Semiconductor device optical detection method and system, electronic equipment and storage medium

Abstract

The invention relates to the technical field of optical detection, in particular to a semiconductor device optical detection method and system, electronic equipment and a storage medium, and the method comprises the steps: determining a defect region based on an initial two-dimensional gray matrix of a to-be-detected semiconductor device, and carrying out the point supplementation of an initial two-dimensional height matrix of the to-be-detected semiconductor device according to the defect region, obtaining a target two-dimensional height matrix; and calculating an optical detection result of the semiconductor device to be detected based on the target two-dimensional height matrix and the initial two-dimensional gray matrix. According to the method, the defect area is innovatively identified and compensated by combining the two-dimensional gray matrix and the height matrix, so that the accuracy and comprehensiveness of optical detection are effectively improved, the limitation of the prior art in complex surface defect detection is solved, and the reliability and consistency of a semiconductor device are ensured.

Description

Background Art

[0002] In the production process of LCD screens, it is necessary to merge the CF (ColorFilter) screen and the Array screen in the middle (the CF screen mainly controls the RGB color, and the Array screen is mainly used to control the color switch, which is convenient for controlling which pixel of the CF screen can be lit). Since there are several fine color film pad heights (PSH: Photo Spacer Height, which can be understood as small columns, hereinafter referred to as PSH) in both screens, when merging, PSH will slightly isolate the two screens to form a gap, and the gap that flows out will be used for liquid crystal pouring. The fine PSH will be distributed on the surface of the two screens. After the PSH is made, the height of each PSH must be measured. If it is too high or too low, the amount of liquid crystal poured must be adjusted. Because if the PSH is too high or too low due to the manufacturing process, once the measurement is inaccurate, it will affect the pouring amount of liquid crystal. In the case of inaccurate measurement, if the PSH is made too short, it will cause excessive pouring of liquid crystal, resulting in the two bonded screens being lifted up and forming a bulge. On the contrary, if the PSH is made too high, there will be gaps without liquid crystal after the fixed liquid crystal is poured, forming internal bubbles. Such situations will cause the expensive LCD screen to be scrapped, resulting in increased costs.

[0003] Therefore, it is urgent to provide a technical solution to solve the above problems. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a semiconductor device optical detection method, system, electronic device and storage medium.

[0005] In a first aspect, the present invention provides a method for optically detecting a semiconductor device, and the technical solution of the method is as follows:

[0006] Based on an initial two-dimensional grayscale matrix of the semiconductor device to be tested, a defect area is determined, and points are supplemented in the initial two-dimensional height matrix of the semiconductor device to be tested according to the defect area to obtain a target two-dimensional height matrix;

[0007] The optical inspection result of the semiconductor device to be tested is calculated based on the target two-dimensional height matrix and the initial two-dimensional grayscale matrix.

[0008] The beneficial effects of a semiconductor device optical detection method of the present invention are as follows:

[0009] The method of the present invention innovatively identifies and compensates for defect areas by combining a two-dimensional grayscale matrix with a height matrix, thereby effectively improving the accuracy and comprehensiveness of optical detection, solving the limitations of existing technologies in complex surface defect detection, and ensuring the reliability and consistency of semiconductor devices.

[0010] On the basis of the above scheme, a semiconductor device optical detection method of the present invention can also be improved as follows.

[0011] In an optional manner, the step of determining the initial two-dimensional height matrix includes:

[0012] Determine the target plane of the three-dimensional point cloud data;

[0013] The initial two-dimensional height matrix is ​​generated by using the X coordinate of the target plane as the row of the two-dimensional height matrix, the Y coordinate of the target plane as the column of the two-dimensional height matrix, and the Z coordinate of the target plane as the element value of the two-dimensional height matrix.

[0014] In the above optional method, by directly extracting the target plane information from the three-dimensional point cloud data and converting it into a two-dimensional height matrix, accurate basic data is provided for subsequent grayscale matrix generation and defect area identification, thereby improving data processing efficiency and accuracy.

[0015] In an optional manner, the step of determining the initial two-dimensional grayscale matrix includes:

[0016] The element values ​​in the initial two-dimensional height matrix are mapped into a preset grayscale value range, and the element values ​​in the initial two-dimensional height matrix are replaced with corresponding grayscale values ​​to generate the initial two-dimensional grayscale matrix.

