Camera scanning error measurement method, image correction method and device

Abstract

The invention provides a camera scanning error measurement method, and an image correction method and device. The camera scanning error measurement method comprises the steps that a camera is used for shooting at least part of the area of a target calibration plate in a two-dimensional shooting platform to obtain a calibration image, and feature points in the target calibration plate are evenly distributed in a row and column mode; obtaining a target feature point in the calibration image, wherein the corresponding feature points of the target feature point in the target calibration plate are located on the same straight line; and obtaining a curve expression of a curve where the target feature point is located based on the movement axis coordinate of the target feature point in the two-dimensional shooting platform, and determining the scanning deviation when the camera shoots the calibration image in the two-dimensional shooting platform according to a component in a target frequency range corresponding to the curve expression. According to the scheme, the scanning error of the camera can be determined more accurately.

Description

Technical Field

[0001] This invention relates to the field of camera calibration technology, specifically to a camera scanning error measurement method, an image correction method, a camera scanning error measurement device, an image correction device, an electronic device, a storage medium, and a computer program product. Background Technology

[0002] In automated optical inspection (AOI) of wafers, high-magnification cameras are often used for precise measurement of the global position of the wafer. Typically, high-magnification cameras are used to capture images of different locations on the wafer, and then these images are stitched together to obtain a global image for wafer analysis.

[0003] Taking a wafer automated optical inspection system using a high-magnification line scan camera as an example, this type of system achieves global imaging by scanning line by line and stitching together images of adjacent stripes using a motion platform. However, due to the six degrees of freedom error (DOF) of the scanning axis, a noticeable stitching effect occurs. Traditional methods can utilize additional equipment or the similarity between images at different locations on the wafer to determine the six DEF caused by the scanning axis, and then combine this determined error to stitch together all images to obtain a global image of the wafer. However, using additional equipment increases costs, and when there is no overlap between images at different locations on the wafer, the similarity between these images cannot reflect the six DEF caused by the scanning axis. Therefore, a more accurate method for determining the aforementioned six DEF caused by the scanning axis is needed. Summary of the Invention

[0004] The present invention was proposed in view of the above-mentioned problems.

[0005] According to a first aspect of the present invention, a method for measuring camera scanning error is provided. The method includes: capturing images of at least a portion of a target calibration plate using a camera in a two-dimensional imaging platform to obtain a calibration image, wherein feature points in the target calibration plate are uniformly distributed in rows and columns; acquiring target feature points in the calibration image, wherein corresponding feature points in the target calibration plate are located on the same straight line; acquiring a curve expression of the curve containing the target feature points based on the motion axis coordinates of the target feature points in the two-dimensional imaging platform; and determining the scanning deviation of the camera when capturing the calibration image in the two-dimensional imaging platform based on the components within a preset frequency range corresponding to the curve expression.

[0006] For example, the method further includes: determining sliding windows of corresponding lengths for different preset frequency ranges, wherein the lower the frequency within the preset frequency range, the larger the corresponding length; using sliding windows of different lengths, decomposing the curve corresponding to the curve expression into components of different preset frequency ranges; and determining the components within the target frequency range corresponding to the curve expression from the components of the different preset frequency ranges.

[0007] For example, the step of using sliding windows of different lengths to decompose the curve corresponding to the curve expression into components of different preset frequency ranges includes: using a moving average algorithm to extract the components of the different preset frequency ranges based on the sliding windows of different lengths.

[0008] For example, determining the scanning deviation of the camera when capturing the calibration image on the two-dimensional imaging platform based on the components within the target frequency range corresponding to the curve expression includes: dividing the curve into multiple continuous segments, wherein the number of target feature points in each segment is the same; calculating the amplitude of the component within the target frequency range corresponding to each segment for each segment; determining the scanning error based on the component within the target frequency range corresponding to the target segment, wherein the target segment is a segment whose calculated amplitude is within a preset amplitude range corresponding to the target frequency range.

[0009] For example, the feature is that the width of the domain corresponding to each segment of the curve expression is greater than the length of the sliding window used to extract components within the target frequency range.

[0010] For example, the camera is a line scan camera, the two-dimensional imaging platform is a motion platform for wafer inspection, and the preset amplitude range corresponding to the target frequency range is greater than 1 micrometer.

[0011] For example, before determining the scanning deviation when the camera captures the calibration image in the two-dimensional imaging platform, the method further includes: performing mean correction on the dependent variable of the curve expression to update the curve expression.

[0012] For example, the target feature points are located in the same row as the corresponding feature points in the target calibration board, and the dependent variable of the curve expression is the ordinate of the target feature points, while the independent variable of the curve expression is the abscissa of the target feature points.

[0013] According to a second aspect of the present invention, an image correction method is provided. The method includes: capturing an image of at least a portion of a target object using a camera in a two-dimensional imaging platform to obtain a target image; and correcting the target image according to a reference image correction amount to obtain a corrected target image, wherein the reference image correction amount is determined based on a camera scanning error determined by the camera scanning error measurement method according to any one of claims 1-8.

[0014] For example, the step of using a camera to capture images of at least a portion of a target object within a two-dimensional imaging platform to obtain a target image includes: capturing images of different sub-regions of the target object from different positions within the two-dimensional imaging platform to obtain respective target images; the step of correcting the target image based on the reference image correction amount to obtain a corrected target image includes: determining a correspondence between the camera's shooting position within the two-dimensional imaging platform and the target image correction amount based on the reference image correction amount and the position of the camera when capturing the calibration image within the two-dimensional imaging platform, wherein the calibration image includes multiple images obtained by capturing images of different sub-regions of a target calibration plate from different positions within the two-dimensional imaging platform; determining a target image correction amount for the target image based on the correspondence and the position of the camera when capturing the target image within the two-dimensional imaging platform; and correcting the target image using the target image correction amount to obtain a corrected target image.

[0015] For example, the method further includes: based on the camera scanning error determined by the above-described camera scanning error measurement method and the position of the camera when capturing the calibration image within the two-dimensional imaging platform, fitting a mapping relationship between the camera's shooting position within the two-dimensional imaging platform and the camera's scanning error, wherein the calibration image includes multiple images obtained by capturing images of different sub-regions of the target calibration board at different positions within the two-dimensional imaging platform; calculating the image compensation amount corresponding to the camera's scanning error in the mapping relationship as the reference image correction amount.

[0016] According to a third aspect of the present invention, a camera scanning error measuring device is also provided, comprising:

[0017] The first imaging module is used to capture at least a portion of the target calibration board using a camera in a two-dimensional imaging platform to obtain a calibration image, wherein the feature points in the target calibration board are evenly distributed in rows and columns.

