Gray quantization method of simulation image and related device

Abstract

The application provides a gray quantization method of a simulation image and related products. The gray quantization method comprises the following steps: obtaining a detection mapping relationship between detection gray values and actual light intensity values at each position in a detection image, and a relative mapping relationship between simulation light intensity values and corrected light intensity values at each position in a simulation image; establishing a gray quantization model based on a preset relative relationship between the corrected light intensity values and the actual light intensity values, and in combination with the detection mapping relationship and the relative mapping relationship; and performing gray quantization processing on the simulation image according to the gray quantization model to obtain a simulation gray image. The method can effectively reduce the influence of physical conditions and extreme values of the whole image, avoid the gray range of the simulation gray image from being improperly compressed or stretched, reduce the problem of simulation image distortion, and achieve the purpose of improving the comparability of the simulation gray image and the detection image.

Description

Technical Field

[0001] This invention relates to the field of semiconductor photolithography simulation technology, and in particular to a method and related equipment for grayscale quantization of simulated images. Background Technology

[0002] In the field of semiconductor optical inspection, the comparative analysis of simulated images and inspected images (images obtained through charge-coupled devices (CCDs)) is a crucial step in process verification and defect detection. Among these, grayscale calibration of the simulated and inspected images is one of the key aspects ensuring the accuracy of the comparative analysis results. Currently, the commonly used grayscale normalization method in grayscale calibration is Global Linear Normalization (GLN), which directly uses the global maximum value iM and global minimum value im in the simulated image to linearly scale the entire simulated image. Among them, i s,g (x, y) represents the gray value at point (x, y) in the simulated grayscale image corresponding to the simulated image, i s,r (x, y) represents the simulated light intensity value at point (x, y) in the simulated image, i M and i m These represent the maximum and minimum relative values ​​of the simulated light intensity in the simulated image, respectively. In the above scheme, the maximum and minimum relative values ​​of the simulated light intensity are converted into grayscale values ​​of 255 and 0, respectively.

[0003] However, due to physical conditions and extreme values ​​of the entire image (such as noise and background drift), using global linear mapping can cause the grayscale range of the simulated grayscale image to be improperly compressed or stretched, leading to distortion. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a grayscale measurement method and related apparatus for simulated images that overcomes or at least partially solves the above problems.

[0005] One objective of this invention is to address the problem of distortion caused by improper compression or stretching of the grayscale range in simulated grayscale images, thereby improving the accuracy of simulated grayscale images and enhancing the comparability between simulated grayscale images and detection images.

[0006] Specifically, according to one aspect of the present invention, the present invention provides a grayscale measurement method for a simulated image, comprising: The detection mapping relationship between the detected grayscale value and the actual light intensity value in the detection image is obtained, and the relative mapping relationship between the simulated light intensity value and the corrected light intensity value at each location in the simulation image is obtained; wherein the simulation image is generated based on preset optical parameters, the detection image is generated based on the actual optical parameters corresponding to the preset optical parameters, and the corrected light intensity value is the light intensity value obtained after calibrating the simulated light intensity value based on the actual optical parameters. Based on the preset relative relationship between the corrected light intensity value and the actual light intensity value, and combined with the detection mapping relationship and the relative mapping relationship, a gray quantization model is established; wherein, the gray quantization model characterizes the mapping relationship between the simulated light intensity value and the simulated gray value at each location in the simulated image; The simulated image is subjected to grayscale quantization processing based on the grayscale quantization model to obtain a simulated grayscale image.

[0007] Optionally, obtaining the relative mapping relationship between the simulated light intensity value and the corrected light intensity value at various locations in the simulated image includes: Select one key region from the light-transmitting area and one light-blocking area of ​​the simulated image, and obtain the simulated light intensity value and the corrected light intensity value for each key region; A linear regression model is constructed between the simulated light intensity values ​​and the corrected light intensity values ​​of the two key regions, and the linear regression model is applied to the entire simulated image as the relative mapping relationship.

[0008] Optionally, obtaining the simulated light intensity value and the corrected light intensity value for each of the key regions includes: Obtain the simulated light intensity value of each key region and the detected grayscale value of the corresponding region of each key region in the detection image; The actual light intensity value corresponding to the detection gray value of each corresponding region is obtained by using the detection mapping relationship. By using the preset relative relationship, the corrected light intensity value of the key region corresponding to the actual light intensity value of each corresponding region is obtained.

[0009] Optionally, the detection mapping relationship between the detected grayscale values ​​and the actual light intensity values ​​at various locations in the detection image is obtained, including: During the generation of the detection image through the charge-coupled device (CCD), a detection grayscale value corresponding to a linearly increasing actual light intensity value is obtained to identify the response threshold of the CCD; wherein, when the actual light intensity value is greater than or equal to the response threshold, the corresponding detection grayscale value increases non-linearly; and when the actual light intensity value is less than the response threshold, the corresponding detection grayscale value increases linearly. A linear response model is obtained based on the actual light intensity value that is less than the response threshold and its corresponding detected gray value; A nonlinear response model is obtained based on the actual light intensity value that is greater than or equal to the response threshold and its corresponding detected gray value. The detection mapping relationship is formed by the linear response model and the nonlinear response model.

