Method and system for calibrating mobile diaphragm of electronic optical system

Abstract

The invention relates to the technical field of electron optics, in particular to an electron optical system mobile diaphragm calibration method, system and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: in response to a diaphragm calibration request for target equipment, obtaining coordinate data of a preset detection point and corresponding electron beam intensity data; preprocessing the coordinate data and the electron beam intensity data, and generating a beam intensity distribution diagram based on the preprocessed coordinate data and electron beam intensity data; processing the beam intensity distribution diagram based on a preset image processing algorithm, and fitting to obtain geometric center coordinates of a diaphragm hole of the diaphragm to be calibrated; and executing diaphragm calibration on the target equipment by taking the geometric center coordinate as a calibration position of the corresponding diaphragm hole. By adopting the method, the diaphragm calibration efficiency and precision can be improved, and the machine efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of electronic optics technology, and in particular to a method, system, apparatus, computer equipment, storage medium, and computer program product for calibrating a moving aperture in an electronic optics system. Background Technology

[0002] In many cutting-edge technology fields such as micro-nano fabrication, surface analysis, and high-resolution microscopic imaging, electron optics systems are key devices for realizing their core functions. The basic principle of electron optics systems is similar to that of geometric optics systems, but the object of their interaction is not visible photons, but rather an electron beam. This system uses carefully designed electrostatic or electromagnetic fields to exert forces on moving electrons, thereby achieving the deflection, focusing, and imaging of the electron beam. Its function is similar to a lens group in an optical system; therefore, these field components are often called "electromagnetic lenses."

[0003] In electron optics systems, the aperture stop is a crucial yet often overlooked fundamental component. Its function is analogous to that of the aperture stop in optical systems, with core functions including limiting the aperture angle of the electron beam, selecting the energy of the electron beam, and improving system aberrations. Specifically, an aperture stop is typically a thin metal sheet with tiny through-holes, precisely positioned on a specific focal plane or image plane along the electron beam path. By limiting the scattering angle of the electron beam, the aperture stop effectively eliminates electrons that cause aberrations (such as spherical aberration) due to excessively large scattering angles, thus significantly improving the resolution and quality of the final image. In applications requiring monoenergetic electron beams, energy dispersive apertures can block electrons that have lost some energy due to inelastic scattering, ensuring that the electron beam used for imaging or processing has a single energy level and reducing chromatic aberration. Therefore, the performance and condition of the aperture stop directly determine the sharpness, contrast, and processing accuracy of the electron optics system. Modern high-end electron beam equipment is typically equipped with multi-aperture movable aperture structures, allowing users to quickly switch between different aperture stops for different modes (such as high-resolution imaging, high-current analysis, and micro-area processing).

[0004] In related technologies, machining and installation errors of the aperture, as well as thermal drift and carbon contamination generated during equipment operation, can cause the physical center of the aperture aperture to misalign with the theoretical optical axis of the electron optical system. This deviation introduces additional astigmatism into the electron optical system and causes beam shift, severely degrading image quality and affecting the repeatability and stability of the instrument's measurements. Currently, most moving aperture calibration methods involve first observing the center position of each aperture aperture on the moving aperture using an offline optical microscope and recording the corresponding data. Then, it is inserted into the electron optical system, and the operator repeatedly moves the aperture position while observing the degree of distortion in the sample image, manually compensating for the XY position of the aperture until the sample image is clearest and without obvious trailing.

[0005] However, current aperture calibration methods have the following technical problems:

[0006] The current aperture calibration process is lengthy and relies on manual operation, resulting in low calibration efficiency and accuracy, and needs to be optimized. Summary of the Invention

[0007] Therefore, it is necessary to provide an electro-optical system moving aperture calibration method, device, computer equipment, computer-readable storage medium, and computer program product that can improve the efficiency and accuracy of aperture calibration and increase the efficiency of the machine tool, in order to address the above-mentioned technical problems.

[0008] Firstly, this application provides a method for calibrating a moving aperture in an electro-optical system. The method includes:

[0009] In response to a calibration request for the aperture of the target device, coordinate data of a preset detection point and corresponding electron beam intensity data are obtained. The electron beam intensity data is obtained by controlling the aperture to be calibrated to perform two-dimensional scanning and sampling in the target plane.

[0010] The coordinate data and the electron beam intensity data are preprocessed, and a beam intensity distribution map is generated based on the preprocessed coordinate data and electron beam intensity data;

[0011] The beam intensity distribution map is processed based on a preset image processing algorithm, and the geometric center coordinates of the aperture of the aperture to be calibrated are obtained by fitting.

[0012] Using the geometric center coordinates as the corresponding calibration position of the aperture, aperture calibration of the target device is performed.

