Electronic vernier caliper alignment method based on area array detector pixels

Abstract

The present application relates to the field of precision visual measurement and alignment technology, and particularly relates to an electronic vernier caliper alignment method based on a pixel of a surface array detector, comprising the following steps: S1, designing a mark plate according to the optical field of view and the pixel size of the surface array detector; S2, collecting an image and positioning a coarse alignment mark through a geometric alignment method to obtain a reference grid position; S3, performing a gray scale processing on a grid area to generate a gray scale distribution; S4, performing a definition analysis based on the gray scale distribution to determine a target grid position; S5, comparing the position difference between the target grid and the reference grid to calculate row and column offsets; and S6, performing a statistics on a plurality of offset results to obtain an overall offset amount. The present application constructs a vernier caliper alignment mechanism based on the period difference between the pixel of the surface array detector and the mark grid, and realizes a fast alignment effect with high precision, low computing power, low cost and strong adaptability without the need of complex calculation and a multi-point structure.

Description

Technical Field

[0001] This invention relates to the field of precision visual measurement and alignment technology, and in particular to an electronic vernier caliper alignment method based on area array detector pixels. Background Technology

[0002] With the increasing demands for position measurement accuracy in precision manufacturing and micro / nano assembly, non-contact alignment methods based on vision imaging are gradually becoming an important component of high-precision measurement systems. One of the widely adopted basic approaches to vision alignment is to image a marked pattern using an area array detector, and then extract image features and calculate deviations to achieve rapid identification and adjustment of the target position. This is particularly relevant in scenarios such as semiconductor exposure, precision mounting, and robotic assembly, where even higher requirements are placed on image alignment accuracy, stability, and system response speed.

[0003] Existing image alignment methods mainly include machine vision methods based on pattern recognition and moiré fringe methods based on periodic interference. The former is highly sensitive to the sharpness of the alignment marks and lighting conditions, and its recognition accuracy is easily reduced when the marks are blurred, contaminated, or the environment changes. The latter relies on the physical overlap of two sets of high-precision grating structures and derives the positional offset through the phase change of the fringes. Although it can achieve nanometer-level alignment, it has extremely high requirements for system structure and installation environment, and the calculation process is complex, the solution speed is slow, and there are limitations such as phase ambiguity, making it difficult to meet the engineering application requirements of high precision and high adaptability. Summary of the Invention

[0004] This invention provides an electronic vernier caliper alignment method based on area array detector pixels. By naturally forming grayscale modulation fringes during imaging and combining this with modulation transfer function (MTF) analysis to extract the optimal position for fringe sharpness, an alignment mechanism that requires no physical overlap and can be rapidly calculated is constructed. This method achieves measurement accuracy comparable to the moiré fringe method while significantly reducing system complexity and environmental dependence, making it suitable for various high-precision, high-reliability real-time alignment scenarios.

[0005] An electronic vernier caliper alignment method based on area array detector pixels includes the following steps: S1. Design a marking plate based on the corresponding optical field size and pixel size of the area array detector; S2: Acquire images output by the array detector, and use a geometric image alignment method to identify and locate the coarse alignment marks in the marker plate, extracting the center coordinates of the geometric image. , Record them as reference coordinates; S3, Process the grid grayscale information of the grid region in the image to extract the grayscale distribution map; S4. Based on the grayscale distribution of each grid in the image, perform sharpness analysis and identify the target grid that is closest to the current alignment state as the actual imaging alignment position. S5: Compare the relative position differences between the target raster and the reference raster in the image, and calculate the corresponding row and column distances to reflect the actual offset. S6, calculate the offset value based on the statistical results of multiple relative position differences.

[0006] Optionally, the pattern size of the marker plate is within the field of view of the area array detector.

[0007] Optionally, the coarse alignment mark of the marking plate is a cross mark designed in the center, and the coarse alignment mark is surrounded by a grid with the same width and spacing.

[0008] Optionally, S2 includes: S21, using a geometric image alignment method, coarsely align with the crosshair in the image; S22, Locate the position of the crosshair in the image and save. , ; S23, divide the crosshair markers and grids into regions, grouping the grids closest to the crosshair markers together, and binding the center image metadata to the corresponding group of grids. For grids distributed along the row direction, use the reference pixel position. A grid distributed along the column direction, using reference pixel positions. ...

[0009] Optionally, the geometric image alignment method employs a template matching method.

[0010] Optionally, the grid region in S3 is imaged as bright and dark stripes of uniform width in the area array detector.