[0010] Optionally, after performing grayscale processing on the simulated image according to the grayscale model to obtain a simulated grayscale image, the step further includes: The simulated grayscale image is converted into a pixelated image corresponding to the pixels of the detected image; The pixelated image and the detected image are spatially registered at the subpixel level.

[0011] Optionally, subpixel-level spatial registration is performed between the pixelated image and the detected image, including: The pixelated image is displaced at the subpixel level according to multiple translation amounts to obtain a shifted image corresponding to each translation amount; wherein, for each pixel in each shifted image, the target gray value of each pixel is obtained by interpolating the gray value of the neighborhood region corresponding to the pixel center in the pixelated image. Based on the correlation analysis of mutual information, the target gray value of each pixel in each shifted image is selected that has the highest correlation with the gray value of each pixel in the detection image, and the image with the highest correlation is selected as the best registration image.

[0012] Optionally, converting the simulated grayscale image into a pixelated image corresponding to the pixels of the detected image includes: The simulated grayscale image is divided into multiple block-shaped discrete pixels, and the grayscale value of each discrete pixel is obtained by performing an integral approximation operation on the region corresponding to the pixel center of each discrete pixel in the simulated grayscale image. Each discrete pixel is mapped one-to-one with the pixels of the detected image to obtain the corresponding pixel of each discrete pixel and the pixelated image containing each corresponding pixel.

[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the steps of the grayscale measurement method for any of the above-described simulation images.

[0014] According to another aspect of the present invention, a computer program product is also provided, comprising a computer program that, when executed by a processor, implements the steps of the grayscale measurement method for any of the above-described simulation images.

[0015] According to another aspect of the present invention, a computer device is also provided, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the grayscale measurement method for any of the above-described simulation images.

[0016] The grayscale quantization method for simulated images of this invention obtains a relative mapping relationship between the simulated light intensity values ​​at various points in the simulated image and the corrected light intensity values ​​by comparing the detected grayscale values ​​at multiple points in the detected image with the corresponding simulated light intensity values ​​at multiple points in the simulated image. Then, a grayscale quantization model based on the detection mapping relationship is used to obtain the simulated grayscale image. This method effectively reduces the influence of physical conditions and extreme values ​​across the entire image, avoids improper compression or stretching of the grayscale range of the simulated grayscale image, reduces the problem of simulation image distortion, and achieves the goal of improving the comparability between the simulated grayscale image and the detected image.

[0017] Furthermore, the grayscale quantization method of the present invention significantly eliminates grayscale errors (such as edge blurring and peak shift) caused by pixel-level misalignment by performing sub-pixel-level affine transformation on the pixelated image, thereby improving detail matching accuracy and greatly enhancing spatial registration efficiency.

[0018] The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments of the invention in conjunction with the accompanying drawings. Attached Figure Description

[0019] The following sections will describe some specific embodiments of the invention in detail by way of example and not limitation, with reference to the accompanying drawings. The same reference numerals in the drawings denote the same or similar parts or portions. Those skilled in the art should understand that these drawings are not necessarily drawn to scale. In the drawings: Figure 1 This is a flowchart illustrating a method for grayscale measurement of a simulated image according to an embodiment of the present invention. Figure 2 This is a schematic flowchart illustrating the process of obtaining the detection mapping relationship using a gray quantization method according to an embodiment of the present invention. Figure 3 This is a schematic flowchart illustrating the process of obtaining relative mapping relationships using a gray quantization method according to an embodiment of the present invention. Figure 4 This is a schematic flowchart illustrating the gray quantization method according to an embodiment of the present invention for benchmarking simulated light intensity values; Figure 5 This is a schematic diagram of the process of pixelation and spatial registration of a simulated grayscale image according to an embodiment of the present invention. Figure 6This is a schematic flowchart of a grayscale image pixelation process according to an embodiment of the present invention. Figure 7 This is a schematic flowchart of a grayscale measurement method according to an embodiment of the present invention for spatial registration of a pixelated image. Figure 8 This is a schematic diagram of a computer program product according to an embodiment of the present invention; Figure 9 This is a schematic diagram of a computer-readable storage medium according to an embodiment of the present invention; and Figure 10 This is a schematic diagram of a computer device according to an embodiment of the present invention. Detailed Implementation

[0020] The purpose of the grayscale quantization method for simulated images in this embodiment is to solve the problem of distortion caused by improper compression or stretching of the grayscale range of simulated grayscale images, so as to improve the accuracy of simulated grayscale images and enhance the comparability between simulated grayscale images and detection images.