[0017] In the above optional manner, by converting the height information into grayscale values, it is convenient to use image processing algorithms to identify defect areas, thereby reducing computational complexity while retaining sufficient information for accurate optical detection.

[0018] In an optional manner, the step of determining the defect area based on the initial two-dimensional grayscale matrix of the semiconductor device to be tested includes:

[0019] The image corresponding to the initial two-dimensional grayscale matrix is ​​binarized by using an image processing algorithm to obtain a binary image, and the defect area is determined from the binary image by using the findContours function in Opencv.

[0020] In the above optional manner, through binarization processing and contour detection algorithm, the defect area in the grayscale image can be efficiently identified, the accuracy and speed of defect detection are improved, and a reliable basis is provided for subsequent point filling operations.

[0021] In an optional manner, the step of supplementing the initial two-dimensional height matrix of the semiconductor device to be tested according to the defect area to obtain a target two-dimensional height matrix includes:

[0022] A filling height value is determined according to the coordinate range corresponding to the defect area, and points are supplemented to the initial two-dimensional height matrix according to the filling height value to obtain the target two-dimensional height matrix.

[0023] In the above optional manner, by performing precise point-filling operations on defective areas, defects in the original height matrix can be repaired, making the target two-dimensional height matrix more complete and accurate, thereby improving the reliability and consistency of optical detection results.

[0024] In a second aspect, the present invention provides a semiconductor device optical detection system, the technical solution of the system is as follows:

[0025] The semiconductor device optical detection system comprises: an acquisition module and an analysis module;

[0026] The acquisition module is used to: determine the defect area based on the initial two-dimensional grayscale matrix of the semiconductor device to be tested, and fill in the initial two-dimensional height matrix of the semiconductor device to be tested according to the defect area to obtain a target two-dimensional height matrix;

[0027] The analysis module is used to calculate the optical detection result of the semiconductor device to be tested based on the target two-dimensional height matrix and the initial two-dimensional grayscale matrix.

[0028] The beneficial effects of a semiconductor device optical detection system of the present invention are as follows:

[0029] The system of the present invention innovatively identifies and compensates for defective areas by combining a two-dimensional grayscale matrix with a height matrix, thereby effectively improving the accuracy and comprehensiveness of optical detection, solving the limitations of prior art in complex surface defect detection, and ensuring the reliability and consistency of semiconductor devices.

[0030] Based on the above solution, the semiconductor device optical detection system of the present invention can also be improved as follows.

[0031] In an optional manner, the semiconductor device optical detection system further includes: a first generating module; the first generating module is used to:

[0032] Determine the target plane of the three-dimensional point cloud data;

[0033] The initial two-dimensional height matrix is ​​generated by using the X coordinate of the target plane as the row of the two-dimensional height matrix, the Y coordinate of the target plane as the column of the two-dimensional height matrix, and the Z coordinate of the target plane as the element value of the two-dimensional height matrix.

[0034] In the above optional method, by directly extracting the target plane information from the three-dimensional point cloud data and converting it into a two-dimensional height matrix, accurate basic data is provided for subsequent grayscale matrix generation and defect area identification, thereby improving data processing efficiency and accuracy.

[0035] In a third aspect, a technical solution of an electronic device of the present invention is as follows:

[0036] The invention comprises a memory, a processor and a program stored in the memory and running on the processor. When the processor executes the program, the steps of the semiconductor device optical detection method of the present invention are realized.

[0037] In a fourth aspect, the present invention provides a computer-readable storage medium having the following technical solution:

[0038] The computer-readable storage medium stores instructions, and when the computer-readable storage medium reads the instructions, the computer-readable storage medium executes the steps of the semiconductor device optical detection method of the present invention.

[0039] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are only used to illustrate the embodiments and are not to be considered as limiting the present invention. In addition, the same reference symbols are used to represent the same components throughout the accompanying drawings. In the accompanying drawings:

[0041] Figure 1 A schematic diagram of a process of an embodiment of a semiconductor device optical detection method of the present invention;

[0042] Figure 2 A schematic diagram of the merging of upper and lower glass substrates during LCD production;

[0043] Figure 3 A schematic diagram of the process for accurately measuring the height of the color film pad;

[0044] Figure 4 It is a top view of the original point cloud data;

[0045] Figure 5 Schematic diagram of the algorithm based on binomial fitting;