[0018] The feature point extraction module is used to obtain target feature points in the calibration image, wherein the target feature points are located on the same straight line as the corresponding feature points in the target calibration plate.

[0019] The curve generation module is used to obtain the curve expression of the curve where the target feature point is located based on the motion axis coordinates of the target feature point in the two-dimensional imaging platform.

[0020] The error calculation module is used to determine the scanning deviation of the camera when capturing the calibration image on the two-dimensional imaging platform based on the components within the target frequency range corresponding to the curve expression.

[0021] According to a third aspect of the present invention, an image correction apparatus is also provided, comprising:

[0022] The second shooting module is used to take pictures of at least a portion of the target object using a camera on a two-dimensional shooting platform to obtain a target image;

[0023] An image correction module is used to correct the target image according to a reference image correction amount to obtain a corrected target image, wherein the reference image correction amount is determined based on the camera scanning error determined by the camera scanning error measurement method described above.

[0024] According to a fifth aspect of the present invention, an electronic device is also provided, comprising: a processor and a memory, wherein the memory stores computer program instructions, which, when executed by the processor, are used to perform the camera scanning error measurement method and / or image correction method described above.

[0025] According to a sixth aspect of the present invention, a storage medium is also provided, on which program instructions are stored, which, when executed, are used to perform the camera scanning error measurement method and / or image correction method described above.

[0026] According to a seventh aspect of the present invention, a computer program product is also provided, comprising computer program instructions, which, when executed, are used to perform the camera scanning error measurement method and / or image correction method described above.

[0027] In the above technical solution, a camera is used to capture at least a portion of the target calibration board on a two-dimensional imaging platform to obtain a calibration image. The feature points on the target calibration board are uniformly distributed in rows and columns. Then, the target feature points in the calibration image are acquired. The corresponding feature points on the target calibration board are located on the same straight line. Next, based on the motion axis coordinates of the target feature points on the two-dimensional imaging platform, the curve expression of the curve containing the target feature points is obtained. Based on the components within the target frequency range corresponding to the curve expression, the scanning deviation of the camera when capturing the calibration image on the two-dimensional imaging platform is determined. Since the corresponding feature points on the target calibration board are located on the same straight line, the components within the target frequency range corresponding to the curve expression can reflect the scanning error of the camera caused by the six degrees of freedom error of the motion axis of the two-dimensional imaging platform, thus more accurately determining the camera's scanning error.

[0028] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0029] The above and other objects, features, and advantages of the present invention will become more apparent from the more detailed description of the embodiments of the invention in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same parts or steps.

[0030] Figure 1 A schematic flowchart of a camera scanning error measurement method according to an embodiment of the present invention is shown;

[0031] Figure 2 A schematic diagram of capturing calibration images according to an embodiment of the present invention is shown;

[0032] Figure 3 A schematic flowchart illustrating the acquisition of components corresponding to different preset frequency ranges of a curve expression according to an embodiment of the present invention is shown.

[0033] Figure 4 A schematic flowchart illustrating the determination of scanning error when a camera captures a calibration image in a two-dimensional imaging platform, according to an embodiment of the present invention, is shown.

[0034] Figure 5 A schematic flowchart of an image correction method according to an embodiment of the present invention is shown;

[0035] Figure 6A schematic flowchart illustrating the correction of a target image according to yet another embodiment of the present invention is shown;

[0036] Figure 7 A schematic flowchart illustrating the determination of a reference image correction amount according to an embodiment of the present invention is shown;

[0037] Figure 8 A schematic block diagram of a camera scanning error measuring device according to an embodiment of the present invention is shown;

[0038] Figure 9 A schematic block diagram of a camera scanning error measuring device according to an embodiment of the present invention is shown;

[0039] Figure 10 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the present invention more apparent, exemplary embodiments according to the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely a part of the embodiments of the present invention, and not all of the embodiments of the present invention. It should be understood that the present invention is not limited to the exemplary embodiments described herein. Based on the embodiments of the present invention described herein, all other embodiments obtained by those skilled in the art without inventive effort should fall within the protection scope of the present invention.

[0041] To at least partially address the aforementioned problems, a camera scanning error measurement method is proposed. This method utilizes a camera to capture images of at least a portion of a target calibration board on a two-dimensional imaging platform to obtain a calibration image. Feature points on the target calibration board are uniformly distributed in rows and columns. Target feature points in the calibration image are then acquired, with corresponding feature points on the target calibration board lying on the same straight line. Based on the motion axis coordinates of the target feature points on the two-dimensional imaging platform, the curve expression of the curve containing the target feature points is obtained. The scanning deviation of the camera when capturing the calibration image on the two-dimensional imaging platform is determined based on the components within the target frequency range corresponding to the curve expression. Since the corresponding feature points on the target calibration board are lying on the same straight line, the components within the target frequency range corresponding to the curve expression can reflect the camera scanning error caused by the six degrees of freedom error of the two-dimensional imaging platform's motion axes, thus more accurately determining the camera's scanning error.

[0042] Figure 1 A schematic flowchart of a camera scanning error measurement method according to an embodiment of the present invention is shown. Figure 1 As shown, the camera scanning error measurement method may include steps S110 to S140.

[0043] In step S110, a camera is used to capture at least a portion of the target calibration board in a two-dimensional imaging platform to obtain a calibration image, wherein the feature points in the target calibration board are uniformly distributed in rows and columns.

[0044] The camera can be any type of camera, such as a monocular camera, a stereo / binocular camera, or an RGB-D camera. For example, the camera can be a line scan camera or an area scan camera.

[0045] In a 2D imaging platform, a target calibration plate can be placed on a stage, while the camera can be mounted on a support. In the standard orientation, the camera's imaging plane is parallel to the plane of the stage, i.e., parallel to the plane of the target calibration plate. At this time, the camera's optical axis is perpendicular to the plane of the target calibration plate and stage, i.e., perpendicular to the target calibration plate, and the camera's field of view remains unchanged relative to the standard field of view. The stage or the support that holds the camera in the 2D imaging platform can move along an XY motion mechanism during the imaging process. This allows the stage and camera to move relative to each other along the X / Y axes of the XY motion mechanism, enabling the camera to capture images of at least a portion of the target calibration plate from the 2D imaging platform. In commonly used 2D imaging platforms, the camera can be mounted on a support and positioned above the stage, with the camera's optical axis pointing vertically downwards. Preferably, a 2D imaging platform where the stage can move along the XY motion mechanism during the imaging process is selected.