[0021] Figure 1 This is a flowchart illustrating a grayscale measurement method for a simulated image according to an embodiment of the present invention. The method generally includes: S100, acquire the detection mapping relationship between the detected gray value and the actual light intensity value in the detection image, and the relative mapping relationship between the simulated light intensity value and the corrected light intensity value at various points in the simulation image; wherein the simulation image is generated based on preset optical parameters, the detection image is generated based on the actual optical parameters corresponding to the preset optical parameters, and the corrected light intensity value is the light intensity value obtained after calibrating the simulated light intensity value based on the actual optical parameters. S200 establishes a grayscale quantization model based on the preset relative relationship between the corrected light intensity value and the actual light intensity value, combined with the detection mapping relationship and the relative mapping relationship; wherein, the grayscale quantization model characterizes the mapping relationship between the simulated light intensity value and the simulated grayscale value at each point in the simulated image; S300 performs grayscale processing on the simulation image according to the grayscale quantization model to obtain the simulation grayscale image.

[0022] In the process of optical simulation of a mask, a simulation model (e.g., a physical optics model) is used to calculate the exposure image of the mask pattern on a preset plane under preset optical parameters; this is the simulation image. Then, under experimental conditions, the mask is exposed using actual optical parameters identical to the preset optical parameters of the simulation model, and a detection image is acquired through a charge-coupled device (CCD). By comparing the simulation image and the detection image, reverse calculations are performed to improve the relevant parameters in the simulation model until the results of the simulation image and the detection image are consistent.

[0023] The detection image is also called a CCD image. Detection images are typically pixelated grayscale images, with the grayscale value of each pixel usually ranging from 0 to 255.

[0024] Since the simulated image obtained from the simulation model is a continuous distribution map of electric field intensity or light intensity, while the experimentally acquired detection image is a pixelated grayscale image, it is necessary to convert or map the simulated image into a pixelated grayscale image before comparing the two.

[0025] As described in the background section, existing technologies typically use global linear mapping (also known as direct extremum scaling) to linearly scale the electric field intensity or light intensity values ​​at each point in the simulated image to the range [0, 255]. Due to physical conditions and extreme values ​​of the entire image (such as noise and background drift), using global linear mapping can cause the grayscale range of the simulated image to be improperly compressed or stretched, leading to distortion.

[0026] In this embodiment, the actual light intensity value is the light intensity value incident on the charge-coupled device (CCD), and the detected grayscale value is the light intensity value output by the CCD after photoelectric conversion. The detected grayscale value is in the range [0, 255]. The detection mapping relationship between the detected grayscale value and the actual light intensity value reflects the physical characteristics of the CCD and can be obtained by collecting experimental data and fitting it.

[0027] The simulated light intensity value is the light intensity value calculated by the simulation algorithm based on the mask pattern and preset optical parameters. The simulated light intensity value is usually a continuous light intensity value. The corrected light intensity value is the light intensity value obtained by calibrating the simulated light intensity value using actual optical parameters. It is used to eliminate optical field parameter errors, improve the simulated image, and make the simulated image more accurately reflect the actual light intensity value. Specifically, when calibrating the simulated light intensity value using actual optical parameters, several sampling areas can be selected in the simulated image to obtain the simulated light intensity value at the center of each sampling area. Next, the detected grayscale value is obtained from the corresponding area of ​​the detection image, and the actual light intensity value corresponding to each detected grayscale value is calculated based on the detection mapping relationship between the detected grayscale value of the charge-coupled device and the actual light intensity value. Then, each calculated actual light intensity value is used as a corrected light intensity value, which serves as the benchmark for calibrating the simulated light intensity value at the center of each sampling area, and the simulated light intensity values ​​at various locations in the simulated image are then compared. Compared to related technologies that directly assign the maximum and minimum relative values ​​of relative light intensity to grayscale values ​​of 255 and 0 respectively, this scheme can effectively reduce the influence of physical conditions and extreme values ​​of the entire image (such as noise and background drift), prevent the grayscale range of the corrected light intensity value of the simulated image from being improperly compressed or stretched, and thus reduce the problem of distortion in the simulated grayscale image.

[0028] Preferably, the sampling regions may include regions in the simulated image that are near the upper limit of the simulated light intensity value and regions that are near the lower limit of the simulated light intensity value.

[0029] After obtaining the detection mapping relationship of the charge-coupled device and the relative mapping relationship between the simulated light intensity value and the corrected light intensity value at various points in the simulated image, a grayscale quantization model can be established. The grayscale quantization model is used to convert the simulated light intensity value at various points in the simulated image into a simulated grayscale value. The simulated grayscale value ranges from [0, 255]. After grayscale quantization processing by the grayscale quantization model, a simulated grayscale image that can be compared with the detected image is obtained.