[0046] Figure 6 This is the filling effect after filling the points;

[0047] Figure 7 This is a schematic diagram for calculating the height and volume of color film padding;

[0048] Figure 8 It is a structural schematic diagram of an embodiment of a semiconductor device optical detection system of the present invention;

[0049] Fig. 9 The figure is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION

[0050] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0051] Figure 1 FIG. 1 is a flow chart of an embodiment of a semiconductor device optical detection method provided by the present invention, wherein the method is executed by a controller. Figure 1 As shown, the following steps are included:

[0052] S1. Based on the initial two-dimensional grayscale matrix of the semiconductor device to be tested, the defect area is determined, and the initial two-dimensional height matrix of the semiconductor device to be tested is supplemented according to the defect area to obtain a target two-dimensional height matrix. In S1:

[0053] 1) The initial two-dimensional grayscale matrix is ​​a two-dimensional matrix obtained by grayscale mapping the three-dimensional morphological information of the semiconductor device (height information obtained by some means such as three-dimensional scanning, such as three-dimensional point cloud data). Each element in the initial two-dimensional grayscale matrix represents the grayscale value of the corresponding position, and the magnitude of the grayscale value reflects the change in the surface height or morphology of the semiconductor device. The initial two-dimensional grayscale matrix is ​​the basis for subsequent image processing and analysis. Through the initial two-dimensional grayscale matrix, image processing algorithms can be efficiently used for feature extraction, defect detection and other operations.

[0054] 2) The defect area refers to the area in the initial two-dimensional grayscale matrix (or the corresponding image) that is identified by image processing algorithms (such as binarization, contour detection, etc.) and has obvious differences from the normal area (such as abnormal grayscale values, irregular shapes, etc.). The above area usually corresponds to defects or abnormalities on the surface of the semiconductor device. Determining the defect area is a prerequisite for subsequent point filling operations. By accurately identifying the defect area, the location and method of the point filling operation can be guided, thereby restoring the integrity of the surface of the semiconductor device.

[0055] 3) The initial two-dimensional height matrix is ​​a two-dimensional matrix extracted directly from the three-dimensional point cloud data of the semiconductor device. Each element in the initial two-dimensional height matrix represents the height value (or Z coordinate value) of the corresponding position. The initial two-dimensional height matrix is ​​the raw data for generating the initial two-dimensional grayscale matrix, and is also the reference for subsequent point filling operations to restore complete height information. Through the initial two-dimensional height matrix, the height distribution and morphological changes on the surface of the semiconductor device can be intuitively understood.

[0056] 4) The target two-dimensional height matrix is ​​a complete two-dimensional height matrix obtained by point filling operation based on the initial two-dimensional height matrix. The point filling operation is usually performed according to the coordinate range and filling height value corresponding to the defect area. The target two-dimensional height matrix is ​​the basis for the subsequent calculation of optical inspection results. Through the point filling operation, the complete height information of the semiconductor device surface can be restored, so as to more accurately evaluate the optical performance.

[0057] Specifically, the controller determines the defect area based on the initial two-dimensional grayscale matrix of the semiconductor device to be tested, and fills the initial two-dimensional height matrix of the semiconductor device to be tested according to the defect area to obtain a target two-dimensional height matrix.

[0058] S2. Calculate the optical inspection result of the semiconductor device to be tested based on the target two-dimensional height matrix and the initial two-dimensional grayscale matrix.

[0059] 1) The optical inspection results are calculated based on the target two-dimensional height matrix and the initial two-dimensional grayscale matrix (or other related data) and are used as indicators or results to evaluate the optical performance of semiconductor devices. The optical inspection results are an important basis for measuring the quality of semiconductor devices. Through the optical inspection results, it can be determined whether the semiconductor device has defects, abnormalities or does not meet the requirements, thereby guiding the subsequent production process improvement or quality control.

[0060] Specifically, the controller further calculates the optical detection result of the semiconductor device to be tested based on the target two-dimensional height matrix and the initial two-dimensional grayscale matrix.

[0061] The technical solution of this embodiment innovatively identifies and compensates for defect areas by combining a two-dimensional grayscale matrix and a height matrix, thereby effectively improving the accuracy and comprehensiveness of optical detection, solving the limitations of existing technologies in complex surface defect detection, and ensuring the reliability and consistency of semiconductor devices.