[0046] For example, when placing the target calibration plate on the stage, the row or column where the feature points of the target calibration plate are located can be parallel to the X-axis or Y-axis of the two-dimensional motion axis of the two-dimensional imaging platform, respectively. Thus, the row or column where the feature points of the target calibration plate are located can be parallel to the X-axis or Y-axis of the motion axis coordinate system of the two-dimensional imaging platform, respectively.

[0047] The arrangement of feature points along the Y-axis of the two-dimensional motion axis in the target calibration plate can be considered as rows, meaning any row of feature points along the Y-axis is considered to be in the same row. Alternatively, the arrangement can be considered as columns, meaning any row of feature points along the Y-axis is considered to be in the same column. The row and column arrangement of the feature points in the target calibration plate when they are evenly distributed in rows and columns can be determined according to actual needs.

[0048] For example, the total area of ​​at least a portion of the target calibration board can be the largest area of ​​the target calibration board that can be captured based on the motion axis of the two-dimensional imaging platform, or it can be any area.

[0049] Optionally, at least a portion of the target calibration board may include a single sub-region. This allows for the generation of a calibration image that is specific to that single sub-region.

[0050] Optionally, at least a portion of the target calibration board may include multiple distinct sub-regions. This allows for the generation of multiple calibration images, each targeting a different sub-region.

[0051] Optionally, when at least a portion of the target calibration plate comprises multiple distinct sub-regions, overlapping areas may exist between the different sub-regions captured by the camera, thereby maximizing the capture of calibration images for each sub-region.

[0052] The pattern type of the target calibration board can include checkerboard, dot array, random dot array, etc., and is not limited here. Taking a checkerboard as an example, the feature points of the target calibration board can be corner points. Taking a dot array as an example, the feature points of the target calibration board can be the centers of the dots. The same applies to the feature points of other pattern types of target calibration boards, provided they can be detected, and will not be elaborated here. During the process of capturing calibration images, the orientation of the rows and columns of the feature points in the target calibration board can be predetermined, so as to determine which feature points are in the same row and which feature points are in the same column.

[0053] In step S120, target feature points in the calibration image are obtained, and the corresponding feature points in the target calibration plate are located on the same straight line.

[0054] Target feature points can be extracted from each reference image using feature point detection algorithms or deep learning models. Alternatively, all feature points in each reference image can be extracted using feature point detection algorithms or deep learning models, and then the target feature points can be determined from them.

[0055] For example, feature point detection algorithms may include SIFT algorithm, Harris feature point detection, FAST algorithm, and other detection algorithms.

[0056] For example, sample images with predetermined feature points can be pre-acquired as training samples. These sample images are then input into the deep learning model to output predicted feature points for the sample images. A loss value is then calculated based on the predicted feature points and the predetermined feature points in the sample images, and the deep learning model is optimized using this loss value. After multiple optimizations in this manner, a deep learning model for extracting feature points from a reference image can be obtained.

[0057] Understandably, if the target feature points and their corresponding feature points on the target calibration plate are on the same straight line, and if the calibration image does not have image errors caused by the camera, the target feature points should be on the same straight line in the calibration image, and the motion axis coordinates in the two-dimensional shooting platform should also be on the same straight line.

[0058] In step S130, based on the motion axis coordinates of the target feature point in the two-dimensional imaging platform, the curve expression of the curve where the target feature point is located is obtained.

[0059] By analyzing the transformation relationship between the coordinates of the target feature point in the calibration image and its motion axis coordinates, the motion axis coordinates of the target feature point in the 2D imaging platform can be determined. Then, based on these motion axis coordinates, a curve expression for the curve containing the target feature point can be fitted. For example, a grating ruler can be used to detect the motion axis coordinates of the camera in the 2D platform when capturing the calibration image. Then, based on the transformation relationship between the coordinates of the target feature point in the image and its motion axis coordinates, and according to the camera's motion axis coordinates, the motion axis coordinates of the target feature point in the 2D imaging platform can be determined.

[0060] Understandably, if the camera has no scanning error when capturing the calibration image, the curve containing the target feature point should be a straight line. If the camera has scanning errors when capturing the calibration image, the curve containing the target feature point will not be a straight line. The difference between the curve and the straight line can be used to further analyze the scanning error of the camera when capturing the calibration image.

[0061] Understandably, when the target feature point and its corresponding feature point on the target calibration plate are located on the same straight line, even if the direction of that line is not parallel to the X or Y direction of the motion axis coordinate system of the 2D imaging platform, the curve expression of the curve where the target feature point should be located can be determined based on the coordinates of the target feature point in the calibration image and the direction of the line it should be located on. Then, based on the difference between the curve and the line, combined with the angle between the direction of the line and the X or Y direction, the scanning error of the camera when capturing the calibration image can be analyzed.

[0062] For example, the target feature points are located in the same row as the corresponding feature points in the target calibration board, and the dependent variable of the curve expression is the vertical coordinate of the target feature points, while the independent variable of the curve expression is the horizontal coordinate of the target feature points.

[0063] When the target feature points and their corresponding feature points on the target calibration board are located in the same row, it is not necessary to consider the angle between the direction of the line containing the target feature points and the X or Y direction of the motion axis coordinate system, thus allowing for the analysis of the camera's scanning error when capturing calibration images. This reduces the amount of computation required to subsequently determine the scanning error.

[0064] Based on the target feature points, the curve containing the target feature points can be fitted using spline curve fitting, polynomial fitting, or other methods, and the curve can be expressed based on the motion axis coordinates of the target feature points and the curve expression.

[0065] in

[0066] For example, since the target feature points are discrete, the independent and dependent variables in the curve expression can also be discrete.

[0067] Figure 2 A schematic diagram of capturing calibration images according to an embodiment of the present invention is shown.

[0068] like Figure 2 As shown, when using a camera to photograph at least a portion of a target calibration board on a two-dimensional imaging platform, the target calibration board can be divided into multiple scan bands, each representing a sub-region. For example, the target calibration board can be divided into multiple scan bands along the X-axis of the two-dimensional motion axis of the imaging platform, and a line scan camera can be used to scan each scan band sequentially along the X-axis until all scan bands are scanned, thus obtaining multiple calibration images for each scan band. The arrangement of features along the Y-axis of the two-dimensional motion axis in the target calibration board can be considered as rows, that is, any row of feature points along the Y-axis of the two-dimensional motion axis in the target calibration board can be considered as feature points located in the same row. The feature points in each scan band can be only one row, thus eliminating the need to select target calibration points from the feature points of the calibration images.