[0030] The grayscale quantization method for simulated images of this invention obtains a relative mapping relationship between the simulated light intensity values ​​and the corrected light intensity values ​​at various points in the simulated image by comparing the detected grayscale values ​​at multiple points in the detected image with the corresponding simulated light intensity values ​​at multiple points in the simulated image. Then, a grayscale quantization model based on the detection mapping relationship is used to obtain the simulated grayscale image. This method effectively reduces the influence of physical conditions and extreme values ​​across the entire image, avoids improper compression or stretching of the grayscale range of the simulated grayscale image, reduces the problem of simulation image distortion, and achieves the goal of improving the comparability between the simulated grayscale image and the detected image.

[0031] In some embodiments of the gray measurement method of the present invention, such as Figure 2 As shown, the detection mapping relationship between the detected grayscale values ​​and the actual light intensity values ​​at various locations in the detection image is obtained, including: S111, during the process of generating a detection image through a charge-coupled device, a detection gray value corresponding to a linearly increasing actual light intensity value is obtained to identify the response threshold of the charge-coupled device; wherein, when the actual light intensity value is greater than or equal to the response threshold, the corresponding detection gray value increases non-linearly; and when the actual light intensity value is less than the response threshold, the corresponding detection gray value increases linearly. S113, obtain a linear response model based on the actual light intensity value less than the response threshold and its corresponding detected gray value; S115, based on the actual light intensity value that is greater than or equal to the response critical value and its corresponding detection gray value, a nonlinear response model is obtained; S117 uses a linear response model and a nonlinear response model to form a detection mapping relationship.

[0032] Due to the photoelectric conversion characteristics of charge-coupled devices (CCDs), the response of a CCD is not linear across the entire spectrum when generating a detection image. When the charge amount of a single pixel in a CCD approaches its full-well capacity, the photoelectric response enters a nonlinear region. The light intensity corresponding to the full-well capacity is called the response critical value. This is because the number of electrons exceeds the storage capacity of a single pixel, causing signal truncation or diffusion to adjacent pixels, forming a halo. During photoelectric conversion in the nonlinear response region of the CCD, the correspondence between the detected grayscale value and the actual light intensity value of the corresponding pixel in the detection image is also nonlinear. In the simulated grayscale image with global linear mapping, the correspondence between each pixel and its corresponding light intensity value is linear. This leads to data distortion in the light intensity value range corresponding to the nonlinear response region of the CCD, resulting in poor comparability of the simulated grayscale images obtained through grayscale quantification processing using global linear mapping, making quantitative analysis prone to bias.

[0033] In this embodiment, by gradually increasing the input light intensity through a stepped light intensity experiment, a curve of the input light intensity versus the output grayscale value of the charge-coupled device (CCD) can be plotted. The critical response value of the CCD can then be determined, dividing the signal into linear and nonlinear intervals. Response models for the linear and nonlinear response intervals are then fitted respectively. For example, the fitted detection mapping relationship can be expressed as: In the above formula, S is the output signal of the charge-coupled device (i.e., the detected grayscale value), I is the input signal (i.e., the actual light intensity value), g is the slope obtained by linear fitting, and g is usually proportional to the system gain. S0 is the baseline signal when there is no light, and I... c The critical value of the fitted response. For the sigmoid function, I M denoted as saturated light intensity, k is the transition slope obtained from the fitting, and k is used to reflect the steepness of the nonlinear interval curve. c is the offset position of the center point.

[0034] When the light intensity at a point is between 0 and the critical response value, an upward linear formula, i.e., a linear response model, can be used. When the light intensity at a point is greater than or equal to the critical response value, a downward nonlinear formula, i.e., a nonlinear response model, can be used.

[0035] It's important to understand that, in addition to the sigmoid function, other curve functions, such as polynomial fitting, can be used for nonlinear interval curves. The appropriate function can be selected based on the needs of the user, and will not be elaborated upon here.

[0036] By fitting the detection mapping relationship including the linear response range and the nonlinear response range, the simulated image can be accurately converted in the whole domain of light intensity, preventing the distortion of gray value in the converted simulated image due to the nonlinear photoelectric conversion characteristics of the charge-coupled device.

[0037] In some embodiments of the gray measurement method of the present invention, such as Figure 3 As shown, the relative mapping relationship between the simulated light intensity value and the corrected light intensity value at various locations in the simulated image is obtained, including: S121, Select one key region from the light-transmitting area and one light-blocking area of ​​the simulation image, and obtain the simulated light intensity value and the corrected light intensity value of each key region. S123, construct a linear regression model between the simulated light intensity value and the corrected light intensity value of two key regions, and apply the linear regression model to the entire simulated image as a relative mapping relationship.

[0038] In this embodiment, when calibrating the simulated light intensity value, key areas of the transmitted and shaded regions of the simulated image are used as sampling areas. The transmitted region can be the image area corresponding to a flat, high-transmittance area of ​​the mask, and the shaded region can be the image area corresponding to a flat, low-transmittance area of ​​the mask.

