Method and system for setting region of interest, device, storage medium and computer program product

Abstract

A method and system for setting a region of interest, a device, a storage medium and a computer program product, the method for setting a region of interest comprising: acquiring a target image of a target object; acquiring a gray value of each pixel point in the target image; selecting a pixel point corresponding to a gray value in a preset gray value range to generate a region of interest of the target image. The present application is beneficial to efficiently and accurately setting a region of interest.

Description

Technical Field

[0001] This invention relates to the field of semiconductor manufacturing, and more particularly to a method and system for setting a region of interest, an apparatus, a storage medium, and a computer program product. Background Technology

[0002] As integrated circuit manufacturing processes evolve, the physical dimensions of devices continue to shrink. With each generation reduction in process node, the number of fatal defects generated in the process increases by 50%. These nanometer-scale defects lead to yield problems, posing a challenge to high-precision defect detection.

[0003] To improve efficiency, inspection is often limited to specific critical locations on the chip, such as Static Random Access Memory (SRAM), densely patterned areas, and fine-line areas. These critical locations are commonly referred to as the Area of ​​Interest (CA) in the industry. The selection of the CA allows users to employ different strategies for defect detection on the wafer surface, balancing inspection accuracy and efficiency. Summary of the Invention

[0004] The problem solved by the embodiments of the present invention is to provide a method, system, device, storage medium and computer program product for setting a region of interest, which is beneficial for setting a region of interest efficiently and accurately.

[0005] To address the aforementioned problems, embodiments of the present invention provide a method for setting a region of interest, comprising: acquiring a target image of a target object; acquiring the grayscale value of each pixel in the target image; selecting pixels corresponding to grayscale values ​​within a preset grayscale value range to generate a region of interest for the target image.

[0006] Optionally, obtaining the grayscale value of each pixel in the target image includes: processing the target image into a target grayscale image; and recording the grayscale value of each pixel in the target grayscale image.

[0007] Optionally, before selecting pixels corresponding to gray values ​​within a preset gray value range to generate the region of interest of the target image, the method further includes: obtaining the coordinates of each pixel in the target image; selecting pixels corresponding to gray values ​​within a preset gray value range to generate the region of interest of the target image, and generating the region of interest of the target image based on the coordinates of the pixels corresponding to gray values ​​within the preset gray value range.

[0008] Optionally, based on the coordinates of the pixels corresponding to gray values ​​within a preset gray value range, a region of interest for the target image is generated, including: obtaining the coordinates of the pixels corresponding to gray values ​​within the preset gray value range as target coordinates; and obtaining the region in the target image corresponding to the target coordinates as the region of interest.

[0009] Optionally, in the same step, the grayscale value of each pixel in the target image and the coordinates of each pixel in the target image are obtained.

[0010] Optionally, before selecting pixels corresponding to gray values ​​within a preset gray value range to generate the region of interest of the target image, the method further includes: obtaining the mapping relationship between gray values ​​and the number of pixels corresponding to gray values ​​in the target image; and based on the mapping relationship, selecting the number of pixels corresponding to gray values ​​within a preset gray value range to generate the region of interest of the target image.

[0011] Optionally, the mapping relationship between grayscale values ​​and the number of pixels corresponding to grayscale values ​​in the target image is obtained, including: generating a histogram with grayscale values ​​as the horizontal axis and the number of pixels corresponding to grayscale values ​​as the vertical axis, as the mapping relationship; based on the mapping relationship, selecting the number of pixels corresponding to grayscale values ​​within a preset grayscale value range to generate the region of interest in the target image, including: selecting the horizontal axis region corresponding to the preset grayscale value range as the target horizontal axis region; obtaining the sum of the vertical coordinates of the target horizontal axis region in the histogram, and the corresponding pixels in the target image, to generate the region of interest.

[0012] Optionally, after selecting the pixels corresponding to the gray values ​​within a preset gray value range to generate the region of interest of the target image, the method further includes: selecting connected regions within the region of interest as sub-regions of interest.

[0013] Optionally, after selecting the connected regions within the region of interest as sub-regions of interest, the process further includes: performing edge shrinking or edge expansion processing on the sub-regions of interest.

[0014] Optionally, acquiring a target image of the target object includes: imaging the target object to generate an initial image; and acquiring the target region in the initial image as the target image.

[0015] Optionally, the target object can be imaged using a light beam or an electron beam.

[0016] Optionally, the target region in the initial image is obtained as the target image, and the target region includes the rear metal interconnect region.