[0069] In step S140, the scanning deviation of the camera when capturing calibration images on the two-dimensional imaging platform is determined based on the components within the target frequency range corresponding to the curve expression.

[0070] The target frequency range can be one or more predetermined frequency ranges.

[0071] The components in different frequency ranges reflect the difference between the curve where the target feature point is located and the straight line where the target feature point should be located, as well as the corresponding types of errors. For example, high-frequency components can reflect errors caused by random noise in the image recognition and feature point extraction process, while mid-frequency components can reflect errors caused by objective turret jitter, autofocus fluctuations, etc. Low-frequency components can reflect errors caused by the relative motion of the camera and stage along the two-dimensional motion axis of the motion platform.

[0072] Optionally, based on the time domain or frequency domain, corresponding component extraction conditions can be set according to the parameters corresponding to the components within the target frequency range, so as to directly extract the components within the target frequency range from the curve corresponding to the curve expression.

[0073] Optionally, the components corresponding to different preset frequency ranges of the curve expression can be determined based on the frequency domain function of the curve expression, and then the components within the target frequency range corresponding to the curve expression can be determined from these components. For example, the curve expression can be decomposed into components within different preset frequency ranges based on Fourier transform.

[0074] Optionally, the components of the curve expression within different preset frequency ranges can be determined based on the time-domain function of the curve expression, and then the components within the target frequency range corresponding to the curve expression can be determined from these components. For example, sliding windows of different lengths can be used to decompose the curve expression into components within different preset frequency ranges.

[0075] For example, before determining the scanning deviation when the camera captures a calibration image in a two-dimensional imaging platform, the method further includes step S131: performing mean correction on the dependent variable of the curve expression to update the curve expression.

[0076] Taking a line scan camera as an example, when capturing calibration images, the first target feature point may not be precisely located at the center of the camera's field of view. The curve expression of the target feature point will be superimposed with a constant offset, causing subsequent curves to deviate numerically from the ideal zero baseline. Therefore, the average value of the dependent variable in the curve expression can be determined, and then this average value can be subtracted from the entire dependent variable of the curve expression to perform mean correction on the dependent variable of the curve. After this update, the curve corresponding to the curve expression will be free of overall offset, and the subsequently extracted components can more accurately reflect the camera's scanning error when capturing calibration images.

[0077] For example, the scanning error when capturing calibration images in a two-dimensional imaging platform can be determined based on the Y-coordinate of the components in the target frequency range.

[0078] For example, the scanning error along a first direction and the scanning error along a second direction when the camera captures a calibration image on a two-dimensional imaging platform can be determined separately, thereby determining the total scanning error when capturing a calibration image on the two-dimensional imaging platform, where the first and second directions are perpendicular. For example, the first direction can be the X-direction or Y-direction of the motion axis coordinate system of the two-dimensional imaging platform. For example, based on the above... Figure 2 The process of capturing calibration images shown can determine the scanning error along the X direction when the camera captures calibration images on a two-dimensional imaging platform.

[0079] For example, based on, as Figure 2A similar process to the illustrated image capture can also be used to determine the scanning error along the Y direction when the camera captures calibration images on a 2D imaging platform. For example, the target calibration board can be divided into multiple scan zones along the Y direction of the 2D motion axis of the imaging platform, and a line scan camera can be used to scan each scan zone sequentially along the Y direction until all scan zones are scanned, resulting in multiple calibration images for each scan zone. The arrangement of features along the X direction of the 2D motion axis on the target calibration board can be considered as rows, meaning any row of feature points along the Y direction of the 2D motion axis on the target calibration board is considered to be in the same row. Then, based on the above steps, the scanning error along the Y direction can be determined.

[0080] In the above technical solution, a camera is used to capture at least a portion of the target calibration board on a two-dimensional imaging platform to obtain a calibration image. The feature points on the target calibration board are uniformly distributed in rows and columns. Then, the target feature points in the calibration image are acquired. The corresponding feature points on the target calibration board are located on the same straight line. Next, based on the motion axis coordinates of the target feature points on the two-dimensional imaging platform, the curve expression of the curve containing the target feature points is obtained. Based on the components within the target frequency range corresponding to the curve expression, the scanning deviation of the camera when capturing the calibration image on the two-dimensional imaging platform is determined. Since the corresponding feature points on the target calibration board are located on the same straight line, the components within the target frequency range corresponding to the curve expression can reflect the scanning error of the camera caused by the six degrees of freedom error of the motion axis of the two-dimensional imaging platform, thus more accurately determining the camera's scanning error.

[0081] Figure 3 A schematic flowchart illustrating the acquisition of components corresponding to different preset frequency ranges of a curve expression according to an embodiment of the present invention is shown. Figure 3 As shown, the above camera scanning error measurement may include steps S210 to S230.

[0082] In step S210, a sliding window of corresponding length is determined for different preset frequency ranges, wherein the lower the frequency within the preset frequency range, the larger the corresponding length.

[0083] A smaller sliding window length allows it to capture rapid changes in the curve and separate higher-frequency components. Conversely, a smaller sliding window length balances time positioning and frequency resolution, separating lower-frequency components. Different preset frequency ranges correspond to different sliding window lengths.

[0084] For example, the preset frequency range can include three different frequency ranges, such as the first frequency range corresponding to the high-frequency component, the second frequency range corresponding to the mid-frequency component, and the third frequency range corresponding to the low-frequency component. Among them, the first frequency range has the highest frequency and the corresponding sliding window length is the shortest, while the third frequency range has the lowest frequency and the corresponding sliding window length is the longest.

[0085] For example, the length of a sliding window can be distinguished based on the number of target feature points on the curve corresponding to the curve expression that can be included in each sliding window. For instance, the sliding window length corresponding to the third frequency range mentioned above can contain tens to hundreds of target feature points, while the sliding window length corresponding to the third frequency range mentioned above can contain several to tens of target feature points.

[0086] In step S220, by using sliding windows of different lengths, the curve corresponding to the curve expression is decomposed into components with different preset frequency ranges.

[0087] For example, for each component of different frequencies, a sliding window corresponding to the frequency of the component can be used to slide along the direction of the independent variable of the curve, and the component can be determined based on the arithmetic mean, weighted mean, median or robust mean of the dependent variable of the curve in each sliding window, which can reflect the overall situation of the dependent variable of the sliding window.