[0062] In an optional manner, the step of determining an initial two-dimensional height matrix includes:

[0063] Determine the target plane of the three-dimensional point cloud data;

[0064] The X coordinate of the target plane is used as the row of the two-dimensional height matrix, the Y coordinate of the target plane is used as the column of the two-dimensional height matrix, and the Z coordinate of the target plane is used as the element value of the two-dimensional height matrix to generate an initial two-dimensional height matrix.

[0065] The 3D point cloud data of the semiconductor device to be tested is a set of points with (X, Y, Z) coordinates in 3D space. The above coordinate points are obtained through some measurement technology (such as laser scanning, stereo vision, structured light scanning, etc.), which can accurately represent the shape and geometric features of the object surface.

[0066] In this embodiment, a three-dimensional laser scanning head is used to scan layer by layer from the bottom to the top of the surface of the semiconductor device to be tested within the field of view to obtain an interference fringe image, thereby finding the position of each pixel point in the above process when the light intensity is the maximum, and then determining the target plane and completing the 3D reconstruction. Figure 2 As shown, since the color film spacer height (PSH: Photo Spacer Height, which can be understood as a small column, hereinafter referred to as PSH) is a column shape close to an ellipse, the column is slightly inclined from the bottom to the top, and the longitudinal refraction of light is difficult to obtain good reflected light on the inclined surface, so the above-mentioned part of the shape will cause data loss. In order to solve the problem of data loss caused by the tilt of the column, four rectangles of the top and bottom and the left and right are selected to perform tilt correction on the 3D point cloud data.

[0067] like Figure 3 As shown in Figure 1, the general process of PSH precision measurement is described. When the three-dimensional point cloud data is collected, the height value of each point in the three-dimensional point cloud data is associated with the coordinates of each point, and then the X coordinate of each point is used as the row of the initial two-dimensional height matrix, the Y coordinate of each point is used as the column of the initial two-dimensional height matrix, and the Z coordinate of each point is used as the element value of the initial two-dimensional height matrix (also called height value or z value) to generate the initial two-dimensional height matrix.

[0068] Specifically, the controller further determines the target plane of the three-dimensional point cloud data, and uses the X coordinate of the target plane as the row of the two-dimensional height matrix, the Y coordinate of the target plane as the column of the two-dimensional height matrix, and the Z coordinate of the target plane as the element value of the two-dimensional height matrix to generate an initial two-dimensional height matrix.

[0069] In an optional manner, the step of determining an initial two-dimensional grayscale matrix includes:

[0070] The element values ​​in the initial two-dimensional height matrix are mapped to a preset grayscale value range, and the element values ​​in the initial two-dimensional height matrix are replaced with corresponding grayscale values ​​to generate an initial two-dimensional grayscale matrix.

[0071] It should be noted that the purpose of generating the grayscale matrix is ​​to facilitate the processing of various image processing algorithms and to facilitate finding the area of ​​points to be filled (defect area).

[0072] In this embodiment, after obtaining the initial two-dimensional height matrix, each z value of the initial two-dimensional height matrix is ​​mapped to a grayscale value of 0-255, thereby generating an initial two-dimensional grayscale matrix based on the initial two-dimensional height matrix and obtaining corresponding data (the area to be filled in is a black grayscale value of 0).

[0073] Specifically, the controller further maps the element values ​​in the initial two-dimensional height matrix into a preset grayscale value range, and replaces the element values ​​in the initial two-dimensional height matrix with corresponding grayscale values ​​to generate an initial two-dimensional grayscale matrix.

[0074] In an optional manner, the step of determining the defect area based on the initial two-dimensional grayscale matrix of the semiconductor device to be tested includes:

[0075] The image corresponding to the initial two-dimensional grayscale matrix is ​​binarized using an image processing algorithm to obtain a binary image, and the findContours function in Opencv is used to determine the defect area from the binary image.

[0076] In this embodiment, first, through the various coordinates located and the measurement results, the template matching method of the image is used to find the image with the highest similarity between the two images in the measurement, that is, the image with the largest correlation coefficient, and the matched image is binarized. Then, the findContours function in Opencv is used to identify the contour of the PSH column, and the minimum contour is determined by implementing parameter card control on the contour. Subsequently, the least squares method is used to fit the center coordinates of the circumscribed circle of the minimum contour, and the center position of the column in the two-dimensional height matrix is ​​located from the center coordinates. With the center coordinates of the column as the center, a certain area is selected, and the average value of the height value of the area is calculated to obtain the height value of the column. In this process, special attention is paid to multiple defect areas (also called areas to be supplemented or black areas) formed due to incomplete three-dimensional data acquisition. The above-mentioned area appears as black in the initial two-dimensional grayscale matrix, which is the part where the data is missing.