[0011] Optionally, S4 includes: S41, calculate the gray value of the pixel corresponding to each group of raster cells. If it is a bright stripe, then it is judged to be a bright stripe. The current pixel value. The left pixel value, The pixel value is on the right. S42, calculate the raster data of the same group using the modulation and demodulation function MTF, expressed as: ; ; S43 records the pixel raster with the highest MTF value in the grayscale information distribution ladder map. This pixel raster has the best sharpness and is denoted as the target raster. ,in Indicating the first group of grid areas, in The pixel position of the current raster with the highest clarity in the direction; S44, extract the target raster position in the row direction and record its pixel coordinates. Extract the target raster position along the column direction and record its pixel coordinates. .

[0012] Optionally, the relative positional difference between the target grid and the reference grid in the image in S5 is represented as follows: ; in, Due to differences in relative position, , These represent the positions of the target raster and the reference raster, respectively. This represents the raster period.

[0013] Optionally, the offset value in S6 includes the overall position offset in the x-axis direction. and the overall positional offset in the y-axis direction , is represented as: ; ; in, This represents the number of raster marker groups involved in the calculation.

[0014] The beneficial effects of this invention are: This invention, by treating the pixel array of the area detector as an equivalent periodic grid and designing a marker plate grid structure with a specific period difference, achieves high-sensitivity alignment discrimination without the need for physically overlapping grids. Combined with geometric pattern coarse alignment and MTF sharpness analysis mechanism, it can accurately extract the position information of the reference grid and the target grid, and finally obtain sub-pixel level alignment accuracy comparable to the moiré fringe alignment method, effectively meeting the real-time alignment requirements of high-precision assembly or positioning systems.

[0015] This invention simplifies the complex phase calculation, Fourier analysis, and iterative optimization processes relied upon by traditional alignment algorithms, and constructs a fast MTF judgment mechanism based on grayscale value jump characteristics. This significantly reduces the system's requirements for computing resources and processor computing power, improves overall alignment efficiency, and is suitable for embedded devices or edge computing platforms with tight operating cycles and limited computing resources.

[0016] This invention achieves alignment by utilizing an array detector and a single marker plate, eliminating the traditional three-point alignment structure, simplifying the physical installation and calibration requirements of multiple marking and positioning devices, significantly reducing system components, lowering marker plate manufacturing and replacement costs, and improving adaptability to mechanical platform structures, thus enabling the method to have wider industrial scenario adaptability and deployment flexibility. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0018] Figure 1 This is a schematic diagram of the alignment method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a single-group marker design according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the application structure of the electronic vernier caliper principle in an image measurement system according to an embodiment of the present invention, wherein 1 is an alignment array detector, 2 is a marking plate, and 3 is a motion mechanical stage. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Those skilled in the art may employ other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0020] like Figures 1-3 As shown, an electronic vernier caliper alignment method based on area array detector pixels includes the following steps: S1. Design a marking plate based on the corresponding optical field size and pixel size of the area array detector; S2: Acquire images output by the array detector, and use a geometric image alignment method to identify and locate the coarse alignment marks in the marker plate, extracting the center coordinates of the geometric image. , Record them as reference coordinates; S3, Process the grid grayscale information of the grid region in the image to extract the grayscale distribution map; S4. Based on the grayscale distribution of each grid in the image, perform sharpness analysis and identify the target grid that is closest to the current alignment state as the actual imaging alignment position. S5: Compare the relative position differences between the target raster and the reference raster in the image, and calculate the corresponding row and column distances to reflect the actual offset. S6, calculate the offset value based on the statistical results of multiple relative position differences.

[0021] The size of the pattern on the marker board is within the field of view of the area array detector, meaning that all the markers set can be seen in the image of the area array detector.

[0022] The coarse alignment mark on the marker plate is a cross mark designed in the center. Its function is to determine the fixed intermediate grid position, that is, the alignment reference point. The coarse alignment mark is surrounded by a grid. The width and spacing of the grid are the same, with a value of T, which is slightly smaller or larger than the pixel size of the area array detector.

[0023] Assuming the area array detector and optical parameters remain unchanged, the designed marking plate can be used indefinitely.

[0024] Using a grayscale area array detector allows for the direct output of the original grayscale image from the area array detector.

[0025] S2 includes: S21, using a geometric image alignment method, coarsely align with the crosshair in the image; S22, Locate the position of the crosshair in the image and save. , ; S23, divide the crosshair markers and grids into regions, grouping the grids closest to the crosshair markers together, and binding the center image metadata to the corresponding group of grids. For grids distributed along the row direction, use the reference pixel position. A grid distributed along the column direction, using reference pixel positions. .

[0026] The geometric image alignment method uses template matching.

[0027] The crosshairs do not need to be aligned with the center of the field of view of the array detector, making them more flexible in use.

[0028] The field of view of an array detector can contain more than one crosshair, with the number ranging from 1 to n, i.e., multiple rows and columns, and at least four sets of grid data participating in the calculation; n is determined by the relationship between the size of the crosshair and the size of the field of view.