[0013] In one embodiment, the preprocessing of the coordinate data and the electron beam intensity data, and the generation of a beam intensity distribution map based on the preprocessed coordinate data and electron beam intensity data, includes:

[0014] When the number of detection points is less than a first preset threshold, a preset linear interpolation algorithm is used to uniformly supplement data points between the detection points, and the beam intensity value of the supplemented points is calculated based on the data of the neighboring detection points.

[0015] When the number of detection points exceeds the second preset threshold, a preset uniform sampling method is used to enrich and reduce the data density.

[0016] In one embodiment, the step of processing the beam intensity distribution map based on a preset image processing algorithm and fitting the geometric center coordinates of the aperture of the aperture to be calibrated includes:

[0017] The electron beam intensity data at each of the detection points are linearly mapped to a preset grayscale range to generate a corresponding grayscale image.

[0018] Based on a preset adaptive threshold algorithm, the grayscale image is segmented to extract the foreground region in the grayscale image. The adaptive threshold algorithm is constructed based on the local neighborhood weighted Gaussian mean.

[0019] Calculate the spatial moments of the foreground region, weight the gray values ​​of each coordinate point, and determine the coordinates of the geometric center of the foreground region.

[0020] In one embodiment, the grayscale image is segmented based on a preset adaptive threshold algorithm to extract the foreground region from the grayscale image. The adaptive threshold algorithm, after being constructed based on a local neighborhood weighted Gaussian mean, further includes:

[0021] Perform connected component analysis on the segmented foreground region to determine the area of ​​each connected region;

[0022] The foreground region is determined based on the area, and the foreground region is the connected region with the largest area.

[0023] In one embodiment, the method further includes:

[0024] The fitted shape of the aperture is compared with a reference circle. The center coordinates of the fitted shape and the theoretical center of the reference circle are calculated by a preset clustering algorithm, and / or the overlap rate between the fitted shape and the reference circle is calculated.

[0025] The contamination status of the aperture is determined based on the center deviation and / or the overlap rate.

[0026] In one embodiment, the method further includes:

[0027] The preprocessed standard data dictionary, the fitted area edge of the aperture, and the coordinates of the geometric center are then graphically rendered.

[0028] A visualization interface is generated, which includes two-dimensional and three-dimensional heat maps, and the geometric center coordinates of the aperture are marked in the visualization interface.

[0029] Secondly, this application also provides a calibration system for a moving aperture in an electro-optical system. The system includes:

[0030] A movable aperture assembly, wherein the movable aperture assembly is used to drive the aperture plate to move;

[0031] An electron beam detection device is mounted on the sample stage of the target device and is used to measure the electron beam intensity.

[0032] The main control module is connected to both the moving aperture assembly and the electron beam detection device. It is used to drive the moving aperture assembly to make the aperture plate scan and move, and also to control the opening and closing of the electron beam, and synchronously collect the electron beam intensity data collected by the electron beam detection device.

[0033] Thirdly, this application also provides a calibration device for a moving aperture in an electro-optical system. The device includes:

[0034] The acquisition module is used to respond to the aperture calibration request of the target device, acquire the coordinate data of the preset detection point and the corresponding electron beam intensity data. The electron beam intensity data is acquired by controlling the aperture to be calibrated to perform two-dimensional scanning and sampling in the target plane.

[0035] The preprocessing module is used to preprocess the coordinate data and the electron beam intensity data, and generate a beam intensity distribution map based on the preprocessed coordinate data and electron beam intensity data;

[0036] The fitting module is used to process the beam intensity distribution map based on a preset image processing algorithm and fit the geometric center coordinates of the aperture of the aperture to be calibrated.

[0037] The calibration module is used to perform aperture calibration on the target device using the geometric center coordinates as the corresponding calibration position of the aperture.

[0038] In one embodiment, the preprocessing module includes:

[0039] The upsampling module is used to uniformly supplement data points between the detection points using a preset linear interpolation algorithm when the number of detection points is less than a first preset threshold, and to calculate the beam intensity value of the supplemented points based on the data of the adjacent detection points.

[0040] The downsampling module is used to reduce data density by employing a preset uniform sampling enrichment method when the number of detection points exceeds a second preset threshold.

[0041] In one embodiment, the fitting module includes:

[0042] The grayscale mapping module is used to linearly map the electron beam intensity data of each detection point to a preset grayscale range to generate a corresponding grayscale image.

[0043] The segmentation module is used to segment the grayscale image based on a preset adaptive threshold algorithm to extract the foreground region in the grayscale image. The adaptive threshold algorithm is constructed based on the local neighborhood weighted Gaussian mean.

[0044] The central calculation module is used to calculate the spatial moments of the foreground region, weight the gray values ​​of each coordinate point, and determine the geometric center coordinates of the foreground region.