[0077] Specifically, the controller further uses an image processing algorithm to perform binarization processing on the image corresponding to the initial two-dimensional grayscale matrix to obtain a binary image, and uses the findContours function in Opencv to determine the defect area from the binary image.

[0078] In an optional manner, the step of supplementing the initial two-dimensional height matrix of the semiconductor device to be tested according to the defect area to obtain the target two-dimensional height matrix includes:

[0079] According to the coordinate range corresponding to the defect area, the filling height value is determined, and the initial two-dimensional height matrix is ​​supplemented according to the filling height value to obtain the target two-dimensional height matrix.

[0080] It should be noted that the method of filling in points is: using existing collection points (existing height values) for training, and then predicting the defect data through polynomial fitting, so as to obtain accurate prediction values ​​of the defect data.

[0081] In this embodiment, the connected area of ​​the black area is first found through the mapped two-dimensional grayscale matrix image, and the center point coordinates of the PSH can be roughly located through the above connected area (here it is assumed that the center point of the PSH is equivalent to the center point of the connected area ring). Then, the center point is extended in any direction until it stops at the black area. Multiple extension points can be understood as training data (assuming that the center point is extended horizontally to the left, and the training data is the x coordinate of each extension point and the corresponding z coordinate). Using the above training data, the binomial parameters a, b, and c are obtained through binomial fitting (least squares fitting).

[0082] If we continue to extend in the same direction to the area of ​​points to be filled, we can calculate the corresponding z value using the x coordinates of the area of ​​points to be filled and the binomial parameters a, b, c obtained by the above fitting. In this process, each calculated z value will be filled according to the circle corresponding to its current position, that is, filled with the same z value. Therefore, every time the z value of a new position is calculated, the same z value will be added to the circle corresponding to the current z and x coordinates, thereby gradually filling all the areas of points to be filled, until the target two-dimensional height matrix after filling is finally obtained.

[0083] The target two-dimensional height matrix is ​​the key data result obtained after the point filling process is completed, and accurately reflects the height distribution of the three-dimensional shape of PSH on the two-dimensional plane. Furthermore, each value of the target two-dimensional height matrix represents the height value of a certain point within the field of view of PSH, that is, the projection of the z value of the three-dimensional data on the two-dimensional plane.

[0084] In PSH point filling data, such as Figure 4 As shown in , the top view (where the black area represents the area to be filled) and the front view of the original point cloud data are described. Figure 5 As shown in the figure, a schematic diagram of the algorithm based on binomial fitting is described, showing how to calculate the z value of the area of ​​the points to be filled by fitting the binomial parameters. Figure 6 As shown in FIG, the filling effect after filling the points is explained, that is, how to fill the target two-dimensional height matrix according to the calculated z value to obtain complete PSH morphological information.

[0085] Specifically, the controller further determines a filling height value according to a coordinate range corresponding to the defect area, and fills in points on the initial two-dimensional height matrix according to the filling height value to obtain a target two-dimensional height matrix.

[0086] After successfully filling in the points and obtaining the complete point cloud data, the initial two-dimensional grayscale matrix will be updated accordingly, with the corresponding grayscale values ​​of the filled points, and the initial two-dimensional grayscale image can be processed in depth according to the complete PSH shape, so as to accurately fit the shape of the PSH. On this basis, the connected domain of the fitted shape can be calculated, and the blob connected domain analysis function provided by the opencv library can be used to accurately obtain the major and minor axis data of the column in the top view.

[0087] like Figure 7 As shown in the figure, to calculate the height of PSH, we first need to determine the center point of the fitting area and calculate the average value of the height value of the extended area of ​​the center point in the target two-dimensional height matrix (recorded as z1). Then, find the bottom layer height value of PSH in the outermost patch area (recorded as z2). Through the subtraction operation, that is, z1 minus z2, the height data of PSH can be obtained.

