region of sample A to generate the image to be processed;
[0083] 03: After the image acquisition operation, multiple images to be processed are stitched together to generate a complete image of sample A.
[0084] The image acquisition operation includes the following steps:
[0085] The image acquisition operation to be processed includes:
[0086] 021: Use a local portion of the image as the image to be processed;
[0087] 022: Perform judgment processing on the local image to determine whether the local image includes the area to be inspected for sample A; and
[0088] 023: If the result of the initial judgment and processing is an image in which the area to be inspected of sample A is included in the local image, the area to be inspected is identified and processed, and the detection information of the target to be inspected in the area to be inspected is obtained;
[0089] In the detection method of this application, after providing a local image, a processing image acquisition operation is performed on the local image to generate a processing image. When there is no area to be detected in the local image, the local image is directly used as the processing image. When there is an area to be detected in the local image, the detection information of the target to be detected is acquired. A processing image is generated for each local area of sample A, and the multiple processing images are then stitched together to generate a complete image of sample A. Therefore, by taking a picture of each local area of sample A only once, the purpose of detecting the area to be detected and obtaining a complete image of sample A can be achieved. It is not necessary to take pictures of the sample, such as a wafer, multiple times, which improves the efficiency of detecting sample A.
[0090] Specifically, in step 01, a local image is provided, which is an image of a local region of sample A. Sample A includes multiple local regions. In one example, the local image can be provided by the image acquisition device 10, or by any input module; there are no limitations here. Taking the local image provided by the image acquisition device 10 as an example, the area of each local region can be less than or equal to the field of view of the image acquisition device 10, so that when the image acquisition device 10 acquires the local image, it can at least completely acquire one local region. It is understood that each local region can include the edges and / or non-edge areas of sample A; there are no limitations here. Please refer to [reference needed]. Figure 5 The example shown, Figure 5 This is a schematic diagram illustrating the implementation principle of the detection method in some embodiments of this application. The local image P1 is a local image of the local area shown by the dashed box in sample A. When images of other local areas are acquired, the resulting local images are not entirely the same.
[0091] When performing step 02: Perform the image acquisition operation on each local region of sample A to generate the image to be processed. Specifically, when performing step 02, step 021 can be performed first, followed by steps 022 and 023, or steps 022 and 023 can be performed first, followed by step 021. There is no restriction here.
[0092] In step 022, the local image is processed to determine whether it includes the area to be inspected for sample A. Since sample A may not be inspected at every location, after acquiring the local image and before inspecting the local area indicated by the local image, it is necessary to first determine whether the local image includes the area to be inspected for sample A, and then perform different processing on the local image based on the determination result.
[0093] When implementing step 023, if the initial judgment result is an image in which the inspected area of sample A is included in the local image, the inspected area is identified, and the detection information of the inspected target in the inspected area is obtained. If the local image includes the inspected area, the inspected target in the inspected area needs to be detected. This includes detecting one or more of the following: the presence or absence of the inspected target, the position of the inspected target, the geometric parameters of the inspected target, the shape of the inspected target, and the deflection angle of the inspected target. The inspected target can include any one or more of defects, films, or holes. The specific detection method can be: comparing the inspected target with the inspected target in a standard template to generate detection information. After generating the detection information, the user can determine whether the quality of sample A is qualified based on the detection information. Of course, if the local image does not include the inspected area, step 023 is not required.
[0094] In step 03, after acquiring the images to be processed, multiple images are stitched together to generate a complete image of sample A. Specifically, a separate image to be processed is generated for each local region of sample A. For each local region, a corresponding image to be processed can be generated, so that all images corresponding to all local regions can be stitched together to obtain a complete image of sample A. Generating a complete image of sample A facilitates a comprehensive view of sample A, such as the overall distribution of defects. Furthermore, since the targets to be inspected in the inspection area are actually detected during the generation of the images to be processed, the generation of a complete image of sample A also completes the inspection of sample A. Please refer to [link to relevant documentation]. Figure 6 The example shown, Figure 6 This is a schematic diagram illustrating the implementation principle of the detection method in some embodiments of this application. After generating a processing image for each local region of sample A, multiple processing images P2 can be obtained. By stitching together multiple processing images P2, a complete image P of sample A can be obtained.