[0017] Optionally, after selecting the pixels corresponding to the gray values ​​within a preset gray value range to generate the region of interest of the target image, the method further includes: performing defect detection on the target object in the region of interest.

[0018] Accordingly, this embodiment of the invention also provides a system for setting a region of interest, including: a target image acquisition module for acquiring a target image of a target object; a grayscale value acquisition module for acquiring the grayscale value of each pixel in the target image; and a region of interest generation module for selecting pixels corresponding to grayscale values ​​within a preset grayscale value range to generate a region of interest for the target image.

[0019] Accordingly, embodiments of the present invention also provide a device including at least one memory and at least one processor, wherein the memory stores one or more computer instructions, and the one or more computer instructions are executed by the processor to implement the method for setting the region of interest provided in embodiments of the present invention.

[0020] Accordingly, embodiments of the present invention also provide a storage medium storing one or more computer instructions, which are used to implement the method for setting the region of interest provided in embodiments of the present invention.

[0021] Accordingly, embodiments of the present invention also provide a computer program product, including a computer program / instruction, which, when executed by a processor, implements the method for setting the region of interest provided in embodiments of the present invention.

[0022] Compared with the prior art, the technical solution of the embodiments of the present invention has the following advantages:

[0023] In the method for setting the region of interest provided in this embodiment of the invention, a target image of the target object is obtained, the grayscale value of each pixel in the target image is obtained, and the pixel corresponding to the grayscale value within a preset grayscale value range is selected to generate the region of interest of the target image. In this embodiment of the invention, the different structures, materials, densities, etc. of different regions of the target object often exhibit different brightness during imaging, that is, the grayscale values ​​corresponding to different regions of the target object are different. Therefore, this embodiment of the invention selects the corresponding pixel in the target image based on the different grayscale value ranges of different regions in the target image, and automatically generates the region of interest. This helps to reduce the time spent selecting the region of interest and improve the efficiency of selecting the region of interest. Moreover, selecting based on grayscale values ​​can form a region of interest with irregular boundaries, which is conducive to generating a region of interest with higher accuracy and excluding as many irrelevant regions as possible, thereby facilitating the efficient and accurate setting of the region of interest. Attached Figure Description

[0024] Figure 1 This is a flowchart of an embodiment of the method for setting the region of interest according to the present invention;

[0025] Figures 2 to 6 This is a schematic diagram of each step in one embodiment of the method for setting the region of interest of the present invention;

[0026] Figure 7 This is a functional block diagram of an embodiment of the region of interest setting system of the present invention;

[0027] Figure 8 This is a hardware structure diagram of an embodiment of the device provided by the present invention. Detailed Implementation

[0028] As the background technology shows, in order to improve work efficiency, it is often necessary to select only certain key locations on the chip for testing. These key locations are often referred to as the area of ​​interest (CA) in the industry.

[0029] Currently, in electron beam wafer defect inspection systems, the setting of the region of interest (ROI) mainly involves two methods: hand-drawing and importing from a Graphic Design System (GDS). Hand-drawing the ROI is time-consuming and labor-intensive, requiring manual inspection of the wafer surface to draw the ROI bit by bit. A mature product may have hundreds to thousands of areas in its static storage alone, resulting in a significant amount of manual time required. Importing from a GDS can obtain a high-precision ROI and can generate and combine different ROIs according to certain rules. However, this requires obtaining intellectual property rights and support from the design company, and also consumes a considerable amount of time.

[0030] Currently, when using electron beams to detect defects on wafer surfaces, the industry lacks a fast and efficient method to set up areas of interest, thereby reducing manual labor and saving detection resources.

[0031] To address the aforementioned technical problems, embodiments of the present invention provide a method for setting a region of interest. (See reference...) Figure 1 The diagram shows a flowchart of an embodiment of the method for setting the region of interest according to the present invention.

[0032] In this embodiment, the method for setting the region of interest includes the following basic steps:

[0033] Step S1: Obtain the target image of the target object;

[0034] Step S2: Obtain the grayscale value of each pixel in the target image;

[0035] Step S3: Select the pixels corresponding to the gray values ​​within the preset gray value range to generate the region of interest of the target image.

[0036] To make the above-mentioned objects, features and advantages of the embodiments of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0037] Figures 2 to 6 This is a schematic diagram of each step in one embodiment of the method for setting the region of interest of the present invention.

[0038] In semiconductor manufacturing, to improve efficiency, inspection is often limited to specific critical locations on the chip, such as Static Random Access Memory (SRAM), densely patterned areas, and fine-line areas. These critical locations are commonly referred to in the industry as the Area of ​​Interest (CA). The selection of the CA allows users to employ different strategies for defect detection on the wafer surface to balance inspection accuracy and efficiency.