[0039] The simulated light intensity value i at the center of the light-transmitting area f1 The simulated light intensity value i at the center of the shaded area can be used as the maximum light intensity of the entire simulated image. f0 This can be used as the minimum light intensity of the entire simulated image. By obtaining the corrected light intensity values ​​of these two regions, and using the corrected light intensity values ​​to calibrate the simulated light intensity values, and then extending this to the entire simulated image, a relative mapping relationship between the simulated light intensity values ​​and the corrected light intensity values ​​of the entire simulated image can be obtained. This further improves the accuracy of the calibration and enhances the robustness of the relative mapping relationship.

[0040] In some embodiments of the gray measurement method of the present invention, such as Figure 4 As shown, the simulated light intensity value and corrected light intensity value of each key region are obtained, including: S131, obtain the simulated light intensity value of each key region and the detection gray value of the corresponding region in the detection image for each key region; S133, by detecting the mapping relationship, the actual light intensity value corresponding to the detected gray value of each corresponding area is obtained; S135, by pre-setting relative relationships, obtains the corrected light intensity value of the key area corresponding to the actual light intensity value of each corresponding area.

[0041] In this embodiment, the simulated light intensity value i at the center of the light-transmitting area is obtained using the detected image. f1 The simulated light intensity value i at the center of the shading area f0 The corresponding correction light intensity values ​​are as follows. Specifically, the detection grayscale value of the pixel corresponding to the center of the light-transmitting area in the experimentally acquired detection image can be read as the benchmark grayscale value S. fiAnd the detected grayscale value of the pixel corresponding to the center of the shading area in the detection image, as the benchmark grayscale value S. f0 Next, by detecting the mapping relationship, the corresponding grayscale value S is derived. fi The corresponding actual light intensity value I f1 And the benchmark grayscale value S f0 The corresponding actual light intensity value I f0 The preset relative relationship describes the equivalence between the actual light intensity value and the corrected light intensity value of one or more sampling areas. It is used to calibrate the simulated light intensity value based on the preset optical parameters using the actual light intensity value obtained based on the actual optical parameters. Specifically, in this embodiment, the preset relative relationship means that the actual light intensity value of each key area is used as the corrected light intensity value to achieve the calibration of the simulated light intensity value.

[0042] For example, based on a linear regression model, the corrected light intensity value can be expressed as: In the above formula, i s,r (x, y) represents the simulated light intensity value at point (x, y) in the simulated image. The above formula not only describes the mapping relationship between the simulated light intensity value and the corrected light intensity value in each key region, but also describes the mapping relationship (i.e., the relative mapping relationship) between the simulated light intensity value and the corrected light intensity value in the entire simulated image.

[0043] For example, after determining the corrected light intensity values ​​at various locations in the simulated image, and combining this with the detection mapping relationship, the gray quantification model can be expressed in the following form: In the above formula, i s,g (x, y) represents the simulated grayscale value at point (x, y) in the simulated grayscale image. The actual critical value i obtained after calibration... c It can be represented as: By using the detected grayscale values ​​of corresponding regions in the detected image to benchmark the simulated light intensity values ​​of key regions in the transparent and shaded areas of the simulated image, the accuracy of the benchmarking is further improved, and the robustness of the relative mapping relationship is enhanced.

[0044] In some embodiments of the gray measurement method of the present invention, such as Figure 5 As shown, after performing grayscale processing on the simulated image according to the grayscale model to obtain the simulated grayscale image, the following steps are also included: S311 converts the simulated grayscale image into a pixelated image corresponding to the pixels of the detected image; S313 performs sub-pixel-level spatial registration between the pixelated image and the detection image.

[0045] The grayscale quantization model is based on an optical model of a continuous physical field. The density of the simulated grayscale value dataset obtained from the simulated grayscale image is much higher than the resolution of the detection image, and the simulated grayscale image contains continuous simulated grayscale values ​​throughout. In order to compare the simulated grayscale image with the pixelated detection image, it is also necessary to pixelate the simulated grayscale image and spatially register the pixelated image with the detection image.

[0046] In the process of pixelating a simulated grayscale image into a pixelated image, the simulated grayscale image can be divided into multiple block-shaped discrete pixels (e.g., rectangular blocks). Then, the grayscale value of the discrete pixel is determined based on the corrected light intensity value at each point within the discrete pixel block. For example, the simulated grayscale value at the center of the discrete pixel block can be used as the grayscale value of the discrete pixel, or the average simulated grayscale value of the discrete pixel block can be used as the grayscale value of the discrete pixel. The appropriate calculation method can be selected as needed.

[0047] In the process of spatially registering the pixelated image with the detection image, subpixel resampling can be used to perform pixel translation on the pixelated image. For example, subpixel-level translations with effective accuracy can be iterated, and a full-pixel integral approximation formula can be applied to each translation to obtain multiple pixelated images. Then, the one with the highest matching degree to the detection image is determined as the final simulation image. For example, interpolation algorithms with dynamic Lanczos kernel function cutoff range, Lanczos-4 interpolation algorithms with fixed Lanczos kernel function cutoff range, and bicubic interpolation algorithms can also be used to achieve subpixel-level affine transformation of the pixelated image. Then, among the multiple pixel-translated pixelated images, the one with the best registration with the detection image is determined, obtaining the target grayscale value of each pixel in the pixelated image.