[0095] Please see Figure 7 , Figure 7 This is a flowchart illustrating the detection method according to certain embodiments of this application. In some embodiments, step 02 further includes step 024: performing local association processing on the detection information and the local image, so that the image region of the target to be detected in the local image corresponds to the detection information of the target to be detected. Before stitching together multiple images to be processed, the detection information is first performed locally on the local image. Specifically, the image region of the target to be detected is associated with the detection information of the target to be detected, so that the associated detection information in the image to be processed can be obtained by viewing the image to be processed alone.
[0096] Specifically, please refer to Figure 8 , Figure 8 This is a flowchart illustrating the detection method according to certain embodiments of this application. In some embodiments, step 024 includes the following steps:
[0097] 0241: Based on the location information of the target to be inspected, obtain the image region of the target to be inspected in the local image; and
[0098] 0242: Mark the detection information in the image region of the target to be detected in the local image.
[0099] In this context, the target to be inspected is the structure of sample A itself. The location information of the target within sample A is known, and this location information can be used to obtain the image region of the target in a local image. Since detection information has already been generated in step 023, this detection information can be annotated on the image region of the target in the local image to more intuitively represent the detection information. Please refer to [link / reference]. Figure 9The example shown, Figure 9 This is a schematic diagram illustrating the implementation principle of the detection method in some embodiments of this application. After implementing steps 0241 and 0242, a shadow is marked in the image area of the target to be inspected in the image to be processed, P2. The detection information represented by the shadow can be customized by the user, such as representing the presence of defects. Of course, there can be different marking methods depending on the actual type of detection information, which are not limited here.
[0100] Please see Figure 10 , Figure 10 This is a flowchart illustrating the detection method according to certain embodiments of this application. In some embodiments, after step 03, the detection method further includes step 04: performing overall association processing between the detection information and the overall image, so that the image region of the target to be detected in the overall image corresponds to the detection information of the target to be detected. After obtaining the overall image, the image region of each target to be detected is then associated with the detection information of that target to be detected, without needing to perform local association processing for each image to be processed, thus improving the efficiency of the association processing.
[0101] Specifically, please refer to Figure 11 , Figure 11 This is a flowchart illustrating the detection method according to certain embodiments of this application. In some embodiments, step 04 includes the following steps:
[0102] 041: Based on the location information of the target to be inspected, obtain the image region of the target to be inspected in the overall image; and
[0103] 042: Mark the detection information in the image region of the target to be detected in the overall image.
[0104] As mentioned above, the location information of the target to be inspected in sample A is known. Therefore, the image region of the target to be inspected can be obtained in the overall image using this location information. Then, the detection information is marked in the image region of the target to be inspected in the overall image. By viewing the overall image, the user can see the corresponding detection information in the image region of the target to be inspected.
[0105] Please see Figure 12 , Figure 12 This is a flowchart illustrating the detection method according to certain embodiments of this application. In some embodiments, the second and subsequent image acquisition operations include the following steps:
[0106] 025: Determine whether the image to be processed formed by the current image acquisition operation contains the region to be inspected obtained in the previous image acquisition operation steps;
[0107] 026: If so, the same region in the previous image acquisition operation as the region to be inspected in the current image acquisition operation is taken as the common region to be inspected; the detection information of the common region to be inspected is locally correlated with the local image of the current image acquisition operation; and
[0108] 027: If not, perform identification processing on the detection area.
[0109] Steps 025, 026 and 027 are the steps included in the second and subsequent image acquisition operations. For the first image acquisition operation, steps 023 and 024 can be executed.