[0045] In one embodiment, after the segmentation module, the following is further included:

[0046] The connected component module is used to perform connected component analysis on the segmented foreground region and determine the area of ​​each connected region.

[0047] The foreground region module is used to determine the foreground region based on the area, wherein the foreground region is the connected region with the largest area.

[0048] In one embodiment, the device further includes:

[0049] The comparison module is used to compare the fitted shape of the aperture with a reference circle, and calculate the center deviation between the center coordinates of the fitted shape and the theoretical center of the reference circle, and / or the overlap rate between the fitted shape and the reference circle using a preset clustering algorithm.

[0050] A contamination detection module is used to determine the contamination status of the aperture based on the center deviation and / or the overlap rate.

[0051] In one embodiment, the device further includes:

[0052] The graphical module is used to graphically render the preprocessed standard data dictionary, the fitted area edge of the aperture, and the coordinates of the geometric center.

[0053] An interface generation module is used to generate a visualization interface, which includes two-dimensional and three-dimensional heat maps, and the geometric center coordinates of the aperture are marked in the visualization interface.

[0054] Fourthly, this application also provides a computer device. The computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps in the electro-optical system moving aperture calibration method as described in any embodiment of the first aspect.

[0055] Fifthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of a moving aperture calibration method for an electro-optical system as described in any embodiment of the first aspect.

[0056] Sixthly, this application also provides a computer program product. The computer program product includes a computer program that, when executed by a processor, implements the steps in a method for calibrating a moving aperture in an electro-optical system as described in any embodiment of the first aspect.

[0057] The above-described method, apparatus, computer equipment, storage medium, and computer program product for calibrating a moving aperture in an electro-optical system, derived from the technical features in the embodiments, can achieve beneficial effects to address the technical problems in the background art.

[0058] This application provides a method for calibrating a moving aperture in an electro-optical system, comprising: in response to a calibration request for the aperture of a target device, acquiring coordinate data of a preset detection point and corresponding electron beam intensity data, wherein the electron beam intensity data is acquired by controlling the aperture to be calibrated to perform two-dimensional scanning and sampling within a target plane; preprocessing the coordinate data and the electron beam intensity data to generate a beam intensity distribution map based on the preprocessed coordinate data and the electron beam intensity data; processing the beam intensity distribution map based on a preset image processing algorithm to fit the geometric center coordinates of the aperture of the aperture to be calibrated; and using the geometric center coordinates as the calibration position of the corresponding aperture, performing aperture calibration on the target device. In implementation, by automatically acquiring the coordinates and beam intensity data during the two-dimensional scanning process of the aperture in response to the calibration request, the automatic triggering and data acquisition of the calibration process are realized, laying the foundation for full automation. Subsequently, the raw data is preprocessed to unify the scale, effectively eliminating analytical errors caused by differences in scanning step size or fluctuations in data volume, providing a reliable data foundation for generating a high-fidelity beam intensity distribution map. Furthermore, a pre-defined image processing algorithm is used to automatically process the distribution map and fit the precise geometric center of the aperture, replacing the traditional subjective mode that relies on human observation and manual adjustment. This transforms the calibration process into an objective and quantifiable computational problem, fundamentally ensuring the high accuracy and repeatability of the calibration results. Finally, the fitted geometric center coordinates are directly used as the calibration position of the aperture for equipment calibration, completing a closed-loop operation from data to execution. This application optimizes the originally experience-dependent and lengthy manual operation into an efficient, accurate, and fully automated standardized process, significantly improving calibration efficiency and accuracy, and effectively enhancing the long-term operational stability and maintainability of electro-optical equipment. Attached Figure Description

[0059] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is an application environment diagram of a moving aperture calibration method for an electro-optical system in one embodiment;

[0061] Figure 2 This is a schematic diagram of the first process of a method for calibrating a moving aperture in an electro-optical system according to one embodiment;

[0062] Figure 3 This is a schematic diagram of the scanning movement of the aperture in one embodiment;

[0063] Figure 4 This is a schematic diagram of the second process of a method for calibrating a moving aperture in an electro-optical system in another embodiment;

[0064] Figure 5 This is a schematic diagram of the third process of a method for calibrating a moving aperture in an electro-optical system, as described in another embodiment.

[0065] Figure 6 This is a schematic diagram of the fourth process of a method for calibrating a moving aperture in an electro-optical system in another embodiment;

[0066] Figure 7 This is a schematic diagram of the fifth step of a method for calibrating a moving aperture in an electro-optical system, as described in another embodiment.

[0067] Figure 8 This is a schematic diagram of contamination in a two-dimensional heat map of one embodiment;

[0068] Figure 9 This is a schematic diagram of the sixth step of a method for calibrating a moving aperture in an electro-optical system, as described in another embodiment.