[0088] For example, a moving average algorithm can be used to extract components within different preset frequency ranges based on sliding windows of varying lengths. In the moving average algorithm, for each component within a preset frequency range, the sliding window corresponding to that frequency range slides along the independent variable direction of the curve, and the component is determined based on the average value of the dependent variable of the curve within each sliding window. The average value of the dependent variable within the sliding window better reflects the overall oscillation of the curve within that window, and the moving average algorithm can better extract components of different frequencies.

[0089] For example, the components of each frequency can be determined sequentially from smallest to largest or from largest to smallest based on the magnitude relationship of the frequencies within a preset frequency range corresponding to different components. Specifically, when determining the component of each frequency, components determined before that frequency in the curve can be removed, and then the component can be determined based on the current curve.

[0090] In step S230, the component within the target frequency range corresponding to the curve expression is determined from the components within different preset frequency ranges.

[0091] The desired preset frequency range components can be selected from components within different preset frequency ranges as components within the target frequency range, based on the requirements. Optionally, the component with the lowest frequency in the preset frequency range can be selected as the component within the target frequency range, such as the low-frequency component mentioned above. Optionally, both the low-frequency component and the mid-frequency component mentioned above can be selected as components within the target frequency range. Other selection methods are similar and will not be detailed here.

[0092] In the above technical solution, sliding windows of corresponding lengths are determined for different preset frequency ranges. The lower the frequency within the preset frequency range, the larger the corresponding window length. Then, using sliding windows of different lengths, the curve corresponding to the curve expression is decomposed into components within different preset frequency ranges. Subsequently, from these components, the components within the target frequency range corresponding to the curve expression are determined. Using sliding windows to decompose the curve components based on the time domain, compared to pure frequency domain analysis, yields components that are easier for users to understand and is also beneficial for subsequently determining camera scanning errors.

[0093] Figure 4 A schematic flowchart illustrating the determination of scanning error when a camera captures a calibration image in a two-dimensional imaging platform, according to an embodiment of the present invention, is shown. Figure 4 As shown, step S150 may include steps S310 to S330.

[0094] In step S310, the curve is divided into multiple consecutive segments, wherein the number of target feature points in each segment is the same.

[0095] Having the same number of target feature points in each segment ensures that the curve within each segment is affected by the same number of target feature points.

[0096] For example, the width of the domain corresponding to each segment of the curve expression is greater than the length of the sliding window used to extract components within the target frequency range. This ensures that each segment covers multiple sliding windows, allowing for a more comprehensive estimation of the component amplitudes based on the curve combining multiple sliding windows.

[0097] In step S320, for each segment, the amplitude of the component within the target frequency range corresponding to that segment is calculated.

[0098] For each segment, the difference between the maximum and minimum values ​​of the dependent variable of the target frequency component in that segment can be used as the amplitude of the target frequency component corresponding to that segment.

[0099] For example, the amplitude of the component corresponding to the target frequency range for each segment can be determined according to the following formulas 1 and 2:

[0100] X seg={X′ i Formula 1 |i∈[p,q]}

[0101] A = max(X) seg )-min(X seg ) Formula 2

[0102] Among them, X seg Let X′ represent the dependent variable of the components corresponding to the target frequency range in the segment [p,q]. p represents the p-th target feature point of the target frequency component, i.e., the first target feature point in the segment [p,q], and q represents the q-th target feature point of the target frequency component, i.e., the last target feature point in the segment [p,q]. i This represents the component corresponding to the target frequency range in the segment [p,q], where A represents the amplitude.

[0103] In step S330, the scanning error is determined based on the components within the target frequency range corresponding to the target segment, wherein the target segment is a segment whose calculated amplitude is within a preset amplitude range corresponding to the target frequency range.

[0104] Different target frequency ranges can be preset with corresponding preset amplitude ranges. Taking the low-frequency component, which reflects the error caused by the two-dimensional motion axis, as an example, this low-frequency component is characterized by a large amplitude and low frequency. Similarly, the high-frequency component is characterized by a small amplitude and high frequency. The mid-frequency component lies between these two. When the amplitude of the low-frequency component corresponding to a non-target segment is too small, i.e., exceeds the corresponding preset amplitude range, the amplitude may be due to a calculation error, and the low-frequency component corresponding to this non-target segment cannot accurately reflect the error caused by the overall sway of the motion platform. The same applies to components in other frequency ranges within the target frequency range. Therefore, the scanning error can be determined solely based on the components corresponding to the target segment within the target frequency range, thus eliminating the influence of components corresponding to non-target segments within the target frequency range on the scanning error.

[0105] For example, the camera is a line scan camera, the 2D imaging platform is a motion platform for wafer inspection, the target frequency range is the lowest preset frequency range, and the preset amplitude range is greater than 1 micrometer. The lowest preset frequency range can be the preset frequency range corresponding to the aforementioned low-frequency components, where high imaging accuracy is required during wafer inspection. Based on the imaging principle of the line scan camera, low-frequency components with amplitudes greater than 1 micrometer can accurately reflect scanning errors present when the camera captures calibration images, and are not outliers. Therefore, the preset amplitude threshold corresponding to the lowest preset frequency range can be set to greater than 1 micrometer to adapt to the accuracy requirements of wafer inspection and avoid low-amplitude components being mistakenly identified as scanning errors present when the line scan camera captures calibration images.

[0106] In the above technical solution, the curve is divided into multiple continuous segments, where the number of target feature points in each segment is the same. For each segment, the amplitude of the component within the target frequency range corresponding to that segment is calculated. Based on the component within the target frequency range corresponding to the target segment, the scanning error is determined. The target segment is defined as the segment whose calculated amplitude falls within a preset amplitude range corresponding to the target frequency range. This allows for the selection of more reliable components within the target frequency range based on amplitude, thus more accurately determining the camera's scanning error.

[0107] Figure 5 A schematic flowchart of an image correction method according to an embodiment of the present invention is shown. Figure 5 As shown, the image correction method may include steps S410 to S420.

[0108] In step S410, a camera is used to capture images of at least a portion of the target object on a two-dimensional imaging platform to obtain a target image.

[0109] The target object can be at least a portion of multiple different sub-regions or a single sub-region; there is no limitation here. For example, the target object can be a wafer, and at least a portion of the target object is the area where the chip is located or other areas that are desired to be captured.

[0110] For example, the motion axis coordinates corresponding to at least a portion of the target object can be the same as the motion axis coordinates of at least a portion of the target calibration board when the calibration board image was captured.

[0111] In step S420, the target image is corrected according to the reference image correction amount to obtain the corrected target image, wherein the reference image correction amount is determined based on the camera scanning error determined by the camera scanning error measurement method described above.