[0048] In some embodiments of the gray measurement method of the present invention, such as Figure 6 As shown, converting the simulated grayscale image into a pixelated image corresponding to the pixels of the detected image includes: S321, the simulated grayscale image is divided into multiple block-shaped discrete pixels, and the grayscale value of each discrete pixel is obtained by performing an integral approximation operation on the region corresponding to the pixel center of each discrete pixel in the simulated grayscale image. S323, each discrete pixel is mapped one-to-one with the pixels in the detection image to obtain the corresponding pixel for each discrete pixel and a pixelated image containing each corresponding pixel.

[0049] In this embodiment, local integral approximation is used to determine the grayscale value of each pixel in the pixelated image. Specifically, the simulated grayscale image can first be pixelated to obtain multiple discrete pixel blocks corresponding to the pixels of the detection image. Then, an integral approximation operation is performed on the simulated grayscale value data within each discrete pixel block to quickly obtain the grayscale value of that discrete pixel block. For example, the following integral approximation formula can be used for calculation: In the above formula, I s,g (x, y) is the grayscale value of a discrete pixel block centered at (x, y), corresponding to the grayscale value of a single pixel in the detected image. Ω is the discrete pixel block centered at (x, y) in the simulated grayscale image, i s,g (u, v) is the simulated grayscale value of the point with coordinates (u, v) in Ω, and w is the preset weight function.

[0050] The grayscale values ​​of each discrete pixel obtained by integral approximation can accurately reflect the overall simulated grayscale values ​​within each discrete pixel block, thereby improving the fidelity of the simulated grayscale image.

[0051] In some embodiments of the gray quantization method of the present invention, the integral approximation operation is based on the local intensity integral operation of a weighted Gaussian kernel function. Specifically, the preset weight function can use the following Gaussian kernel: In the above formula, r is the distance between the point with coordinates (u, v) and the point with coordinates (x, y). By fusing the Gaussian distribution, the accuracy of the integral approximation calculation can be improved, making the gray values ​​of each pixel in the pixelated image more accurately reflect the light intensity of its local area as a whole.

[0052] In some embodiments of the gray measurement method of the present invention, such as Figure 7 As shown, sub-pixel spatial registration is performed between the pixelated image and the detection image, including: S331, perform sub-pixel level displacement on the pixelated image according to multiple translation amounts to obtain the shifted image corresponding to each translation amount; wherein, for each pixel in each shifted image, the target gray value of each pixel is obtained by interpolating the gray value of the neighborhood region corresponding to the pixel center in the pixelated image. S333, based on the correlation analysis of mutual information, selects the target gray value of each pixel in each shifted image that has the highest correlation with the gray value of each pixel in the detection image, and uses the one with the highest correlation as the best registration image.

[0053] In a pixelated image, the centers of individual pixels may be offset from those of the pixels in the detection image. Directly comparing the pixelated image with the detection image can lead to inaccurate results. Generally, an offset of ±0.5 pixels from the center of each discrete pixel block relative to the center of a pixel in the detection image can cause changes in the integration region, resulting in grayscale value differences of up to 10% to 30%. Therefore, after obtaining the pixelated image, it is necessary to align it with the detection image.

[0054] Determining the simulation image with the highest matching degree to the detection image by traversing subpixel-level displacements with effective precision would incur a huge computational burden, resulting in low spatial registration efficiency in practical applications. In this embodiment, an interpolation algorithm with a dynamic Lanczos kernel function truncation range is used to perform subpixel-level affine transformation on the pixelated image to further improve spatial registration efficiency. Specifically, during the affine transformation, the initial pixelated image can be subpixel resampled using the following formula: In the above formula, I' s,g (a, b) are I in the pixelated image s,g (x, y) represents the grayscale value after displacement (a, b), where (a, b) is the displacement of the subpixel resampling center relative to the original pixel center in the pixelated image. (i, j) represents the integer pixel coordinates within the neighborhood of the subpixel resampling center, where... , , where N is the cutoff range of the Lanczos kernel function. q a and q b These are the Lanczos-N kernel weights in the x-axis and y-axis directions, respectively. , The Lanczos kernel L(x) consists of the sinc function and the Lanczos window: in, .

[0055] In this embodiment, different displacement amounts (a, b) can be set to obtain multiple deformed pixelated images, which are then compared with the experimental optical image. Finally, based on the mutual information evaluation of pixel grayscale, the one that is best aligned with the detection image among the deformed pixelated images is selected as the final simulation image.

[0056] By performing subpixel-level affine transformation based on interpolation on the initial pixelated image, grayscale errors (such as edge blurring and peak shift) caused by pixel-level misalignment can be significantly eliminated, improving detail matching accuracy and greatly increasing spatial registration efficiency. This allows for the rapid acquisition of a deformed pixelated image that is best aligned with the detection image.