[0029] The grid area in S3 is imaged as bright and dark stripes of uniform width in the area array detector.

[0030] S4 includes: S41, calculate the gray value of the pixel corresponding to each group of raster cells. If it is a bright stripe, then it is judged to be a bright stripe. The current pixel value. The left pixel value, The pixel value is on the right. S42, calculate the raster data of the same group using the modulation and demodulation function MTF, expressed as: ; ; S43 records the pixel raster with the highest MTF value in the grayscale information distribution ladder map. This pixel raster has the best sharpness and is denoted as the target raster. ,in Indicating the first group of grid areas, in The pixel position of the current raster with the highest clarity in the direction; S44, extract the target raster position in the row direction and record its pixel coordinates. Extract the target raster position along the column direction and record its pixel coordinates. .

[0031] The relative positional difference between the target raster and the reference raster in the image in S5 is represented as follows: ; in, Due to differences in relative position, , These represent the positions of the target raster and the reference raster, respectively. The grid period is represented by the same subscripts for D, C, and S.

[0032] The offset value in S6 includes the overall position offset in the x-axis direction. and the overall positional offset in the y-axis direction , is represented as: ; ; in, This represents the number of raster marker groups involved in the calculation.

[0033] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0034] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for aligning an electronic vernier caliper based on a pixel of an area array detector, characterized in that, Includes the following steps: S1. Design a marking plate based on the corresponding optical field size and pixel size of the area array detector; S2: Acquire images output by the array detector, and use a geometric image alignment method to identify and locate the coarse alignment marks in the marker plate, extracting the center coordinates of the geometric image. , Record them as reference coordinates; S3, Process the grid grayscale information of the grid region in the image to extract the grayscale distribution map; S4. Based on the grayscale distribution of each grid in the image, perform sharpness analysis and identify the target grid that is closest to the current alignment state as the actual imaging alignment position. S5: Compare the relative position differences between the target raster and the reference raster in the image, and calculate the corresponding row and column distances to reflect the actual offset. S6, calculate the offset value based on the statistical results of multiple relative position differences.

2. The electronic vernier caliper alignment method based on area array detector pixels according to claim 1, characterized in that, The size of the pattern on the marker plate is within the field of view of the area array detector.

3. The electronic vernier caliper alignment method based on area array detector pixels according to claim 2, characterized in that, The coarse alignment mark of the marking plate is a cross mark designed in the center, and the coarse alignment mark is surrounded by a grid with the same width and spacing.

4. The electronic vernier caliper alignment method based on area array detector pixels according to claim 3, characterized in that, S2 includes: S21, using a geometric image alignment method, coarsely align with the crosshair in the image; S22, Locate the position of the crosshair in the image and save. , ; S23, divide the crosshair markers and grids into regions, grouping the grids closest to the crosshair markers together, and binding the center image metadata to the corresponding group of grids. For grids distributed along the row direction, use the reference pixel position. A grid distributed along the column direction, using reference pixel positions. ...

5. The electronic vernier caliper alignment method based on area array detector pixels according to claim 4, characterized in that, The geometric image alignment method employs template matching.

6. The electronic vernier caliper alignment method based on area array detector pixels according to claim 1, characterized in that, The grid region in S3 is imaged as bright and dark stripes of uniform width in the area array detector.

7. The method for aligning an electronic vernier caliper based on a planar array detector pixel as described in claim 1, characterized in that, S4 includes: S41, calculate the gray value of the pixel corresponding to each group of raster cells. If it is a bright stripe, then it is judged to be a bright stripe. The current pixel value. The left pixel value, The pixel value is on the right. S42, calculate the raster data of the same group using the modulation and demodulation function MTF, expressed as: ； ； S43 records the pixel raster with the highest MTF value in the grayscale information distribution ladder map. This pixel raster has the best sharpness and is denoted as the target raster. ,in Indicating the first group of grid areas, in The pixel position of the current raster with the highest clarity in the direction; S44, extract the target raster position in the row direction and record its pixel coordinates. Extract the target raster position along the column direction and record its pixel coordinates. .

8. The method for aligning an electronic vernier caliper based on a planar array detector pixel as described in claim 1, characterized in that, The relative positional difference between the target grid and the reference grid in the image in S5 is represented as follows: ； in, Due to differences in relative position, , These represent the positions of the target raster and the reference raster, respectively. This represents the raster period.

9. The electronic vernier caliper alignment method based on area array detector pixels according to claim 1, characterized in that, The offset value in S6 includes the overall position offset in the x-axis direction. and the overall positional offset in the y-axis direction , is represented as: ； ； in, This represents the number of raster marker groups involved in the calculation.

Images