[0110] It is understandable that the indication ranges of two images to be processed (e.g., adjacent images) may partially overlap. If the overlapping area includes the region to be inspected, the detection information of the target in that region needs to be associated with at least two images to be processed simultaneously. Therefore, before detecting a specific region to be inspected, it is first determined whether that specific region has already been detected, i.e., whether the image of that specific region is already included in previous images to be processed. If so, that specific region is considered a common region to be inspected, and the previously obtained detection information for that specific region can be directly associated with the current image to be processed through local association processing, without needing to re-detect that specific region, thus improving detection efficiency. For regions to be inspected that are not included in previous images to be processed, the region to be inspected is identified, and the detection information is associated with the image to be processed, thus improving detection efficiency while ensuring that no regions to be inspected are missed.
[0111] Please combine Figure 13 The example shown, Figure 13This is a schematic diagram illustrating the implementation principle of the detection method in certain embodiments of this application. The indication ranges of the two images to be processed are the range enclosed by dashed frame A1 (hereinafter referred to as range 1) and the range enclosed by dashed frame A2 (hereinafter referred to as range 2), respectively. The two indication ranges overlap, and the overlapping area is exactly the area to be inspected (common area to be inspected), and the target to be inspected X is in the common area to be inspected. After obtaining a local image from range 1, the target to be inspected X is detected, and the detection information is associated with the local image to obtain the image to be processed P11. In the image to be processed P11, the detection information of the target to be inspected is marked as having a defect (as shown by the shaded area in the image to be processed P11). After obtaining a local image P12 from range 2, since the target to be inspected X included in the image to be processed P11 is in the local image P12, the detection information of the target to be inspected X is directly associated with the local image P12 through local association processing to obtain the image to be processed P21, without needing to detect the target to be inspected X again. For target Y that is not included in the image P11 to be processed, it is necessary to re-detect target Y and associate the detection information with the local image P12 to obtain the image P21 to be processed. Figure 13 Both the target X and the target Y shown in the figure have defects.
[0112] Please see Figure 14 , Figure 14 This is a flowchart illustrating the detection method according to certain embodiments of this application. In some embodiments, step 022 includes the following steps:
[0113] 0221: Based on the position and field of view of the image acquisition device when acquiring local images, the indication range of the local image is determined. The local image is the image formed based on the indication range of sample A.
[0114] 0222: Determine whether the indicated range and the area to be inspected intersect on sample A;
[0115] 0223: If so, identify the image containing the region to be inspected for sample A within the local image; and
[0116] 0224: If not, determine the image in which the region to be inspected of sample A is not included in the local image.
[0117] In step 0221, the indication range of the local image is determined based on the position and field of view of the image acquisition device 10 when acquiring the local image. The local image is an image formed based on the indication range of sample A. The position of the image acquisition device 10 can be the relative position of the image acquisition device 10 and sample A, for example, it can be represented by the relative position of the image acquisition device 10 and the support device 30. The field of view of the image acquisition device 10 is determined by the height and field of view angle of the image acquisition device 10. After determining the position and field of view of the image acquisition device 10 when acquiring the local image, the range on sample A indicated by the local image can also be determined, for example... Figure 5 The dashed box in sample A shows the indication range when acquiring local image P1.
[0118] In step 0222, it is determined whether the indicated range and the area to be inspected intersect on sample A. The area to be inspected is pre-defined by the user based on their needs; for example, it could be a region of interest or a region requiring focused inspection. When acquiring any local image, the indicated range of that local image is compared with the area to be inspected. For example, the position of the indicated range of the local image is compared with the position of the area to be inspected. If they intersect, it means that the local image includes the area to be inspected; if they do not intersect, it means that the local image does not include the area to be inspected. Determining whether there is an area to be inspected by judging whether the indicated range and the area to be inspected intersect is highly efficient and reduces the likelihood of missing areas.