[0039] refer to Figure 2 Step S1: Obtain the target image 100b of the target object.

[0040] Subsequently, a region of interest is generated based on the target image 100b.

[0041] Accordingly, in this embodiment, the target object can be a wafer or a chip.

[0042] In this embodiment, acquiring the target image 100b of the target object includes: imaging the target object to generate an initial image 100a.

[0043] The initial image 100a is used to obtain the target image 100b.

[0044] In this embodiment, a light beam or electron beam is used to image the target object.

[0045] That is, the energy source of the imaging system for the target object can be a light beam or an electron beam.

[0046] Specifically, in this embodiment, an optical imaging system is introduced into the electron beam detection system to perform optical microscopic imaging on the target object in order to obtain an initial image 100a.

[0047] In this embodiment, the target region in the initial image 100a is obtained as the target image 100b.

[0048] Specifically, in the initial image 100a, a risk area prone to defects is selected as the target area and used as the target image 100b so that the defect risk area can be detected subsequently.

[0049] In this embodiment, the target region in the initial image 100a is obtained as the target image 100b, and the target region includes the rear metal interconnect region.

[0050] The back-end metal interconnect area has a relatively dense pattern, especially the static storage area, which requires defect detection, such as bridging defect detection. Therefore, in this embodiment, it is particularly suitable to use the back-end metal interconnect area as the target area.

[0051] Step S2: Obtain the grayscale value of each pixel in the target image 100b.

[0052] Obtain the grayscale value of each pixel in the target image 100b, which will be used as the basis for dividing the region of interest in the target image 100b.

[0053] In this embodiment, obtaining the grayscale value of each pixel in the target image 100b includes: processing the target image 100b into a target grayscale image.

[0054] The target image 100b is processed into a target grayscale image to obtain the grayscale value of each pixel.

[0055] Specifically, in this embodiment, an optical imaging system and image recognition function are introduced into the electron beam detection system. Optical microscopy combined with computer image processing is used to process the target image 100b into a target grayscale image.

[0056] Accordingly, in this embodiment, the grayscale value of each pixel in the target grayscale image is recorded.

[0057] In this embodiment, before selecting the pixels corresponding to the gray values ​​within the preset gray value range to generate the region of interest of the target image 100b, the method further includes: obtaining the coordinates of each pixel in the target image 100b.

[0058] Obtain the coordinates of each pixel in the target image 100b, which are used to mark the position of each pixel in the target image 100b.

[0059] In this embodiment, in the same step, the grayscale value of each pixel in the target image 100b and the coordinates of each pixel in the target image 100b are obtained.

[0060] Specifically, in this embodiment, each pixel in the target grayscale image is traversed and its corresponding coordinates and grayscale value are recorded.

[0061] refer to Figure 3 Before selecting the pixels corresponding to gray values ​​within a preset gray value range to generate the region of interest of the target image 100b, the process also includes: obtaining the mapping relationship between gray values ​​and the number of pixels with corresponding gray values ​​in the target image 100b.

[0062] The mapping relationship between grayscale values ​​and the number of pixels with corresponding grayscale values ​​in the target image 100b is obtained to associate grayscale values ​​and the number of pixels in the target image 100b, thereby providing a data-driven representation of the overall grayscale value distribution of the target image 100b and facilitating subsequent pixel selection based on grayscale values.

[0063] In this embodiment, obtaining the mapping relationship between grayscale values ​​and the number of pixels corresponding to grayscale values ​​in the target image 100b includes: generating a histogram with grayscale values ​​on the horizontal axis and the number of pixels corresponding to grayscale values ​​on the vertical axis as the mapping relationship.

[0064] As an example, Figure 3 This diagram shows a histogram where the horizontal axis represents grayscale values ​​and the vertical axis represents the number of pixels corresponding to those grayscale values.

[0065] By showing the mapping relationship between gray values ​​and the number of pixels with corresponding gray values ​​in the target image 100b in the form of a histogram, the overall gray value distribution of the target image 100b is made more intuitive, making the subsequent selection of pixels based on gray values ​​more efficient and convenient.

[0066] Reference Figures 3 to 6 Step S3: Select the pixels corresponding to the gray values ​​within the preset gray value range to generate the region of interest 100c of the target image 100b.

[0067] Generate a region of interest 100c for the target image 100b, which is used as the region of interest for the target object.

[0068] Specifically, in this embodiment, the region of interest 100c is used as the region in the wafer or chip for subsequent defect detection.

