A method, system, terminal and storage medium for automatic correction of chromatic aberration of focus of a charged particle beam imaging apparatus

Abstract

This invention relates to the field of electron microscopy, and discloses an automatic focusing and astigmatism correction method, system, terminal, and storage medium for charged particle beam imaging equipment. The method includes: improving the signal-to-noise ratio by phase-aligned superposition of two frames; accurately extracting focusing and astigmatism coefficients using a second Fourier transform and symmetrical region accumulation, combined with elliptic fitting; identifying the astigmatism "double-peak" characteristic interval near the focal midpoint; obtaining the astigmatism gradient vector based on linear regression fitting to determine the correction direction; and solving for the optimal astigmatism control parameters through local fine-tuning to drive the astigmatism correction coil. This invention achieves fast, stable, and high-precision automatic focusing and astigmatism correction, significantly improving imaging quality.

Description

Technical Field

[0001] This invention relates to the field of electron microscopy, and in particular to an automatic focusing astigmatism correction method, system, terminal, and computer-readable storage medium for a charged particle beam imaging device. Background Technology

[0002] The imaging quality of charged particle beam imaging devices (such as electron microscopes) heavily relies on the performance of electromagnetic lenses. Astigmatism is one of the main inherent aberrations of electromagnetic lenses, manifested as inconsistent focusing ability of the lens in different directions, leading to image blurring. Existing automatic correction methods mainly include those based on image database comparison, neural networks, and Fourier space analysis. Among these, Fourier space-based methods, while effective, still have shortcomings: some methods use the autocorrelation coefficient of a single image, making them susceptible to regular noise interference in the image; some methods employ complex polar coordinate transformations or ellipse fitting formulas, resulting in low computational efficiency; and some methods, while adjusting astigmatism in defocused states, do not optimize the defocus amount to maximize the system's sensitivity to astigmatism, affecting correction accuracy.

[0003] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0004] The main objective of this invention is to provide an automatic astigmatism correction method, system, terminal, and computer-readable storage medium for charged particle beam imaging devices. This invention aims to solve the problems of insufficient correction accuracy and poor stability in existing astigmatism correction techniques, which are caused by image drift interference, noise sensitivity, high algorithm complexity, and failure to perform correction at the optimal sensitivity position.

[0005] To achieve the above objectives, the present invention provides an automatic focus astigmatism correction method for a charged particle beam imaging device, the method comprising the following steps: Within a preset time period, a first image and a second image are continuously acquired at the same physical location. The displacement between the first image and the second image is calculated using a phase correlation algorithm. The first image and the second image are then aligned and superimposed based on the displacement to obtain a speckle image. The speckle image is subjected to a second Fourier transform to obtain an initial spectrum. Multiple symmetrically distributed regions of interest are selected from the initial spectrum and accumulated to obtain an accumulated spectrum. The accumulated spectrum is then fitted with an ellipse to obtain the astigmatism coefficient. Near the predicted focal point, the focusing coefficients of multiple sampling points are collected by changing the objective lens current, and the multiple focusing coefficients are fitted to obtain the focal midpoint. Two feature points with the greatest astigmatism intensity are identified on both sides of the focal midpoint. Within the interval defined by the two feature points, a linear regression fitting is performed on the astigmatism coefficient and the objective lens distance to obtain the astigmatism gradient vector. The correction direction is determined based on the astigmatism gradient vector. Fine-tuning sampling is performed along the correction direction, and the optimal astigmatism control parameter that minimizes the astigmatism intensity is solved. Based on the optimal astigmatism control parameter, the astigmatism correction coil in the charged particle beam imaging device is adjusted.

[0006] Furthermore, the step of calculating the displacement between the first image and the second image using a phase correlation algorithm includes the following steps: The first image and the second image are respectively subjected to mean removal and window function weighting to obtain the first preprocessed image and the second preprocessed image; Perform Fourier transform on the first preprocessed image and the second preprocessed image to obtain the first target image and the second target image, and calculate the cross power spectrum between the first target image and the second target image; The cross-power spectrum is subjected to inverse Fourier transform, and the displacement is determined by finding the peak position in the modulus image.