[0119] like Figure 5 In the example shown, the area to be inspected is the shaded region in sample A. Of course, depending on different detection requirements, the area to be inspected can be any other shape, or even the entire area of sample A; there are no restrictions here. Figure 5 As can be seen, the indicated area of local image P1 overlaps with the area to be inspected, so the area to be inspected in local image P1 needs to be detected.
[0120] By implementing step 0223 or step 0224, a judgment result can be output on whether the local image includes the region to be detected.
[0121] Please see Figure 15 , Figure 15 This is a flowchart illustrating the detection method of certain embodiments of this application. In some embodiments, step 03 includes the following steps:
[0122] 031: When at least two images to be processed include images of the same region of sample A, selectively retain images of the same region from at least two images to be processed to form the image to be stitched together;
[0123] 032: When only one image to be processed includes an image of a region of sample A, retain the image of that region to form the image to be stitched; and
[0124] 033: Stitch together all the images to be stitched together for sample A to generate a complete image of sample A.
[0125] Two or more different images to be processed may both contain images of the same region. However, in the complete image of sample A, only one image of that same region can be retained. Therefore, if at least two images to be processed contain images of the same region of sample A, one of the images of the same region can be retained to form the image to be stitched before stitching the complete image of sample A. If only one image to be processed contains an image of a certain region of sample A, that image of that region is retained to form the image to be stitched.
[0126] Then, all the images to be stitched together are stitched together to obtain a complete image that is neither incomplete nor has any duplicate areas.
[0127] Please see Figure 16 , Figure 16 This is a flowchart illustrating the detection method according to certain embodiments of this application. In some embodiments, the detection method further includes the following steps:
[0128] 05: Place sample A in the carrier device; and
[0129] 06: Obtain the relative positional relationship between the image acquisition device and the support device when acquiring local images; and generate an acquisition path based on the multiple relative positional relationships corresponding to the acquisition of multiple local images;
[0130] Step 01: Providing a local image includes step 011: the image acquisition device and the carrier device move relative to each other along the acquisition path; and the image acquisition device acquires a local image.
[0131] Before the image acquisition device 10 acquires local images, an acquisition path is first generated so that the image acquisition device 10 and the carrier device 30 can move relative to each other according to the acquisition path and acquire local images of all local areas.
[0132] Specifically, when calculating the acquisition path, it is necessary to ensure that after the image acquisition device 10 and the carrier device 30 move relative to each other along the acquisition path, the image acquisition device 10 can acquire images of all local areas of sample A. The all local areas include the edges of sample A and the non-inspection areas of sample A. This is quite different from the usual path design that only considers the inspection area.
[0133] When the image acquisition device 10 acquires a local image, it is possible to move only the image acquisition device 10, only the support device 30, or both the image acquisition device 10 and the support device 30 simultaneously, so that the relative positional relationship between the image acquisition device 10 and the support device 30 satisfies the result calculated in step 07.
[0134] Please see Figure 17 , Figure 17 This is a schematic diagram of the processor 20 according to certain embodiments of the present application. In some embodiments, the processor 20 includes an input module 21, a to-be-processed image acquisition module 22, and a stitching module 23. The input module 21 can be used to implement step 01, that is, the input module 21 can be used to provide a local image. The to-be-processed image acquisition module 22 can be used to implement step 02, that is, the to-be-processed image acquisition module 22 can be used to perform a to-be-processed image acquisition operation on each local region of sample A to generate a to-be-processed image. The stitching module 23 can be used to implement step 03, that is, after the to-be-processed image acquisition operation, the stitching module 23 can be used to stitch together multiple to-be-processed images to generate a complete image of sample A.
[0135] The processor 20 is used to execute Figures 1 to 16 The detection method in the illustrated embodiment.