[0069] Figure 10 This is a visual flowchart illustrating the processing flow in one embodiment;

[0070] Figure 11 This is a structural block diagram of a moving aperture calibration device for an electro-optical system in one embodiment;

[0071] Figure 12 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0072] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0073] This application provides a method for calibrating a moving aperture in an electro-optical system, which can be applied to, for example... Figure 1 The application environment shown. For example... Figure 1 As shown, Figure 1 An electro-optical system moving aperture calibration system is illustrated. The system includes a moving aperture assembly, an electron beam detection device, and a main control module. The moving aperture assembly drives the aperture plate to move; the electron beam detection device is mounted on the sample stage of the target device and measures the electron beam intensity; the main control module is connected to both the moving aperture assembly and the electron beam detection device, and is used to drive the moving aperture assembly to scan and move the aperture plate, control the opening and closing of the electronic book, and simultaneously acquire the electron beam intensity data collected by the electron beam detection device. In application, the electro-optical system moving aperture calibration system is integrated within the electro-optical system. The system is illustrated using an example where the moving aperture assembly is a high-precision motor and the electron beam detection device is a Faraday cup. In this case, the system mainly includes:

[0074] Electro-optical systems include electron guns, focusing lenses, electron beam blanking devices, focusing objectives, electron beam detectors, etc.

[0075] The movable aperture assembly is a metal plate carrying multiple apertures of different sizes. It can be driven by a high-precision motor to move the aperture plate precisely in the XY plane. The following movable apertures will all be based on circular apertures.

[0076] A Faraday cup is used to capture charged particles when they are incident perpendicularly to the Faraday cup. The inner wall of the cup intercepts and traps the particles, and the charge carried by the particles is transferred to the Faraday cup. If the particles are positive ions, the cup will accumulate positive charge; if they are electrons, it will accumulate negative charge. The Faraday cup is then grounded through an external circuit, and the accumulated charge forms a current flowing from the Faraday cup to the ground. By connecting a high-precision galvanometer or a low-resistance sampling resistor in series in the grounding loop, the current intensity of the charged particle beam can be calculated very accurately by measuring the voltage drop across the resistor. The instrument is placed in the lower left corner of the sample stage; during measurement, the sample stage is moved to the position of the Faraday cup to accurately measure the electron beam current intensity.

[0077] The control system includes a moving aperture displacement control unit, a beam scanning control unit, a signal acquisition unit, and a data processing unit.

[0078] For example, the control system can set the start and end points of the moving aperture scanning as needed, and control the absolute displacement of the aperture plate by adjusting the step size of the aperture motor to achieve fine-grained control of the aperture calibration accuracy, meeting the needs of different application scenarios. The smaller the step size, the higher the displacement accuracy, the larger the data volume, and the more accurate the fitted center point. Therefore, the coordinates of each beam detection point in this calibration process can be determined according to the system parameter settings. After the control system completes the system parameters, it automatically moves the Faraday cup on the sample stage to directly below the objective lens to ensure that the electron beam can be perpendicularly incident into the Faraday cup. After ensuring that the sample stage is moved to the designated position, the control system controls the moving aperture to perform grating-like movement in the XY plane. At the detection point, the electron beam is briefly turned on and off by the electron beam blanking device, and the beam intensity when turned on and the background noise when turned off are recorded respectively. The difference between the two is the beam intensity recorded by the Faraday cup. Then, it moves to the next detection point and repeats the above operation until the end point.

[0079] In one embodiment, such as Figure 2 and Figure 3 As shown, a method for calibrating the moving aperture of an electron optical system is provided, which can be applied to... Figure 1 Taking the control system in the example, the following steps are included:

[0080] Step 202: In response to the aperture calibration request of the target device, obtain the coordinate data of the preset detection point and the corresponding electron beam intensity data. The electron beam intensity data is obtained by controlling the aperture to be calibrated to perform two-dimensional scanning and sampling in the target plane.

[0081] Step 204: Preprocess the coordinate data and the electron beam intensity data, and generate a beam intensity distribution map based on the preprocessed coordinate data and the electron beam intensity data.

[0082] Step 206: Process the beam intensity distribution map based on the preset image processing algorithm, and fit the geometric center coordinates of the aperture of the aperture to be calibrated.

[0083] Step 208: Using the geometric center coordinates as the corresponding calibration position of the aperture, perform aperture calibration on the target device.