[0069] In this embodiment, the different structures, materials, densities, etc. of different regions of the target object often exhibit different brightness during imaging, that is, the gray values ​​corresponding to different regions of the target object are different. Therefore, this embodiment selects the corresponding pixels in the target image 100b based on the different ranges of gray values ​​in different regions of the target image 100b, and automatically generates the region of interest 100c. This helps to reduce the time spent selecting the region of interest 100c and improve the efficiency of selecting the region of interest 100c. Moreover, selecting based on gray values ​​can form a region of interest 100c with irregular boundaries, which is conducive to generating a region of interest 100c with higher accuracy and excluding as many irrelevant regions as possible, thereby facilitating the efficient and accurate setting of the region of interest 100c.

[0070] Specifically, in this embodiment, an optical imaging system and image recognition function are introduced into the electron beam detection system. By combining optical microscopy with computer image processing, one or more regions of interest 100c are automatically generated based on the different gray values ​​of each region of the target image 100b.

[0071] In this embodiment, pixels corresponding to gray values ​​within a preset gray value range are selected to generate a region of interest 100c of the target image. Based on the coordinates of the pixels corresponding to gray values ​​within the preset gray value range, a region of interest 100c of the target image 100b is generated.

[0072] Based on the coordinates of the pixels corresponding to the gray values ​​within a preset gray value range, the region of interest 100c of the target image 100b can be generated accurately and quickly.

[0073] In this embodiment, the region of interest 100c of the target image is generated based on the coordinates of the pixels corresponding to the gray values ​​within a preset gray value range, including: obtaining the coordinates of the pixels corresponding to the gray values ​​within the preset gray value range as the target coordinates.

[0074] Obtain the target coordinates to select the corresponding pixels in the target image 100b.

[0075] In this embodiment, the region corresponding to the target coordinates in the target image 100b is obtained as the region of interest 100c.

[0076] In the target image 100b, the merged region of the pixels corresponding to the target coordinates is obtained as the region of interest 100c.

[0077] In this embodiment, based on the mapping relationship, a number of pixels corresponding to gray values ​​within a preset gray value range are selected to generate the region of interest 100c of the target image 100b.

[0078] Based on the mapping relationship, the number of pixels corresponding to the gray values ​​within the preset gray value range is selected. This allows for the selection of pixels based on an overview of the gray value distribution of the target image 100b, making the selection of pixels more accurate and efficient.

[0079] Specifically, in this embodiment, based on the mapping relationship, a number of pixels corresponding to gray values ​​within a preset gray value range are selected to generate a region of interest 100c of the target image 100b, including: selecting the horizontal coordinate region corresponding to the preset gray value range as the target horizontal coordinate region.

[0080] Selecting the target horizontal coordinate region directly in the histogram makes the selection of the preset grayscale value range more intuitive.

[0081] In this embodiment, the sum of the vertical coordinates of the target horizontal coordinate region in the histogram is obtained, and the corresponding pixel points in the target image 100b are used to generate the region of interest 100c.

[0082] As an example, Figure 4 for Figure 3The pixels contained in the horizontal coordinate region A of the target are located in the region of interest 100c in the target image 100b. Figure 5 for Figure 3 The pixels contained in the horizontal coordinate region B of the target image correspond to the region of interest 100c in the target image 100b. Figure 6 for Figure 3 In this embodiment, based on actual needs, a preset grayscale value range is set, and the corresponding target horizontal coordinate region is selected in the histogram. Then, the target horizontal coordinate region C contains the pixels in the target horizontal coordinate region, which are then merged to form the target region 100c in the target image 100b.

[0083] In this embodiment, after selecting the pixels corresponding to the gray values ​​within a preset gray value range to generate the region of interest 100c of the target image 100b, the method further includes: selecting the connected regions within the region of interest 100c as sub-regions of interest.

[0084] The region where pixels are merged based on grayscale values ​​is selected as the region of interest 100c. However, the boundary of the region of interest 100c is irregular and it is easy to generate multiple disconnected regions. Therefore, the connected regions in the region of interest 100c are selected as sub-regions of interest, so that the sub-regions of interest can be independently detected for defects.

[0085] In this embodiment, after selecting the connected regions within the region of interest as sub-regions of interest, the method further includes: performing edge shrinking or edge expansion processing on the sub-regions of interest.

[0086] Based on actual needs, if it is necessary to focus on all related areas of the sub-region of interest as comprehensively as possible, then the sub-region of interest will be expanded by increasing its area appropriately. If it is necessary to focus on the key areas of the sub-region of interest more concisely, then the sub-region of interest will be shrunken by decreasing its area appropriately.