[0088] Furthermore, based on the data after filling in the points, the two-dimensional grayscale matrix can be fully utilized for various algorithm processing. First, the initial two-dimensional grayscale image is binarized to effectively segment the PSH area. The above area can be analyzed in detail by the blob analysis function provided by opencv to obtain the major and minor axis data.

[0089] Similarly, if the coordinates of the center point of the two-dimensional image are determined, the corresponding height z value can be found in the target two-dimensional height matrix. Subsequently, the bottom layer height of the PSH is determined in the outermost patch area, and the height data of the PSH can be accurately obtained by calculating the difference between the two.

[0090] In terms of volume calculation based on PSH, the morphological area of ​​PSH is regarded as multiple pixels, each pixel is a cube with height. Therefore, if the volume of each cube is calculated and then multiplied by the number of all pixels, the volume data of the PSH morphological area can be obtained.

[0091] Furthermore, PSH is an irregular ellipse, and it is difficult to accurately obtain the volume calculation using the ellipse volume calculation formula. PSH is regarded as a number of 1*1 pixels, each pixel corresponds to a height value z. Therefore, the volume calculation of PSH can be quantified into several 1*1 volume calculations, that is, 1*1*z is the volume of one point of PSH, and the area of ​​the PSH fitting shape in the top view is the total number of points. Finally, the volume calculation of PSH can be quantified into 1*1*z*total number of points.

[0092] In this embodiment, after the algorithm is designed, a 20x lens is used to perform repeatability testing on the point cloud data of PSH, that is, 30 repeated acquisitions are performed on a single position, and the stability of the final statistical data measurement is that the 3 times standard deviation of the height of PSH is within 0.017 mm, and the stability of the major and minor axes is within 0.3. The data can meet the usage standards of the equipment manufacturer.

[0093] Figure 8 FIG. 2 is a schematic diagram showing a structure of an embodiment of a semiconductor device optical inspection system 200 provided by the present invention. Figure 8 As shown, the system 200 includes: an acquisition module 210 and an analysis module 220 .

[0094] The acquisition module is used to: determine the defect area based on the initial two-dimensional grayscale matrix of the semiconductor device to be tested, and fill in the initial two-dimensional height matrix of the semiconductor device to be tested according to the defect area to obtain a target two-dimensional height matrix;

[0095] The analysis module is used to calculate the optical detection result of the semiconductor device to be tested based on the target two-dimensional height matrix and the initial two-dimensional grayscale matrix.

[0096] In an optional manner, the semiconductor device optical detection system further includes: a first generating module; the first generating module is used to:

[0097] Determine the target plane of the three-dimensional point cloud data;

[0098] The X coordinate of the target plane is used as the row of the two-dimensional height matrix, the Y coordinate of the target plane is used as the column of the two-dimensional height matrix, and the Z coordinate of the target plane is used as the element value of the two-dimensional height matrix to generate an initial two-dimensional height matrix.

[0099] The technical solution of this embodiment innovatively identifies and compensates for defect areas by combining a two-dimensional grayscale matrix and a height matrix, thereby effectively improving the accuracy and comprehensiveness of optical detection, solving the limitations of existing technologies in complex surface defect detection, and ensuring the reliability and consistency of semiconductor devices.

[0100] The above parameters and steps for each module to implement corresponding functions in the semiconductor device optical inspection system 200 of this embodiment can refer to the parameters and steps in the embodiment of the semiconductor device optical inspection method above, and will not be repeated here.

[0101] like Fig. 9As shown, an electronic device 300 according to an embodiment of the present invention includes a processor 320, the processor 320 is coupled to a memory 310, and the memory 310 stores at least one computer program 330, and the at least one computer program 330 is loaded and executed by the processor 320, so that the electronic device 300 implements any of the above-mentioned semiconductor device optical detection methods, specifically:

[0102] The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more processors 320 (Central Processing Units, CPU) and one or more memories 310, wherein the one or more memories 310 store at least one computer program 330, and the at least one computer program 330 is loaded and executed by the one or more processors 320, so that the electronic device 300 implements any semiconductor device optical detection method provided in the above embodiments. Of course, the electronic device 300 may also have components such as a wired or wireless network interface, a keyboard, and an input and output interface for input and output, and the electronic device 300 may also include other components for implementing device functions, which will not be repeated here.

[0103] A computer-readable storage medium according to an embodiment of the present invention stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that a computer implements any of the above-mentioned semiconductor device optical detection methods.