[0084] In the above-described method for calibrating a moving aperture in an electro-optical system, a reasonable derivation is made based on the technical features in the embodiments, achieving the beneficial effect of solving the technical problems raised in the background art:

[0085] This application provides a method for calibrating a moving aperture in an electro-optical system, comprising: in response to a calibration request for the aperture of a target device, acquiring coordinate data of a preset detection point and corresponding electron beam intensity data, wherein the electron beam intensity data is acquired by controlling the aperture to be calibrated to perform two-dimensional scanning and sampling within a target plane; preprocessing the coordinate data and the electron beam intensity data to generate a beam intensity distribution map based on the preprocessed coordinate data and the electron beam intensity data; processing the beam intensity distribution map based on a preset image processing algorithm to fit the geometric center coordinates of the aperture of the aperture to be calibrated; and using the geometric center coordinates as the calibration position of the corresponding aperture, performing aperture calibration on the target device. In implementation, by automatically acquiring the coordinates and beam intensity data during the two-dimensional scanning process of the aperture in response to the calibration request, the automatic triggering and data acquisition of the calibration process are realized, laying the foundation for full automation. Subsequently, the raw data is preprocessed to unify the scale, effectively eliminating analytical errors caused by differences in scanning step size or fluctuations in data volume, providing a reliable data foundation for generating a high-fidelity beam intensity distribution map. Furthermore, a pre-defined image processing algorithm is used to automatically process the distribution map and fit the precise geometric center of the aperture, replacing the traditional subjective mode that relies on human observation and manual adjustment. This transforms the calibration process into an objective and quantifiable computational problem, fundamentally ensuring the high accuracy and repeatability of the calibration results. Finally, the fitted geometric center coordinates are directly used as the calibration position of the aperture for equipment calibration, completing a closed-loop operation from data to execution. This application optimizes the originally experience-dependent and lengthy manual operation into an efficient, accurate, and fully automated standardized process, significantly improving calibration efficiency and accuracy, and effectively enhancing the long-term operational stability and maintainability of electro-optical equipment.

[0086] In one embodiment, such as Figure 4 As shown, step 204 includes:

[0087] Step 402: When the number of detection points is less than the first preset threshold, a preset linear interpolation algorithm is used to uniformly supplement data points between the detection points, and the beam intensity value of the supplemented points is calculated based on the data of the neighboring detection points.

[0088] Step 404: When the number of detection points is higher than the second preset threshold, a preset uniform sampling method is used to enrich and reduce the data density.

[0089] For example, after the control system imports the raw data, it can parse and extract the data. Based on the column names in the table header, it obtains the X and Y coordinates and their corresponding beam intensity values. The data is then converted into a raw data dictionary based on the correspondence between coordinate points and beam intensity values ​​for subsequent processing. Since the range of input coordinate data fluctuates significantly, the number of sampled coordinate points may range from hundreds to thousands. Direct visualization would lead to large differences in image resolution later, affecting subsequent operations such as region division and center point determination. Therefore, the raw data can be uniformly scaled. For example, a standard image width of 1000 sampling points can be set. If the number of sampling points in the raw data dictionary is less than the standard size, a linear interpolation algorithm is used to supplement it. Coordinate points are uniformly expanded, and the values ​​of supplementary coordinate points are calculated based on the values ​​of neighboring coordinate points until the data volume meets the requirements. If the data exceeds the standard size, its resolution is reduced through uniform sampling.

[0090] In this embodiment, an adaptive data preprocessing mechanism is introduced to intelligently process different sampling densities: sparse data is enhanced by interpolation to ensure the shape of the distribution map is complete and continuous, avoiding deviation of the fitting center point due to insufficient data; overly dense data is downsampled to improve the speed of subsequent image processing, avoid redundant calculations, and also help improve the data quality and consistency of the beam intensity distribution map, laying a stable and reliable data foundation for subsequent high-precision and high-efficiency center fitting.

[0091] In one embodiment, such as Figure 5 As shown, step 206 includes:

[0092] Step 502: Linearly map the electron beam intensity data of each detection point to a preset grayscale range to generate a corresponding grayscale image.

[0093] Step 504: Based on a preset adaptive threshold algorithm, the grayscale image is segmented to extract the foreground region in the grayscale image. The adaptive threshold algorithm is constructed based on the local neighborhood weighted Gaussian mean.

[0094] Step 506: Calculate the spatial moments of the foreground region, weight the gray values ​​of each coordinate point, and determine the geometric center coordinates of the foreground region.

[0095] For example, the control system can convert a standard-sized data dictionary into an 8-bit grayscale image, that is, map the current value corresponding to each coordinate point to a grayscale range of 0-255, with a minimum value of 0 and a maximum value of 255, so that it can be adapted to subsequent visual algorithms. The formula used is:

[0096]

[0097] in, The grayscale value is the result of mapping the point (x, y). Let (x, y) be the original values ​​of the point. The smallest original value, This is the largest original value.