[0087] Specifically, in this embodiment, shrinking the boundary of the sub-region of interest means shrinking the boundary of the sub-region of interest inward, and expanding the boundary of the sub-region of interest means expanding the boundary of the sub-region of interest outward.

[0088] In this embodiment, after selecting the pixels corresponding to the gray values ​​within a preset gray value range to generate the region of interest 100c of the target image, the method further includes: performing defect detection on the target object in the region of interest 100c.

[0089] This involves defect detection of the wafer or chip structure within the 100c region of interest.

[0090] Specifically, in this embodiment, defect detection is performed on the SRAM structure of the region of interest 100c on the wafer or chip. Traditionally, drawing the region of interest 100c of all SRAM structures of a product by hand takes 1 to 2 working days, while the method used in this embodiment can shorten the time to minutes and can eliminate as many irrelevant areas as possible, generating a relatively high-precision polygonal region of interest 100c.

[0091] Accordingly, the present invention also provides a system for setting a region of interest. Figure 7 This is a functional block diagram of an embodiment of the region of interest setting system of the present invention.

[0092] In this embodiment, the interest region setting system 50 includes: a target image acquisition module 501, used to acquire a target image of a target object; a grayscale value acquisition module 502, used to acquire the grayscale value of each pixel in the target image; and an interest region generation module 503, used to select pixels corresponding to grayscale values ​​within a preset grayscale value range to generate an interest region of the target image.

[0093] In semiconductor manufacturing, to improve efficiency, inspection is often limited to specific critical locations on the chip, such as Static Random Access Memory (SRAM), densely patterned areas, and fine-line areas. These critical locations are commonly referred to in the industry as the Area of ​​Interest (CA). The selection of the CA allows users to employ different strategies for defect detection on the wafer surface to balance inspection accuracy and efficiency.

[0094] The target image acquisition module 501 is used to acquire the target image of the target object.

[0095] The region of interest is then generated based on the target image.

[0096] Accordingly, in this embodiment, the target object can be a wafer or a chip.

[0097] In this embodiment, acquiring the target image of the target object includes: imaging the target object to generate an initial image.

[0098] The initial image is used to obtain the target image.

[0099] In this embodiment, a light beam or electron beam is used to image the target object.

[0100] That is, the energy source of the imaging system for the target object can be a light beam or an electron beam.

[0101] Specifically, in this embodiment, an optical imaging system is introduced into the electron beam detection system to perform optical microscopic imaging on the target object in order to obtain an initial image.

[0102] In this embodiment, the target region in the initial image is obtained as the target image.

[0103] Specifically, in the initial image, risk areas prone to defects are selected as target areas and used as target images so that subsequent defect risk areas can be detected.

[0104] In this embodiment, the target region in the initial image is obtained. In the target image, the target region includes the rear metal interconnect region.

[0105] The back-end metal interconnect area has a relatively dense pattern, especially the static storage area, which requires defect detection, such as bridging defect detection. Therefore, in this embodiment, it is particularly suitable to use the back-end metal interconnect area as the target area.

[0106] The grayscale value acquisition module 502 is used to acquire the grayscale value of each pixel in the target image.

[0107] The grayscale value of each pixel in the target image is obtained and used as the basis for dividing the region of interest in the target image.

[0108] In this embodiment, obtaining the grayscale value of each pixel in the target image includes: processing the target image into a target grayscale image.

[0109] The target image is processed into a target grayscale image to obtain the grayscale value of each pixel.

[0110] Specifically, in this embodiment, an optical imaging system and image recognition function are introduced into the electron beam detection system. The target image is processed into a target grayscale image by combining optical microscopy imaging with computer image processing.

[0111] Accordingly, in this embodiment, the grayscale value of each pixel in the target grayscale image is recorded.

[0112] In this embodiment, before selecting pixels corresponding to gray values ​​within a preset gray value range to generate the region of interest of the target image, the method further includes: obtaining the coordinates of each pixel in the target image.

[0113] Obtain the coordinates of each pixel in the target image, which are used to mark the position of each pixel in the target image.

[0114] In this embodiment, in the same step, the grayscale value of each pixel in the target image and the coordinates of each pixel in the target image are obtained.

[0115] Specifically, in this embodiment, each pixel in the target grayscale image is traversed and its corresponding coordinates and grayscale value are recorded.

[0116] In this embodiment, before selecting pixels corresponding to gray values ​​within a preset gray value range to generate the region of interest of the target image, the method further includes: obtaining the mapping relationship between gray values ​​and the number of pixels with corresponding gray values ​​in the target image.