[0112] The two-dimensional imaging platform used to capture the target image and the two-dimensional imaging platform used in the aforementioned camera scanning error measurement method can be the same two-dimensional imaging platform. Therefore, when the position of the camera when capturing the target image is the same as the position when capturing the calibration image, the image error caused by the camera's scanning error will also be the same. In this case, the corresponding image correction amount can be determined based on the camera scanning error determined by the aforementioned camera scanning error measurement method, and then the target image can be corrected using this image correction amount.

[0113] For example, the position where the camera needs to capture the target image can be determined in advance, and then in the camera scanning error measurement method described above, a calibration image is captured at the same position to determine the image correction amount when the camera captures the calibration image as a reference image correction amount for the target image.

[0114] For example, based on the camera scanning error determined by the aforementioned camera scanning error measurement method and the position of the calibration image captured by the camera, the correspondence between the camera's position during shooting and the scanning error can be determined. Then, based on the scanning error, the corresponding image correction amount can be determined, establishing a mapping relationship between the camera's position during shooting and the image correction amount. Subsequently, based on the camera's position when capturing the target image, the corresponding image correction amount can be matched within this mapping relationship as a reference image correction amount for the target image.

[0115] For example, the corresponding image correction amount can be determined based on the camera scanning error determined by the camera scanning error measurement method described above. This image correction amount can then be used as a reference image correction amount.

[0116] Furthermore, the corresponding image correction amount and the position of the calibration image captured by the camera can be determined based on the scanning error, establishing a correspondence. Then, based on the position of the target image captured by the camera, the corresponding image correction amount can be matched in this mapping relationship as the reference image correction amount for the target image.

[0117] For example, when determining the above correspondence or mapping relationship, a corresponding surface model can be generated to represent the above correspondence or mapping relationship. The method of generating the surface model is not limited here.

[0118] For example, as mentioned above Figure 2 Taking the process of capturing calibration images as an example, the lowest frequency component corresponding to a calibration image for one of the scan bands can be determined based on the above steps. Among them, (centre x , centre y The value () indicates the position of the camera when capturing the calibration image. This component reflects the scanning error in the X direction when the camera captured the scan band for which the calibration image was taken. For each scan band, multiple low-frequency curves representing the lowest frequency component can be acquired. Where m represents the m-th scan band. By interpolating these low-frequency curves in a two-dimensional plane of motion about the camera position, the global low-frequency yaw surface ΔX = F(centre) can be obtained. x , centre y In actual wafer AOI measurement, for any target image targeting a scanning strip, it is only necessary to determine the coordinates (centre) when the camera captured the target image. x ,centre y The corresponding scanning error is obtained by looking up a table in the compensation surface, and the corresponding target image correction amount in the X direction is determined. Then, the target image can be corrected to obtain the corrected target image. Similarly, the image correction amount in the Y direction can also be determined to correct the target image.

[0119] The target image correction can include image correction in the X direction and image correction in the Y direction. The target image correction can be used to adjust the pixels in the target image to obtain a corrected target image.

[0120] In the above technical solution, a camera is used to capture images of at least a portion of the target object on a two-dimensional imaging platform to obtain a target image. Then, the target image is corrected based on a reference image correction amount to obtain a corrected target image. The reference image correction amount is determined based on the camera scanning error measured using the aforementioned camera scanning error measurement method. When subsequent image stitching is performed using the corrected target image, the stitching seams caused by the camera scanning error can be eliminated.

[0121] Figure 6 A schematic flowchart illustrating the correction of a target image according to yet another embodiment of the present invention is shown. Figure 6 As shown, step S410 may include step S510, and step S420 may include steps S520 to S540.

[0122] In step S510, the camera is used to take pictures of different sub-regions of the target object at different positions within the two-dimensional imaging platform to obtain their respective target images.

[0123] The different sub-regions of the target object can be determined according to actual needs. For example, when performing wafer inspection, the target object can be a wafer, and the different sub-regions of the target object can be multiple contiguous different sub-regions.

[0124] In step S520, based on the reference image correction amount and the position of the camera when capturing calibration images within the two-dimensional imaging platform, the correspondence between the camera's shooting position within the two-dimensional imaging platform and the target image correction amount is determined. The calibration images include multiple images obtained by capturing images of different sub-regions of the target calibration board from different positions within the two-dimensional imaging platform.

[0125] Based on the camera scanning error determined by the aforementioned camera scanning error measurement method, the corresponding image correction amount can be determined as the reference image correction amount. Because the camera's position when capturing calibration images within the 2D imaging platform is discrete, the reference image correction amount is also discrete and may not be directly matched to the target image correction amount. Therefore, the correspondence between the camera's shooting position within the 2D imaging platform and the reference image correction amount can be determined using the reference image correction amount and the camera's position when capturing calibration images within the 2D imaging platform. This correspondence can then be used as the basis for determining the relationship between the camera's shooting position within the 2D imaging platform and the target image correction amount.

[0126] In step S530, the target image correction amount is determined based on the correspondence and the position of the camera when capturing the target image within the two-dimensional imaging platform.

[0127] After determining the correspondence between the camera's shooting position within the 2D imaging platform and the reference image correction amount, the target image correction amount corresponding to the camera's shooting position within the 2D imaging platform can be matched as the target image correction amount of the target image based on the camera's position when shooting the target image within the 2D imaging platform.

[0128] In step S540, the target image is corrected using the target image correction amount to obtain the corrected target image.

[0129] The target image correction can include image correction in the X direction and image correction in the Y direction. The target image correction can be used to adjust the pixels in the target image to obtain a corrected target image.

[0130] In the above technical solution, a camera is used at different positions within a two-dimensional imaging platform to capture images of different sub-regions of the target object, obtaining individual target images. Then, based on the correction amount of the reference image and the camera's position when capturing the calibration image within the two-dimensional imaging platform, the correspondence between the camera's position within the platform and the target image correction amount is determined. The calibration image includes multiple images obtained by capturing images of different sub-regions of the target calibration plate at different positions within the platform. Next, based on the correspondence and the camera's position when capturing the target image, the target image correction amount is determined. Finally, the target image is corrected using this correction amount to obtain the corrected target image. This method accurately corrects the target image based on the determined correction amount corresponding to the camera's scanning error, eliminating image errors caused by the camera's scanning error. The corrected target image obtained in this way can eliminate stitching seams caused by the camera's scanning error when used for subsequent image stitching.

[0131] Figure 7 A schematic flowchart illustrating the determination of a reference image correction amount according to an embodiment of the present invention is shown. Figure 7 As shown, the above image correction method may further include steps S610 to S620.