[0098] Subsequently, adaptive thresholding is performed on the processed 8-bit grayscale image to identify the candidate feature current regions, i.e., the foreground regions in the grayscale image that differ significantly from the background. Based on the grayscale mean of the local neighborhood of each pixel, it is determined whether the pixel is foreground or background, thereby extracting the feature value regions.

[0099] Specifically, first, a moving window of a specific size is selected, containing n sampling points. The weighted Gaussian mean of the values ​​at these points is calculated and used as the threshold for judging that point. The formula is as follows:

[0100]

[0101] in, Let (x, y) be the threshold value for judging the point (x, y). To resize the window, For the coordinates of the points, The Gaussian weights corresponding to that point. This represents the grayscale value corresponding to that point. This is the offset constant.

[0102] Then, based on the threshold obtained above, it is determined whether the point is foreground or background. The determination formula is as follows:

[0103]

[0104] in, To produce the final judgment output, if the value is greater than the calculated threshold, the point is judged as the foreground with a value of 255; otherwise, it is judged as the background with a value of 0.

[0105] After adaptive threshold segmentation, points with large numerical variations in the foreground and surrounding areas are identified, which are the edge points of each region. These points form the dividing edges of each region on the grayscale image. The numerical variations within these regions are relatively small, while the numerical variations between regions are drastic. Connectivity analysis is then performed on these segmented connected regions to filter out the final required characteristic current regions.

[0106] For example, the spatial moment of the aforementioned current characteristic value region is calculated by weighting the gray value of each coordinate point, and then calculating the coordinates of the center point of the region on the x-axis and y-axis, as shown in the following formula:

[0107]

[0108]

[0109] in, This is the sum of the values ​​of all coordinate points within the region. It is the sum of the pixel's x-coordinate multiplied by its pixel value. The final geometric center coordinates of the region are obtained by multiplying the pixel's ordinate by the sum of its pixel values. .

[0110] In this embodiment, by mapping beam data to a grayscale image and employing an adaptive thresholding algorithm based on local Gaussian weighting for segmentation, the aperture region where beam intensity is concentrated can be accurately identified, effectively overcoming missegmentation caused by background noise or uneven overall brightness. Subsequently, through spatial moment calculation, the grayscale values ​​are used as weights to determine the geometric center, so that the final center coordinates not only reflect the spatial shape of the aperture but also more accurately characterize the actual centroid of beam intensity distribution, thereby achieving sub-pixel-level high-precision positioning and significantly improving calibration accuracy and reliability.

[0111] In one embodiment, such as Figure 6 As shown, after step 504, the method further includes:

[0112] Step 602: Perform connected component analysis on the segmented foreground region to determine the area of ​​each connected region.

[0113] Step 604: Determine the foreground region based on the area, wherein the foreground region is the connected region with the largest area.

[0114] For example, in connected component analysis and filtering, the foreground points filtered by thresholds may be local outliers, points with large gradients, hotspot centers, or region edges. Here, we are more concerned with the edges of each region, so connected component analysis is required. The area of ​​each connected region is obtained and calculated, and they are sorted. Interference points with excessively small areas are removed. The connected component with the largest area is the region containing the desired current characteristic value. The coordinates of points inside and at the edges of this region and their corresponding values ​​are recorded to facilitate subsequent finding of the geometric center and visualization.

[0115] In this embodiment, by introducing connected component analysis and area filtering mechanisms, the unique true region representing the aperture can be accurately identified and locked from multiple discrete regions that may be generated after threshold segmentation. This step effectively eliminates isolated interference points or small pseudo-regions caused by image noise, minor perturbations, or uneven background, ensuring that the image region on which the subsequent center coordinate calculation is based is complete and accurate, thereby significantly improving the robustness of geometric center fitting and the reliability of the final calibration result.

[0116] In one embodiment, such as Figure 7As shown, the method further includes:

[0117] Step 702: Compare the fitted shape of the aperture with the reference circle, and calculate the center deviation between the center coordinates of the fitted shape and the theoretical center of the reference circle, and / or the overlap rate between the fitted shape and the reference circle using a preset clustering algorithm.

[0118] Step 704: Determine the contamination status of the aperture based on the center deviation and / or the overlap rate.

[0119] For example, when the aperture is contaminated (generally manifested as carbon deposition), the accumulation of charge at the contaminated area will push part of the electron beam away from the scanning point, causing abnormal distortion in the beam intensity distribution that represents the aperture shape in the above steps (the beam intensity distribution is no longer a standard circle, but rather a near-circular shape; the generated two-dimensional thermal map and its contamination manifestation are shown in the attached figure). Figure 8 (As shown). Therefore, based on the two-dimensional or three-dimensional heat map generated by the above steps, the contamination status of the aperture can be determined by calculating the center coordinate deviation and center overlap rate through clustering algorithms. The smaller the center coordinate deviation and the higher the center overlap rate, the lighter the aperture contamination; conversely, the larger the deviation and the higher the overlap rate, the more severe the aperture contamination.