[0117] The mapping relationship between grayscale values ​​and the number of pixels with corresponding grayscale values ​​in the target image is obtained to associate grayscale values ​​and the number of pixels in the target image, thereby providing a data-driven representation of the overall grayscale value distribution of the target image and facilitating subsequent pixel selection based on grayscale values.

[0118] In this embodiment, obtaining the mapping relationship between grayscale values ​​and the number of pixels corresponding to grayscale values ​​in the target image includes: generating a histogram with grayscale values ​​on the horizontal axis and the number of pixels corresponding to grayscale values ​​on the vertical axis as the mapping relationship.

[0119] By displaying the mapping relationship between grayscale values ​​and the number of pixels with corresponding grayscale values ​​in the target image in the form of a histogram, the overall grayscale value distribution of the target image becomes more intuitive, making subsequent pixel selection based on grayscale values ​​more efficient and convenient.

[0120] The region of interest generation module 503 is used to select pixels corresponding to gray values ​​within a preset gray value range to generate the region of interest of the target image.

[0121] Generate the region of interest in the target image, which is used as the area of ​​interest for the target object.

[0122] Specifically, in this embodiment, the region of interest is used as the area in the wafer or chip where subsequent defect detection is performed.

[0123] In this embodiment, the different structures, materials, densities, etc. of different regions of the target object often exhibit different brightness during imaging, that is, the gray values ​​corresponding to different regions of the target object are different. Therefore, this embodiment selects the corresponding pixels in the target image based on the different ranges of gray values ​​in different regions of the target image, and automatically generates the region of interest. This helps to reduce the time spent selecting the region of interest and improve the efficiency of selecting the region of interest. Moreover, selecting based on gray values ​​can form a region of interest with irregular boundaries, which is conducive to generating a region of interest with higher accuracy and excluding irrelevant regions as much as possible, thereby facilitating the efficient and accurate setting of the region of interest.

[0124] Specifically, in this embodiment, an optical imaging system and image recognition function are introduced into the electron beam detection system. By combining optical microscopy with computer image processing, one or more regions of interest are automatically generated based on the different gray values ​​of each region of the target image.

[0125] In this embodiment, pixels corresponding to gray values ​​within a preset gray value range are selected to generate the region of interest of the target image. Based on the coordinates of the pixels corresponding to gray values ​​within the preset gray value range, the region of interest of the target image is generated.

[0126] Based on the coordinates of pixels corresponding to gray values ​​within a preset gray value range, the region of interest in the target image can be generated accurately and quickly.

[0127] In this embodiment, the region of interest of the target image is generated based on the coordinates of the pixels corresponding to the gray values ​​within a preset gray value range, including: obtaining the coordinates of the pixels corresponding to the gray values ​​within the preset gray value range as the target coordinates.

[0128] Obtain the target coordinates to select the corresponding pixels in the target image.

[0129] In this embodiment, the region in the target image corresponding to the target coordinates is obtained as the region of interest.

[0130] In the target image, the merged region of the pixels corresponding to the target coordinates is obtained as the region of interest.

[0131] In this embodiment, based on the mapping relationship, a number of pixels corresponding to gray values ​​within a preset gray value range are selected to generate the region of interest of the target image.

[0132] Based on the mapping relationship, the number of pixels corresponding to the gray values ​​within the preset gray value range can be selected. This allows for the selection of pixels based on an overview of the gray value distribution of the target image, making the selection of pixels more accurate and efficient.

[0133] Specifically, in this embodiment, based on the mapping relationship, a number of pixels corresponding to gray values ​​within a preset gray value range are selected to generate the region of interest of the target image, including: selecting the horizontal coordinate region corresponding to the preset gray value range as the target horizontal coordinate region.

[0134] Selecting the target horizontal coordinate region directly in the histogram makes the selection of the preset grayscale value range more intuitive.

[0135] In this embodiment, the sum of the vertical coordinates of the target horizontal coordinate region in the histogram is obtained, and the corresponding pixel points in the target image are used to generate the region of interest.

[0136] In this embodiment, based on actual needs, a preset grayscale value range is set, and the corresponding target horizontal coordinate region is selected in the histogram. Then, the region of interest is generated in the target image by merging the pixels in the target horizontal coordinate region.

[0137] In this embodiment, after selecting the pixels corresponding to the gray values ​​within a preset gray value range to generate the region of interest of the target image, the method further includes: selecting connected regions within the region of interest as sub-regions of interest.