[0136] The image acquisition module 22 includes a setting unit 221, a judging unit 222, and a recognition unit 223. The setting unit 221 can be used to implement step 021, that is, the setting unit 221 can be used to use a local image as the image to be processed. The judging unit 222 can be used to implement step 022, that is, the judging unit 222 can be used to judge the local image to determine whether the local image includes the image of the region to be inspected for sample A. The recognition unit 223 can be used to implement step 023, that is, if the result of the initial judging process is that the local image includes the image of the region to be inspected for sample A, the recognition unit 223 can be used to recognize the region to be inspected and obtain the detection information of the target in the region to be inspected.
[0137] Please see Figure 18 , Figure 18This is a schematic diagram of the processor 20 according to certain embodiments of this application. In some embodiments, the processor 20 further includes an association module 24. In one example, the association module 24 includes an association module 24, which can be used to implement step 024, that is, the association module 24 can be used to perform local association processing between the detection information and the local image, so that the image region of the target to be detected in the local image corresponds to the detection information of the target to be detected. When implementing step 024, the association module 24 can be specifically used to implement steps 0241 and 0242, that is, the association module 24 can be specifically used to obtain the image region of the target to be detected in the local image according to the position information of the target to be detected; and to mark the detection information on the image region of the target to be detected in the local image.
[0138] In another example, the association module 24 includes an association module 24 that can be used to implement step 04. That is, the association module 24 can be used to perform overall association processing between the detection information and the overall image, so that the image region of the target to be inspected in the overall image corresponds to the detection information of the target to be inspected. When implementing step 04, the association module 24 can be specifically used to implement steps 041 and 042. That is, the association module 24 can be used to obtain the image region of the target to be inspected in the overall image based on the location information of the target to be inspected; and to annotate the detection information on the image region of the target to be inspected in the overall image.
[0139] Please see Figure 19 , Figure 19 This is a schematic diagram of the processor 20 in some embodiments of this application. In some embodiments, the judgment unit 222 can also be used to implement step 025, that is, the judgment unit 222 can be used to determine whether the image to be processed formed by the current image acquisition operation contains the inspection area acquired in the previous image acquisition operation. The image acquisition module 22 also includes a processing unit 224, which can be used to implement step 026, that is, if yes, the processing unit 224 can be used to take the inspection area that is the same as the inspection area in the previous image acquisition operation as the common inspection area; and perform local association processing on the detection information of the common inspection area and the local image of the current image acquisition operation. The recognition unit 223 can also be used to implement step 027, that is, if no, the recognition unit 223 can be used to perform recognition processing on the detection area.
[0140] Please see Figure 20 , Figure 20This is a schematic diagram of the processor 20 according to certain embodiments of the present application. In some embodiments, the determination unit 222 includes a determination subunit 2221 and a determination subunit 2222. The determination subunit 2221 can be used to implement step 0221, that is, the determination subunit 2221 can be used to determine the indication range of the local image based on the position and field of view of the image acquisition device when acquiring the local image, wherein the local image is an image formed based on the indication range of sample A. The determination subunit 2222 can be used to implement steps 0222, 0223, and 0224, that is, the determination subunit 2222 can be used to determine whether the position of the indication range and the area to be inspected on sample A intersects; if so, determine that the local image includes the area to be inspected of sample A; and if not, determine that the local image does not include the area to be inspected of sample A.
[0141] Please see Figure 21 , Figure 21 This is a schematic diagram of the processor 20 according to certain embodiments of this application. In some embodiments, the stitching module 23 includes a first retention unit 231, a second retention unit 232, and a stitching unit 233. The first retention unit 231 can be used to implement step 031, that is, when at least two images to be processed include images of the same region of sample A, the first retention unit 231 can selectively retain images of the same region from at least two images to be processed to form an image to be stitched. The second retention unit 232 can be used to implement step 032, that is, when only one image to be processed includes an image of a region of sample A, the second retention unit 232 can retain an image of that region to form an image to be stitched. The stitching unit 233 can be used to implement step 033, that is, the stitching unit 233 stitches all the images to be stitched of sample A to generate a complete image of sample A.