[0120] In this embodiment, by quantitatively comparing the fitted shape with an ideal perfect circle, an objective and accurate diagnosis of the aperture contamination status is achieved. The center deviation directly reflects the degree of beam scattering or offset caused by contamination, while the overlap rate quantifies the integrity and regularity of the aperture shape. The combination of these two factors can effectively distinguish between minor contamination and severe blockage, providing clear and reliable data for predictive maintenance of the equipment, thereby realizing a functional extension from passive calibration to proactive health management.

[0121] In one embodiment, such as Figure 9 and Figure 10 As shown, the method further includes:

[0122] Step 902: Graphically render the preprocessed standard data dictionary, the fitted area edge of the aperture, and the geometric center coordinates.

[0123] Step 904: Generate a visualization interface, which includes two-dimensional and three-dimensional heat maps, and the geometric center coordinates of the aperture are marked in the visualization interface.

[0124] In this embodiment, by visualizing key data and results from the calibration process in multiple dimensions, the usability and interpretability of the method are significantly improved. Two-dimensional heatmaps intuitively display the spatial distribution of beam intensity, while three-dimensional heatmaps enhance depth perception. The overlay display of region edge and center coordinates allows users to quickly verify the rationality of the calibration results. This function not only provides operators with intuitive means of status monitoring and result verification but also provides complete and visual graphical evidence for subsequent data analysis and problem tracing, greatly enhancing the engineering practical value of the method.

[0125] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0126] Based on the same inventive concept, this application also provides an electro-optical system moving aperture calibration device for implementing the above-described method for calibrating a moving aperture in an electro-optical system. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the electro-optical system moving aperture calibration device provided below can be found in the above-described limitations of the electro-optical system moving aperture calibration method, and will not be repeated here.

[0127] In one embodiment, such as Figure 11 As shown, a calibration device for a moving aperture in an electro-optical system is provided, comprising: an acquisition module, a preprocessing module, a fitting module, and a calibration module, wherein:

[0128] The acquisition module is used to respond to the aperture calibration request of the target device, acquire the coordinate data of the preset detection point and the corresponding electron beam intensity data. The electron beam intensity data is acquired by controlling the aperture to be calibrated to perform two-dimensional scanning and sampling in the target plane.

[0129] The preprocessing module is used to preprocess the coordinate data and the electron beam intensity data, and generate a beam intensity distribution map based on the preprocessed coordinate data and electron beam intensity data;

[0130] The fitting module is used to process the beam intensity distribution map based on a preset image processing algorithm and fit the geometric center coordinates of the aperture of the aperture to be calibrated.

[0131] The calibration module is used to perform aperture calibration on the target device using the geometric center coordinates as the corresponding calibration position of the aperture.

[0132] In one embodiment, the preprocessing module includes:

[0133] The upsampling module is used to uniformly supplement data points between the detection points using a preset linear interpolation algorithm when the number of detection points is less than a first preset threshold, and to calculate the beam intensity value of the supplemented points based on the data of the adjacent detection points.

[0134] The downsampling module is used to reduce data density by employing a preset uniform sampling enrichment method when the number of detection points exceeds a second preset threshold.

[0135] In one embodiment, the fitting module includes:

[0136] The grayscale mapping module is used to linearly map the electron beam intensity data of each detection point to a preset grayscale range to generate a corresponding grayscale image.

[0137] The segmentation module is used to segment the grayscale image based on a preset adaptive threshold algorithm to extract the foreground region in the grayscale image. The adaptive threshold algorithm is constructed based on the local neighborhood weighted Gaussian mean.

[0138] The central calculation module is used to calculate the spatial moments of the foreground region, weight the gray values ​​of each coordinate point, and determine the geometric center coordinates of the foreground region.

[0139] In one embodiment, after the segmentation module, the following is further included:

[0140] The connected component module is used to perform connected component analysis on the segmented foreground region and determine the area of ​​each connected region.

[0141] The foreground region module is used to determine the foreground region based on the area, wherein the foreground region is the connected region with the largest area.

[0142] In one embodiment, the device further includes:

[0143] The comparison module is used to compare the fitted shape of the aperture with a reference circle, and calculate the center deviation between the center coordinates of the fitted shape and the theoretical center of the reference circle, and / or the overlap rate between the fitted shape and the reference circle using a preset clustering algorithm.

[0144] A contamination detection module is used to determine the contamination status of the aperture based on the center deviation and / or the overlap rate.

[0145] In one embodiment, the device further includes:

[0146] The graphical module is used to graphically render the preprocessed standard data dictionary, the fitted area edge of the aperture, and the coordinates of the geometric center.