[0138] If the region where pixels are merged based on grayscale values ​​is selected as the region of interest, the boundary of the region of interest will be irregular and it will be easy to generate multiple broken regions. Therefore, the connected regions in the region of interest are selected as sub-regions of interest, so that the sub-regions of interest can be independently detected for defects.

[0139] In this embodiment, after selecting the connected regions within the region of interest as sub-regions of interest, the method further includes: performing edge shrinking or edge expansion processing on the sub-regions of interest.

[0140] Based on actual needs, if it is necessary to focus on all related areas of the sub-region of interest as comprehensively as possible, then the sub-region of interest will be expanded by increasing its area appropriately. If it is necessary to focus on the key areas of the sub-region of interest more concisely, then the sub-region of interest will be shrunken by decreasing its area appropriately.

[0141] Specifically, in this embodiment, shrinking the boundary of the sub-region of interest means shrinking the boundary of the sub-region of interest inward, and expanding the boundary of the sub-region of interest means expanding the boundary of the sub-region of interest outward.

[0142] In this embodiment, after selecting the pixels corresponding to the gray values ​​within a preset gray value range to generate the region of interest of the target image, the method further includes: performing defect detection on the target object in the region of interest.

[0143] This involves defect detection of the wafer or chip structure in the area of ​​interest.

[0144] Specifically, in this embodiment, defect detection is performed on the SRAM structure of the region of interest on the wafer or chip. Traditionally, drawing the region of interest of all SRAM structures of a product by hand takes 1 to 2 working days, while the method used in this embodiment can shorten the time to minutes and can eliminate as many unwanted areas as possible, generating a relatively high-precision polygonal region of interest.

[0145] This invention also provides a device that can implement the method for setting the region of interest provided in this invention by loading a program, as described above. An optional hardware structure of the terminal device provided in this invention can be as follows: Figure 8 As shown, it includes: at least one processor 01, at least one communication interface 02, at least one memory 03, and at least one communication bus 04.

[0146] In this embodiment, the number of processor 01, communication interface 02, memory 03, and communication bus 04 is at least one, and the processor 01, communication interface 02, and memory 03 communicate with each other through communication bus 04. Communication interface 02 can be an interface of a communication module for network communication, such as the interface of a GSM module. Processor 01 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. Memory 03 may include high-speed RAM and may also include non-volatile memory (NVM), such as at least one disk storage device. Memory 03 stores one or more computer instructions, which are executed by processor 01 to implement the method for setting the region of interest provided in this embodiment of the present invention.

[0147] It should be noted that the aforementioned terminal device may also include other devices (not shown) that may not be essential to understanding the content disclosed in the embodiments of the present invention; given that these other devices may not be essential for understanding the content disclosed in the embodiments of the present invention, the embodiments of the present invention will not describe them one by one.

[0148] This invention also provides a storage medium storing one or more computer instructions for implementing the method for setting the region of interest provided in this invention.

[0149] This invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the method for setting the region of interest provided in this invention.

[0150] In this embodiment, the different structures, materials, densities, etc. of different regions of the target object often exhibit different brightness during imaging, that is, the gray values ​​corresponding to different regions of the target object are different. Therefore, this embodiment selects the corresponding pixels in the target image based on the different ranges of gray values ​​in different regions of the target image, and automatically generates the region of interest. This helps to reduce the time spent selecting the region of interest and improve the efficiency of selecting the region of interest. Moreover, selecting based on gray values ​​can form a region of interest with irregular boundaries, which is conducive to generating a region of interest with higher accuracy and excluding irrelevant regions as much as possible, thereby facilitating the efficient and accurate setting of the region of interest.

[0151] The embodiments of the present invention described above are combinations of elements and features of the present invention. Unless otherwise stated, the elements or features described are optional. Individual elements or features may be practiced without combination with other elements or features. Furthermore, embodiments of the present invention may be constructed by combining some elements and / or features. The order of operations described in the embodiments of the present invention may be rearranged. Some constructions of any embodiment may be included in another embodiment and may be replaced by corresponding constructions of another embodiment. It will be apparent to those skilled in the art that claims in the appended claims that are not expressly referenced to each other may be combined to form embodiments of the present invention, or may be included as new claims in amendments made after the filing of this application.

[0152] Embodiments of the present invention can be implemented by various means, such as hardware, firmware, software, or combinations thereof. In a hardware configuration, the method according to an exemplary embodiment of the present invention can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, etc. In a firmware or software configuration, embodiments of the present invention can be implemented in the form of modules, processes, functions, etc. Software code can be stored in memory units and executed by a processor. The memory units are located inside or outside the processor and can send data to and receive data from the processor via various known means.