[0057] In some embodiments of the gray quantization method of the present invention, the interpolation operation is based on the dynamic adaptive interpolation operation of the Lanczos-N kernel. The truncation parameter N of the Lanczos-N kernel is dynamically adjusted according to the local spectral characteristics of the image. The local spectral characteristics of the image are estimated by DCT (Discrete Cosine Transform) or FFT (Fast Fourier Transform) or gradient analysis to determine the adjustment value of the truncation parameter N.

[0058] The dynamic value of the Lanczos kernel function cutoff range N depends on the local frequency feature f. In this embodiment, the value of N can be dynamically determined based on the local frequency feature f by estimation through DCT, FFT, or gradient analysis, thereby improving the accuracy of the affine transformation.

[0059] In this embodiment, a smaller N can be used in the high-frequency region to maintain edge sharpness. A smaller N can be used in the low-frequency region to suppress ringing effects. For example, the value of N can be determined using the following formula: In the above formula, f M and f m These are the maximum and minimum local frequencies in the initial pixelated image, respectively.

[0060] The flowchart provided in this embodiment is not intended to indicate that the operations of the method will be performed in any particular order, or that all operations of the method are included in every case. Furthermore, the method may include additional operations. Within the scope of the technical concept provided by the method in this embodiment, additional variations can be made to the above method.

[0061] It should be understood that in some embodiments, the components may be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods may be implemented using software or firmware stored in memory and executed by a suitable instruction execution system.

[0062] This invention also provides a computer program product 10, a computer-readable storage medium 20, and a computer device 30. Figure 8 This is a schematic diagram of a computer program product 10 according to an embodiment of the present invention. Figure 9 This is a schematic diagram of a computer-readable storage medium 20 according to an embodiment of the present invention. Figure 10This is a schematic diagram of a computer device 30 according to an embodiment of the present invention. The computer program product 10 includes a computer program 11, which, when executed by the processor 32, implements the steps of the grayscale measurement method for the simulated image described above. A computer-readable storage medium 20 stores the computer program 11 thereon, which, when executed by the processor 32, implements the steps of the grayscale measurement method for the simulated image described above. The computer device 30 may include a memory 31, a processor 32, and the computer program 11 stored in the memory 31 and running on the processor 32.

[0063] The computer program 11 used to perform the operations of this invention may be assembly instructions, Instruction Set Architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, integrated circuit configuration data, or source code or object code written in any combination of one or more programming languages ​​and procedural programming languages. The computer program 11 may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer may be connected to the user's computer via any type of network, including a Local Area Network (LAN) or Wide Area Network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, to perform aspects of this invention, electronic circuits, including, for example, programmable logic circuits, Field-Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), may execute computer-readable program instructions to personalize the electronic circuits by utilizing state information from computer-readable program instructions.

[0064] For the purposes of this embodiment, computer program product 10 is a related product that includes computer program 11.

[0065] For the purposes of this embodiment, the computer-readable storage medium 20 is a tangible device capable of holding and storing a computer program 11. It can be any device capable of containing, storing, communicating, propagating, or transmitting the computer program 11 for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable storage medium 20 include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable optical disc read-only memory (CD-ROM), digital versatile disc (DVD), memory stick, floppy disk, mechanical encoding device, and any suitable combination thereof.

[0066] Computer device 30 can be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone. In some examples, computer device 30 can be a cloud computing node. Computer device 30 can be described in the general context of computer system executable instructions (such as program modules) executed by a computer system. Typically, program modules can include routines, programs, object programs, components, logic, data structures, etc., that perform specific tasks or implement specific abstract data types. Computer device 30 can be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices linked through a communication network. In a distributed cloud computing environment, program modules can reside on local or remote computing system storage media, including storage devices.

[0067] Computer device 30 may include a processor 32 adapted to execute stored instructions and a memory 31 that provides temporary storage space for the operation of said instructions during operation. The processor 32 may be a single-core processor, a multi-core processor, a computing cluster, or any other configuration. The memory 31 may include random access memory (RAM), read-only memory, flash memory, or any other suitable storage system.

[0068] Computer device 30 may also include a network adapter / interface and an input / output (I / O) interface. The I / O interface allows external devices that can be connected to the computer device to input and output data. The network adapter / interface provides communication between the computer device and a network, typically represented as a communication network.

[0069] Therefore, those skilled in the art should recognize that although numerous exemplary embodiments of the present invention have been shown and described in detail herein, many other variations or modifications conforming to the principles of the present invention can be directly determined or derived from the disclosure of the present invention without departing from the spirit and scope of the invention. Thus, the scope of the present invention should be understood and construed as covering all such other variations or modifications.