[0147] An interface generation module is used to generate a visualization interface, which includes two-dimensional and three-dimensional heat maps, and the geometric center coordinates of the aperture are marked in the visualization interface.

[0148] The modules in the aforementioned electro-optical system moving aperture calibration device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0149] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 12 As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for calibrating a moving aperture in an electro-optical system. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0150] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0151] In one embodiment, a computer device is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0152] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0153] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above method embodiments.

[0154] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0155] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0156] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0157] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. An electron-optical system mobile diaphragm calibration method, characterized in that, The method comprises: in response to a light barrier calibration request of a target device, obtaining preset coordinate data of detection points and corresponding electron beam intensity data, the electron beam intensity data being obtained by controlling the to-be-calibrated light barrier to move and sample in two dimensions in a target plane; preprocessing the coordinate data and the electron beam intensity data, and generating a beam intensity distribution map based on the preprocessed coordinate data and the electron beam intensity data; processing the beam intensity distribution map based on a preset image processing algorithm to fit a geometric center coordinate of a light barrier hole of the to-be-calibrated light barrier; using the geometric center coordinate as a calibration position of the corresponding light barrier hole, performing light barrier calibration of the target device.

2. The method of claim 1, wherein, The preprocessing of the coordinate data and the electron beam intensity data and the generation of a beam intensity distribution map based on the preprocessed coordinate data and the electron beam intensity data comprise: when the number of detection points is less than a first preset threshold, uniformly supplementing data points between the detection points using a preset linear interpolation algorithm, and calculating the beam intensity value of the supplemented points according to the data of adjacent detection points; when the number of detection points is higher than a second preset threshold, reducing the data density by enriching the sampling uniformly.

3. The method of claim 1, wherein, The processing of the beam intensity distribution map based on a preset image processing algorithm to fit a geometric center coordinate of a light barrier hole of the to-be-calibrated light barrier comprises: linearly mapping the electron beam intensity data of each detection point to a preset gray scale interval to generate a corresponding gray scale map; segmenting the gray scale map based on a preset adaptive threshold algorithm to extract a foreground region in the gray scale map, the adaptive threshold algorithm being constructed based on a local neighborhood weighted Gaussian mean value; calculating the spatial moment of the foreground region, and weighting the gray scale value of each coordinate point to determine the geometric center coordinate of the foreground region.

4. The method of claim 3, wherein, After the segmentation of the gray scale map based on a preset adaptive threshold algorithm to extract a foreground region in the gray scale map, the adaptive threshold algorithm is constructed based on a local neighborhood weighted Gaussian mean value, and further comprises: performing connected component analysis on the segmented foreground region to determine the area of each connected component; determining a foreground region based on the area, the foreground region being the connected component with the largest area.

5. The method of claim 1, wherein, The method further comprises: comparing the fitted shape of the light barrier hole with a reference regular circle, calculating the center deviation of the center of the fitted shape from the theoretical center of the reference regular circle and / or the overlap rate of the fitted shape and the reference regular circle by a preset clustering algorithm; determining the contamination state of the light barrier hole according to the center deviation and / or the overlap rate.

6. The method of claim 1, wherein, The method further comprises: graphically rendering the preprocessed standard data dictionary, the fitted region edge of the light barrier hole, and the geometric center coordinate; generating a visualization interface, the visualization interface including two-dimensional and three-dimensional heat maps, and the geometric center coordinate of the light barrier hole being labeled in the visualization interface.

7. An electron-optical system mobile iris calibration system, characterized by The system comprises: A mobile aperture assembly for driving an aperture plate to move; An electron beam current detection device arranged on a sample table of a target device for measuring an electron beam current intensity; A main control module connected with the mobile aperture assembly and the electron beam current detection device, for driving the mobile aperture assembly to move the aperture plate to scan, and for controlling the opening and closing of an electron book and synchronously collecting the electron beam current intensity data collected by the electron beam current detection device.

8. An electron-optical system mobile diaphragm calibration device, characterized by The device comprises: A collection module for obtaining preset coordinate data of detection points and corresponding electron beam current intensity data in response to an aperture calibration request of a target device, the electron beam current intensity data being obtained by controlling a to-be-calibrated aperture to move in two dimensions on a target plane for sampling; A preprocessing module for preprocessing the coordinate data and the electron beam current intensity data, and generating a beam current intensity distribution map based on the preprocessed coordinate data and the electron beam current intensity data; A fitting module for processing the beam current intensity distribution map based on a preset image processing algorithm, and fitting to obtain geometric center coordinates of aperture holes of the to-be-calibrated aperture; A calibration module for taking the geometric center coordinates as calibration positions of the corresponding aperture holes, and performing aperture calibration of the target device. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the steps of the method of any one of claims 1 to 6.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 6.

Images