[0153] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is accorded the widest scope consistent with the principles and novel features disclosed herein.

[0154] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for setting a region of interest, characterized in that, include: Obtain the target image of the target object; Obtain the grayscale value of each pixel in the target image; Pixels corresponding to gray values ​​within a preset gray value range are selected to generate the region of interest for the target image.

2. The method for setting the region of interest as described in claim 1, characterized in that, Obtaining the grayscale value of each pixel in the target image includes: processing the target image into a target grayscale image; Record the grayscale value of each pixel in the target grayscale image.

3. The method for setting the region of interest as described in claim 1, characterized in that, Before generating the region of interest for the target image by selecting pixels corresponding to gray values ​​within a preset gray value range, the method further includes: Obtain the coordinates of each pixel in the target image; Pixels corresponding to gray values ​​within a preset gray value range are selected to generate the region of interest of the target image. The region of interest of the target image is generated based on the coordinates of the pixels corresponding to gray values ​​within the preset gray value range.

4. The method for setting the region of interest as described in claim 3, characterized in that, Based on the coordinates of the pixels corresponding to the gray values ​​within the preset gray value range, the region of interest of the target image is generated, including: obtaining the coordinates of the pixels corresponding to the gray values ​​within the preset gray value range as the target coordinates; The region in the target image corresponding to the target coordinates is obtained as the region of interest.

5. The method for setting the region of interest as described in claim 4, characterized in that, In the same step, the grayscale value of each pixel in the target image is obtained, as well as the coordinates of each pixel in the target image are obtained.

6. The method for setting the region of interest as described in claim 1, characterized in that, Before generating the region of interest for the target image by selecting pixels corresponding to gray values ​​within a preset gray value range, the method further includes: Obtain the mapping relationship between the grayscale value and the number of pixels in the target image corresponding to the grayscale value; Based on the mapping relationship, a number of pixels corresponding to gray values ​​within a preset gray value range are selected to generate the region of interest of the target image.

7. The method for setting the region of interest as described in claim 6, characterized in that, Obtaining the mapping relationship between the grayscale value and the number of pixels corresponding to the grayscale value in the target image includes: generating a histogram with the grayscale value as the horizontal axis and the number of pixels corresponding to the grayscale value as the vertical axis, as the mapping relationship; Based on the mapping relationship, a number of pixels corresponding to gray values ​​within a preset gray value range are selected to generate the region of interest of the target image, including: selecting the horizontal coordinate region corresponding to the preset gray value range as the target horizontal coordinate region; The sum of the vertical coordinates of the target horizontal coordinate region in the histogram is obtained, and the corresponding pixel points in the target image are used to generate the region of interest.

8. The method for setting the region of interest as described in claim 1, characterized in that, After selecting pixels corresponding to gray values ​​within a preset gray value range to generate the region of interest for the target image, the method further includes: Select the connected regions within the region of interest as sub-regions of interest.

9. The method for setting the region of interest as described in claim 8, characterized in that, After selecting the connected regions within the region of interest as sub-regions of interest, the process further includes: performing edge shrinking or edge expansion processing on the sub-regions of interest.

10. The method for setting the region of interest as described in claim 1, characterized in that, Acquiring a target image of a target object includes: imaging the target object to generate an initial image; The target region in the initial image is obtained as the target image.

11. The method for setting the region of interest as described in claim 10, characterized in that, The target object is imaged using a light beam or electron beam.

12. The method for setting the region of interest as described in claim 10, characterized in that, The target region in the initial image is obtained as the target image, and the target region includes the rear metal interconnect region.

13. The method for setting the region of interest as described in claim 1, characterized in that, After selecting pixels corresponding to gray values ​​within a preset gray value range to generate the region of interest for the target image, the method further includes: Defect detection is performed on the target object in the region of interest.

14. A system for setting a focus area, characterized in that, include: The target image acquisition module is used to acquire the target image of the target object. The grayscale value acquisition module is used to acquire the grayscale value of each pixel in the target image; The region of interest generation module is used to select pixels corresponding to gray values ​​within a preset gray value range to generate the region of interest of the target image.

15. A device, characterized in that, It includes at least one memory and at least one processor, the memory storing one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method for setting the region of interest as described in any one of claims 1-13.

16. A storage medium, characterized in that, The storage medium stores one or more computer instructions, which are used to implement the method for setting the region of interest as described in any one of claims 1-13.

17. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instruction is executed by the processor, it implements the method for setting the region of interest as described in any one of claims 1-13.

Images