[0132] In step S610, based on the camera scanning error determined by the above-described camera scanning error measurement method and the position of the camera when capturing calibration images in the two-dimensional imaging platform, a mapping relationship between the camera's shooting position in the two-dimensional imaging platform and the camera's scanning error is fitted. The calibration images include multiple images obtained by capturing images of different sub-regions of the target calibration board at different positions of the camera in the two-dimensional imaging platform.

[0133] The fitting method for the mapping relationship between the camera's shooting position within the 2D imaging platform and the camera's scanning error can include spline curve fitting, polynomial fitting, etc., and is not limited here. It is understood that the fitted mapping relationship is a continuous relationship for the shooting position on the 2D imaging platform.

[0134] In step S620, the image compensation amount corresponding to the camera scanning error in the mapping relationship is calculated as the reference image correction amount.

[0135] Based on the scanning errors in the mapping relationship, an image compensation amount for all scanning errors can be selected as a reference image correction amount. This reference image correction amount is continuous and applies to the camera's shooting position data within the 2D imaging platform.

[0136] In the above technical solution, based on the camera scanning error determined by the aforementioned camera scanning error measurement method and the camera's position when capturing calibration images within the 2D imaging platform, a mapping relationship between the camera's shooting position within the 2D imaging platform and the camera's scanning error is fitted. The calibration images include multiple images obtained by capturing different sub-regions of the target calibration board from different positions within the 2D imaging platform. Then, the image compensation amount corresponding to the camera's scanning error in the mapping relationship is calculated as a reference image correction amount. This provides continuous reference image correction amounts for the camera's shooting position within the 2D imaging platform. When determining the target image correction amount for the target image, this can be directly matched to obtain the required target image correction amount, reducing the complexity of subsequent calculations.

[0137] Figure 8 A schematic block diagram of a camera scanning error measuring device according to an embodiment of the present invention is shown. Figure 8 As shown, the camera scanning error measurement device includes a first shooting module 710, a feature point extraction module 720, a curve generation module 730, a decomposition module 740, and an error calculation module 750.

[0138] The first imaging module 710 is used to capture at least a portion of the target calibration plate in a two-dimensional imaging platform using a camera to obtain a calibration image, wherein the feature points in the target calibration plate are uniformly distributed in rows and columns.

[0139] The feature point extraction module 720 is used to obtain target feature points in the calibration image. The corresponding feature points of the target feature points in the target calibration plate are located on the same straight line.

[0140] The curve generation module 730 is used to obtain the curve expression of the curve where the target feature point is located based on the motion axis coordinates of the target feature point in the two-dimensional imaging platform.

[0141] The error calculation module 750 is used to determine the scanning deviation of the camera when capturing calibration images on a two-dimensional imaging platform based on the components within the target frequency range corresponding to the curve expression.

[0142] Figure 9 A schematic block diagram of a camera scanning error measuring device according to an embodiment of the present invention is shown. Figure 9 As shown, the camera scanning error measurement device includes a second shooting module 810 and an image correction module 820.

[0143] The second shooting module 810 is used to take pictures of at least a portion of the target object in a two-dimensional shooting platform using a camera to obtain a target image.

[0144] The image correction module 820 is used to correct the target image according to the correction amount of the reference image to obtain the corrected target image, wherein the correction amount of the reference image is determined based on the camera scanning error determined by the camera scanning error measurement method described above.

[0145] According to another aspect of the present invention, an electronic device is also provided. Figure 10 A schematic block diagram of an electronic device according to an embodiment of the present invention is shown. Figure 10 As shown, the electronic device includes a processor and a memory, wherein the memory stores computer program instructions, which are executed by the processor to perform the camera scanning error measurement method and / or image correction method as described above.

[0146] Furthermore, according to another aspect of the present invention, a storage medium is provided, on which program instructions are stored. When the program instructions are executed by a computer or processor, the computer or processor performs corresponding steps of the camera scanning error measurement method and image correction method described in the embodiments of the present invention, and is used to implement corresponding modules in the camera scanning error measurement device and image correction device described in the embodiments of the present invention. The storage medium may, for example, include a storage component of a tablet computer, a hard disk of a personal computer, a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a portable compact disc read-only memory (CD-ROM), a USB memory, or any combination of the above storage media. The computer-readable storage medium may be any combination of one or more computer-readable storage media.

[0147] According to another aspect of the present invention, a computer program product is also provided, including computer program instructions, which, when executed, are used to perform the camera scanning error measurement method and / or image correction method described above.

[0148] Those skilled in the art can understand the specific implementation and beneficial effects of the above-described camera scanning error measurement device, image correction device, electronic device, storage medium, and computer program product by reading the detailed description of the camera scanning error measurement method and image correction method. For the sake of brevity, they will not be described in detail here.

[0149] Although exemplary embodiments have been described herein with reference to the accompanying drawings, it should be understood that the above exemplary embodiments are merely illustrative and are not intended to limit the scope of the invention. Various changes and modifications can be made therein by those skilled in the art without departing from the scope and spirit of the invention. All such changes and modifications are intended to be included within the scope of the invention as claimed in the appended claims.

[0150] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0151] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed.

[0152] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0153] Similarly, it should be understood that, in order to streamline the invention and aid in understanding one or more of the various aspects of the invention, features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof in the description of exemplary embodiments of the invention. However, this approach should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the corresponding claims, its inventive point lies in solving the corresponding technical problem with fewer features than all of those in a single disclosed embodiment. Therefore, the claims following the detailed description are hereby expressly incorporated into that detailed description, wherein each claim itself is a separate embodiment of the invention.

[0154] Those skilled in the art will understand that, apart from the mutual exclusion of features, all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or apparatus so disclosed can be combined in any combination. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0155] Furthermore, those skilled in the art will understand that although some embodiments described herein include certain features but not others included in other embodiments, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. For example, in the claims, any of the claimed embodiments can be used in any combination.

[0156] The various component embodiments of the present invention can be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will understand that microprocessors or digital signal processors (DSPs) can be used in practice to implement some or all of the functions of some modules in the camera scanning error measurement device and image correction device according to embodiments of the present invention. The present invention can also be implemented as an apparatus program (e.g., a computer program and computer program product) for performing part or all of the methods described herein. Such programs implementing the present invention can be stored on a computer-readable medium or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0157] It should be noted that the above embodiments are illustrative of the invention and not restrictive, and that those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names.