Claims

1. A method for grayscale measurement of a simulated image, characterized in that, include: Obtain the detection mapping relationship between the detected grayscale value and the actual light intensity value in the detection image, and the relative mapping relationship between the simulated light intensity value and the corrected light intensity value at various locations in the simulation image; The simulated image is generated based on preset optical parameters, the detected image is generated based on actual optical parameters corresponding to the preset optical parameters, and the corrected light intensity value is the light intensity value obtained after calibrating the simulated light intensity value based on the actual optical parameters. Based on the preset relative relationship between the corrected light intensity value and the actual light intensity value, and combined with the detection mapping relationship and the relative mapping relationship, a gray quantization model is established; wherein, the gray quantization model characterizes the mapping relationship between the simulated light intensity value and the simulated gray value at each location in the simulated image; The simulated image is subjected to grayscale quantization processing according to the grayscale quantization model to obtain a simulated grayscale image; Obtaining the relative mapping relationship between the simulated light intensity value and the corrected light intensity value at various locations in the simulated image includes: Select one key region from the light-transmitting area and one light-blocking area of ​​the simulated image, and obtain the simulated light intensity value and the corrected light intensity value for each key region; A linear regression model is constructed between the simulated light intensity values ​​and the corrected light intensity values ​​of the two key regions, and the linear regression model is applied to the entire simulated image as the relative mapping relationship. The detection mapping relationship is expressed as follows: ， In the above formula, S is the detected gray value, I is the actual light intensity value, g is the slope, S0 is the baseline signal, and I c For the response critical value, σ is the sigmoid function, I M denoted as saturation light intensity, k as transition slope, and c as center point offset. The preset relative relationship means that the actual light intensity value of each key area is used as the correction light intensity value; The gray quantification model is expressed as follows: ， In the above formula, i s,g (x, y) represents the simulated grayscale value at point (x, y) in the simulated grayscale image, i s,r (x, y) represents the simulated light intensity value at point (x, y) in the simulated image, I f0 I f1 i represents the actual light intensity value. f0 i f1 To simulate light intensity values, i c This is the actual critical value.

2. The gray measurement method according to claim 1, characterized in that, Obtain the simulated light intensity value and the corrected light intensity value for each of the key regions, including: Obtain the simulated light intensity value of each key region and the detected grayscale value of the corresponding region of each key region in the detection image; The actual light intensity value corresponding to the detection gray value of each corresponding region is obtained by using the detection mapping relationship. By using the preset relative relationship, the corrected light intensity value of the key region corresponding to the actual light intensity value of each corresponding region is obtained.

3. The gray measurement method according to claim 1, characterized in that, Obtain the detection mapping relationship between the detected grayscale values ​​and the actual light intensity values ​​at various locations in the detection image, including: During the generation of the detection image through the charge-coupled device (CCD), a detection grayscale value corresponding to a linearly increasing actual light intensity value is obtained to identify the response threshold of the CCD; wherein, when the actual light intensity value is greater than or equal to the response threshold, the corresponding detection grayscale value increases non-linearly; and when the actual light intensity value is less than the response threshold, the corresponding detection grayscale value increases linearly. A linear response model is obtained based on the actual light intensity value that is less than the response threshold and its corresponding detected gray value; A nonlinear response model is obtained based on the actual light intensity value that is greater than or equal to the response threshold and its corresponding detected gray value. The detection mapping relationship is formed by the linear response model and the nonlinear response model.

4. The gray measurement method according to claim 1, characterized in that, The steps following the grayscale processing of the simulated image according to the grayscale model to obtain the simulated grayscale image further include: The simulated grayscale image is converted into a pixelated image corresponding to the pixels of the detected image; The pixelated image and the detected image are spatially registered at the subpixel level.

5. The gray measurement method according to claim 4, characterized in that, Subpixel-level spatial registration is performed between the pixelated image and the detected image, including: The pixelated image is displaced at the subpixel level according to multiple translation amounts to obtain a shifted image corresponding to each translation amount; wherein, for each pixel in each shifted image, the target gray value of each pixel is obtained by interpolating the gray value of the neighborhood region corresponding to the pixel center in the pixelated image. Based on the correlation analysis of mutual information, the target gray value of each pixel in each shifted image is selected that has the highest correlation with the gray value of each pixel in the detection image, and the image with the highest correlation is selected as the best registration image.

6. The gray measurement method according to claim 4, characterized in that, Converting the simulated grayscale image into a pixelated image corresponding to the pixels of the detected image includes: The simulated grayscale image is divided into multiple block-shaped discrete pixels, and the grayscale value of each discrete pixel is obtained by performing an integral approximation operation on the region corresponding to the pixel center of each discrete pixel in the simulated grayscale image. Each discrete pixel is mapped one-to-one with the pixels of the detected image to obtain the corresponding pixel of each discrete pixel and the pixelated image containing each corresponding pixel.

7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the gray quantization method as described in any one of claims 1 to 6.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the gray quantization method as described in any one of claims 1 to 6.

9. A computer device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the gray quantization method according to any one of claims 1 to 6.

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