[0142] Please see Figure 22 , Figure 22 This is a schematic diagram of the processor 20 according to certain embodiments of this application. In some embodiments, the processor 20 further includes an acquisition module 25. The acquisition module 25 can be used to implement step 06, that is, the acquisition module 25 can be used to acquire the relative positional relationship between the image acquisition device 10 and the support device 30 when acquiring local images; and generate an acquisition path based on the multiple relative positional relationships corresponding to the acquisition of multiple local images. The input module 21 can be used to implement step 011, that is, the input module 21 is specifically used for the relative movement of the image acquisition device 10 and the support device 30 along the acquisition path; and for the acquisition of local images by the image acquisition device.
[0143] It should be noted that the implementation details and effects of the detection method implemented by the processor 20 can be found in the detailed description of the detection method above, and will not be repeated here.
[0144] As described above, the detection device 100 includes a processor 20. The processor 20 can be used to implement the detection method of any embodiment of this application, and therefore the detection device 100 can also be used to implement the detection method of any embodiment of this application, which will not be elaborated further here. Of course, the processor 20 can also be set independently of the detection device 100. That is, the processor 20 is not limited to being used in the detection device 100 of the embodiments of this application, and can also be used in any other device to execute the detection method of any embodiment of this application.
[0145] In the description of this specification, the terms "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with the described embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.
[0146] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the function involved, as will be understood by those skilled in the art to which embodiments of this application pertain.
[0147] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.
Claims
1. A detection method for detecting a sample, said sample comprising multiple local regions, characterized in that, The detection method includes: A partial image is provided, which is an image of a local area of the sample; the partial image is obtained by an image acquisition device acquiring each local area, and the area of the local area is less than or equal to the field of view of the image acquisition device. The image acquisition operation is performed on each local region of the sample to generate an image to be processed; the image acquisition operation includes: The local image is used as the image to be processed; The local image is processed to determine whether it includes the area of the sample to be inspected; and If the result of the initial judgment processing is an image in which the local image includes the area to be inspected of the sample, the area to be inspected is identified and the detection information of the target to be inspected in the area to be inspected is obtained. After the image acquisition operation, multiple images to be processed are stitched together to generate a complete image of the sample, including: when at least two images to be processed include images of the same region of the sample, selectively retaining the images of the same region from the at least two images to form a stitched image; when only one image to be processed includes an image of a region of the sample, retaining the image of that region to form a stitched image; and stitching together all the stitched images of the sample to generate a complete image of the sample; The detection information is correlated with the overall image to make the image region of the target to be inspected in the overall image correspond to the detection information of the target to be inspected; the overall correlation process includes: obtaining the image region of the target to be inspected in the overall image according to the location information of the target to be inspected; and marking the detection information on the image region of the target to be inspected in the overall image.
2. The detection method according to claim 1, characterized in that, The sample includes multiple partial images of the area to be inspected; the second and subsequent image acquisition operations also include: Determine whether the image to be processed formed by the current image acquisition operation contains the region to be inspected obtained in the previous image acquisition operation steps; If so, the same region to be inspected in the previous image acquisition operation is taken as the common region to be inspected; the detection information of the common region to be inspected is locally correlated with the local image of the current image acquisition operation; and If not, perform the identification process on the area to be inspected.
3. The detection method according to claim 1, characterized in that, The detection information includes one or a combination of the presence or absence of the target to be inspected, the location of the target to be inspected, and the geometric parameters of the target to be inspected; the target to be inspected includes defects, films, or holes.
4. The detection method according to claim 1, characterized in that, The step of performing judgment processing on the local image to determine whether the local image includes the image of the region to be inspected of the sample includes: Based on the position and field of view of the image acquisition device when acquiring the local image, the indication range of the local image is determined, and the local image is an image formed based on the indication range of the sample; Determine whether the indicated range and the location of the area to be inspected on the sample overlap; If so, determine that the local image includes the area to be inspected of the sample; and If not, determine that the local image does not include the area to be inspected of the sample.