[0158] The above description is merely a specific embodiment of the present invention or an explanation of that embodiment. The scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. The scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for measuring camera scanning error, characterized in that, The method includes: At least a portion of a target calibration board is photographed using a camera on a two-dimensional imaging platform to obtain a calibration image, wherein the feature points in the target calibration board are uniformly distributed in rows and columns. Obtain target feature points in the calibration image, wherein the target feature points are located on the same straight line as the corresponding feature points in the target calibration plate; Based on the motion axis coordinates of the target feature point in the two-dimensional imaging platform, obtain the curve expression of the curve where the target feature point is located; Based on the components within the target frequency range corresponding to the curve expression, the scanning deviation of the camera when capturing the calibration image on the two-dimensional imaging platform is determined.

2. The camera scanning error measurement method according to claim 1, characterized in that, The method further includes: For different preset frequency ranges, a sliding window of corresponding length is determined, wherein the lower the frequency within the preset frequency range, the larger the corresponding length. By utilizing sliding windows of different lengths, the curve corresponding to the curve expression can be decomposed into components with different preset frequency ranges; From the components in the different preset frequency ranges, determine the components in the target frequency range corresponding to the curve expression.

3. The camera scanning error measurement method according to claim 2, characterized in that, The method of using sliding windows of different lengths to decompose the curve corresponding to the curve expression into components with different preset frequency ranges includes: Using a moving average algorithm, components within different preset frequency ranges are extracted based on sliding windows of different lengths.

4. The camera scanning error measurement method according to any one of claims 1 to 3, characterized in that, The step of determining the scanning deviation of the camera when capturing the calibration image on the two-dimensional imaging platform based on the components within the target frequency range corresponding to the curve expression includes: The curve is divided into multiple consecutive segments, wherein the number of target feature points in each segment is the same; For each segment, calculate the amplitude of the component within the target frequency range corresponding to that segment; The scanning error is determined based on the components within the target frequency range corresponding to the target segment, wherein the target segment is a segment whose calculated amplitude falls within a preset amplitude range corresponding to the target frequency range.

5. The camera scanning error measurement method according to claim 4 of claim 2, characterized in that, The width of the domain corresponding to each segment of the curve expression is greater than the length of the sliding window used to extract components within the target frequency range.

6. The camera scanning error measurement method according to claim 4, characterized in that, The camera is a line scan camera, the two-dimensional imaging platform is a motion platform for wafer inspection, the target frequency range is a preset frequency range with the lowest frequency, and the preset amplitude range is greater than 1 micrometer.

7. The camera scanning error measurement method according to claim 1, characterized in that, Before determining the scanning deviation when the camera captures the calibration image on the two-dimensional imaging platform, the method further includes: The dependent variable of the curve expression is corrected for mean to update the curve expression.

8. The camera scanning error measurement method according to claim 1, characterized in that, The target feature points are located in the same row as the corresponding feature points on the target calibration board, and the dependent variable of the curve expression is the ordinate of the target feature points, while the independent variable of the curve expression is the abscissa of the target feature points.

9. An image correction method, characterized in that, include: Using a camera on a two-dimensional imaging platform, at least a portion of the target object is photographed to obtain a target image; The target image is corrected according to the reference image correction amount to obtain the corrected target image, wherein the reference image correction amount is determined based on the camera scanning error determined by the camera scanning error measurement method as described in any one of claims 1-8.

10. The image correction method according to claim 9, characterized in that, The method of using a camera to capture images of at least a portion of a target object on a two-dimensional imaging platform to obtain a target image includes: By using a camera at different positions within a two-dimensional imaging platform to capture images of different sub-regions of the target object, individual target images can be obtained. The step of correcting the target image based on the reference image correction amount to obtain the corrected target image includes: Based on the reference image correction amount and the position of the camera when capturing the calibration image within the two-dimensional imaging platform, the correspondence between the camera's shooting position within the two-dimensional imaging platform and the target image correction amount is determined. The calibration image includes multiple images obtained by capturing different sub-regions of the target calibration board from different positions within the two-dimensional imaging platform. Based on the correspondence and the position of the camera when capturing the target image within the two-dimensional imaging platform, the target image correction amount is determined. The target image is corrected using the target image correction amount to obtain the corrected target image.

11. The image correction method according to claim 9, characterized in that, The method further includes: Based on the camera scanning error determined by the camera scanning error measurement method according to any one of claims 1-8 and the position of the camera when capturing the calibration image in the two-dimensional imaging platform, a mapping relationship between the shooting position of the camera in the two-dimensional imaging platform and the camera scanning error is fitted, wherein the calibration image includes multiple images obtained by capturing different sub-regions of the target calibration board at different positions of the camera in the two-dimensional imaging platform. The image compensation amount corresponding to the camera scanning error in the mapping relationship is calculated and used as the reference image correction amount.

12. A camera scanning error measuring device, characterized in that, include: The first imaging module is used to capture at least a portion of the target calibration board in a two-dimensional imaging platform using a camera to obtain a calibration image, wherein the feature points in the target calibration board are evenly distributed in rows and columns. The feature point extraction module is used to obtain target feature points in the calibration image, wherein the target feature points are located on the same straight line as the corresponding feature points in the target calibration plate. The curve generation module is used to obtain the curve expression of the curve where the target feature point is located based on the motion axis coordinates of the target feature point in the two-dimensional imaging platform. The error calculation module is used to determine the scanning deviation of the camera when capturing the calibration image on the two-dimensional imaging platform based on the components within the target frequency range corresponding to the curve expression.

13. An image correction device, characterized in that, include: The second shooting module is used to take pictures of at least a portion of the target object using a camera on a two-dimensional shooting platform to obtain a target image; An image correction module is used to correct the target image according to a reference image correction amount to obtain a corrected target image, wherein the reference image correction amount is determined based on the camera scanning error determined by the camera scanning error measurement method as described in any one of claims 1-8.

14. An electronic device comprising a processor and a memory, characterized in that, The memory stores computer program instructions, which, when executed by the processor, are used to perform the camera scanning error measurement method as described in any one of claims 1 to 8 and / or the image correction method as described in any one of claims 9 to 11.

15. A storage medium on which program instructions are stored, characterized in that, The program instructions, when executed, are used to perform the camera scanning error measurement method as described in any one of claims 1 to 8 and / or the image correction method as described in any one of claims 9 to 11.

16. A computer program product comprising computer program instructions, characterized in that, The computer program instructions, when executed, are used to perform the camera scanning error measurement method as described in any one of claims 1 to 8 and / or the image correction method as described in any one of claims 9 to 11.

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