[0104] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.

[0105] In an exemplary embodiment, a computer program product or a computer program is also provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs any of the above-mentioned semiconductor device optical detection methods.

[0106] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects and represent the definition of a specific order or sequence. The order of use of similar objects can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0107] Those skilled in the art know that the present invention can be implemented as a system, method or computer program product. Therefore, the present disclosure can be specifically implemented in the following forms, namely: it can be complete hardware, it can be complete software (including firmware, resident software, microcode, etc.), or it can be a combination of hardware and software, which is generally referred to as "circuit", "module" or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, and the computer-readable medium contains computer-readable program code.

[0108] Any combination of one or more computer-readable media may be used. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or device.

[0109] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A semiconductor device optical detection method, characterized in that: include: Based on an initial two-dimensional grayscale matrix of the semiconductor device to be tested, a defect area is determined, and points are supplemented in the initial two-dimensional height matrix of the semiconductor device to be tested according to the defect area to obtain a target two-dimensional height matrix; The optical inspection result of the semiconductor device to be tested is calculated based on the target two-dimensional height matrix and the initial two-dimensional grayscale matrix.

2. The semiconductor device optical detection method according to claim 1, characterized in that: The step of determining the initial two-dimensional height matrix comprises: Determine the target plane of the three-dimensional point cloud data; The initial two-dimensional height matrix is ​​generated by using the X coordinate of the target plane as the row of the two-dimensional height matrix, the Y coordinate of the target plane as the column of the two-dimensional height matrix, and the Z coordinate of the target plane as the element value of the two-dimensional height matrix.

3. The semiconductor device optical detection method according to claim 1, characterized in that: The step of determining the initial two-dimensional grayscale matrix comprises: The element values ​​in the initial two-dimensional height matrix are mapped into a preset grayscale value range, and the element values ​​in the initial two-dimensional height matrix are replaced with corresponding grayscale values ​​to generate the initial two-dimensional grayscale matrix.

4. The semiconductor device optical inspection method according to any one of claims 1 to 3, characterized in that: The step of determining the defect area based on the initial two-dimensional grayscale matrix of the semiconductor device to be tested comprises: The image corresponding to the initial two-dimensional grayscale matrix is ​​binarized by using an image processing algorithm to obtain a binary image, and the defect area is determined from the binary image by using the findContours function in Opencv.

5. The semiconductor device optical inspection method according to any one of claims 1 to 3, characterized in that: The step of filling points in the initial two-dimensional height matrix of the semiconductor device to be tested according to the defect area to obtain a target two-dimensional height matrix includes: A filling height value is determined according to the coordinate range corresponding to the defect area, and points are supplemented to the initial two-dimensional height matrix according to the filling height value to obtain the target two-dimensional height matrix.

6. A semiconductor device optical inspection system, characterized in that: include: Acquisition module and analysis module; The acquisition module is used to: determine the defect area based on the initial two-dimensional grayscale matrix of the semiconductor device to be tested, and fill in the initial two-dimensional height matrix of the semiconductor device to be tested according to the defect area to obtain a target two-dimensional height matrix; The analysis module is used to calculate the optical detection result of the semiconductor device to be tested based on the target two-dimensional height matrix and the initial two-dimensional grayscale matrix.

7. The semiconductor device optical inspection system according to claim 6, characterized in that: Also includes: The first generation module; the first generation module is used to: Determine the target plane of the three-dimensional point cloud data; The initial two-dimensional height matrix is ​​generated by using the X coordinate of the target plane as the row of the two-dimensional height matrix, the Y coordinate of the target plane as the column of the two-dimensional height matrix, and the Z coordinate of the target plane as the element value of the two-dimensional height matrix.

8. The semiconductor device optical inspection system according to claim 6, characterized in that: Also includes: The second generation module; The second generating module is used to: map the element values ​​in the two-dimensional height matrix to a preset grayscale value range, and replace the element values ​​in the two-dimensional height matrix with corresponding grayscale values ​​to generate the two-dimensional grayscale matrix.

9. An electronic device, characterized in that: The electronic device includes a processor, the processor is coupled to a memory, the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor so that the electronic device implements the semiconductor device optical detection method as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one computer program, and the at least one computer program is loaded and executed by a processor so that the computer-readable storage medium implements the semiconductor device optical detection method according to any one of claims 1 to 5.

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