5. The detection method according to claim 1, characterized in that, The detection method further includes: The sample is placed in the carrier device; and The relative positional relationship between the image acquisition device and the supporting device is obtained when acquiring local images; and an acquisition path is generated based on the multiple relative positional relationships corresponding to the acquisition of multiple local images. Providing a local image includes: the image acquisition device and the carrier moving relative to each other along the acquisition path; and the image acquisition device acquiring the local image.
6. A processor for processing an image of a sample, said sample comprising a plurality of local regions, characterized in that, The processor includes: An input module is used to provide a local image, which is an image of a local region of the sample; the local image is obtained by an image acquisition device acquiring the local region, and the area of the local region is less than or equal to the field of view of the image acquisition device. The image acquisition module is used to perform image acquisition operations on each local region of the sample to generate an image to be processed; the image acquisition module includes: A setting unit is used to set the local image as the image to be processed; A judgment unit is used to perform judgment processing on the local image to determine whether the local image includes an image of the region to be inspected of the sample; and The identification unit is used to identify the region to be inspected and obtain the detection information of the target in the region to be inspected when the result of the initial judgment processing is an image of the region to be inspected that includes the sample in the local image. A stitching module is used to stitch together multiple images to be processed to generate a complete image of the sample; the stitching module includes: a first retention unit, which selectively retains the images of the same region in at least two images to be processed to form a stitched image when at least two images to be processed include images of the same region of the sample; a second retention unit, which retains the image of only one region in an image to be processed to form a stitched image when only one image to be processed includes an image of only one region of the sample; and a stitching unit, which stitches together all the images to be stitched together to generate a complete image of the sample; The overall association module is used to associate the detection information with the overall image after stitching together multiple images to generate a complete image of the sample, so that the image region of the target to be inspected in the overall image corresponds to the detection information of the target to be inspected; the overall association process includes: obtaining the image region of the target to be inspected in the overall image according to the location information of the target to be inspected; and marking the detection information on the image region of the target to be inspected in the overall image.
7. The processor according to claim 6, characterized in that, The sample includes multiple local images of the area to be inspected. The judgment unit is further configured to: determine whether the image to be processed formed by the current image acquisition operation contains the area to be inspected acquired in the previous image acquisition operation steps; The image acquisition module further includes a processing unit, which is configured to: if so, take the same area as the area to be inspected in the previous image acquisition operation as the common area to be inspected in the current image acquisition operation; and perform local correlation processing on the detection information of the common area to be inspected and the local image of the current image acquisition operation. The identification unit is further configured to: if not, perform the identification process on the area to be inspected.
8. The processor according to claim 6, characterized in that, The determination unit includes: A determining subunit is used to determine the indication range of the local image based on the position and field of view of the image acquisition device when acquiring the local image, wherein the local image is an image formed based on the indication range of the sample; The judgment subunit is used to determine whether the position of the indicated range and the area to be inspected on the sample intersects; if so, it determines that the local image includes the image of the area to be inspected of the sample; and if not, it determines that the local image does not include the image of the area to be inspected of the sample.
9. A detection device for detecting a sample, said sample comprising multiple local regions, characterized in that, The detection equipment includes: An image acquisition device for acquiring local images of various local areas of a sample; and The processor according to any one of claims 6 to 8.
10. The detection device according to claim 9, characterized in that, The sample is placed on the carrier device, and the processor further includes an acquisition module for acquiring the relative positional relationship between the image acquisition device and the carrier device when acquiring local images; and generating an acquisition path based on the multiple relative positional relationships corresponding to the acquisition of multiple local images; The input module is specifically used for: relative movement between the image acquisition device and the carrier device along the acquisition path; and for the image acquisition device to acquire the local image.