[0007] Furthermore, the step of performing a second Fourier transform on the speckle image to obtain the initial spectrum includes the following steps: Perform a first Fourier transform on the speckle image to obtain a first-order spectrum; After taking the amplitude of the first spectrum and centering it, a second Fourier transform is performed to obtain the initial spectrum.

[0008] Furthermore, the model used for elliptic fitting of the accumulated spectrum is as follows: ; in, For focusing coefficient, For the astigmatism coefficients corresponding to the 0° or 90° directions, For the astigmatism coefficients corresponding to the 45° or 135° directions, and These are the number of pixels for the image's length and width, respectively.

[0009] Furthermore, the step of performing linear regression fitting on the astigmatism coefficient and the objective lens distance within the interval defined by the two feature points to obtain the astigmatism gradient vector includes the following steps: Within the interval defined by the two feature points, a linear regression is performed on the astigmatism coefficient and the objective lens distance, and the P-value is calculated using the F-test. If the P value is greater than the preset threshold, the fitting is deemed invalid and the astigmatism correction process is terminated. If the P-value is less than or equal to the preset threshold, the fit is deemed valid, and the slope parameter of the linear regression model is extracted as the astigmatic gradient vector.

[0010] Furthermore, the step of determining the correction direction based on the astigmatic gradient vector, performing fine-tuning sampling along the correction direction, and solving for the optimal astigmatic control parameters that minimize the astigmatic intensity includes the following steps: Set new sampling points along the correction direction and collect the corresponding astigmatism coefficient data; Curve fitting is performed on the astigmatism coefficient data to solve for the optimal astigmatism control parameters that minimize astigmatism intensity.

[0011] Furthermore, the process between solving for the optimal astigmatism control parameters that minimize astigmatism intensity and determining the optimal astigmatism control parameters includes the following steps: Calculate the normalized deviation between the current astigmatism control parameters and the optimal astigmatism control parameters; If the normalization deviation is less than the preset convergence threshold, the optimal astigmatism control parameter is output; otherwise, the optimal astigmatism control parameter is used as the starting value for the next cycle, and the fine-tuning sampling and the solution process are repeated.

[0012] Furthermore, to achieve the above objectives, the present invention also provides an automatic focus astigmatism correction system for a charged particle beam imaging device. This system is used to implement the automatic focus astigmatism correction method for a charged particle beam imaging device as described above. The automatic focus astigmatism correction system includes: The speckle reconstruction module is used to continuously acquire a first image and a second image at the same physical location within a preset time, calculate the displacement between the first image and the second image using a phase correlation algorithm, and align and superimpose the first image and the second image according to the displacement to obtain a speckle image. The aberration analysis module is used to perform a second Fourier transform on the speckle image to obtain an initial spectrum, select multiple symmetrically distributed regions of interest in the initial spectrum for accumulation to obtain an accumulated spectrum, and perform ellipse fitting on the accumulated spectrum to obtain the astigmatism coefficient. The focusing module is used to acquire the focusing coefficients of multiple sampling points near the predicted focusing point by changing the objective lens current, and to fit the multiple focusing coefficients to obtain the focusing midpoint; The astigmatism correction module is used to identify the two feature points with the greatest astigmatism intensity on both sides of the focal midpoint, and to perform linear regression fitting on the astigmatism coefficient and the objective lens distance within the interval defined by the two feature points to obtain the astigmatism gradient vector. The correction direction is determined based on the astigmatism gradient vector, fine-tuning sampling is performed along the correction direction, and the optimal astigmatism control parameter that minimizes the astigmatism intensity is solved. The astigmatism correction coil in the charged particle beam imaging device is adjusted based on the optimal astigmatism control parameter.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and an automatic focus astigmatism correction program for a charged particle beam imaging device stored in the memory and executable on the processor, wherein when the automatic focus astigmatism correction program for a charged particle beam imaging device is executed by the processor, it implements the steps of the automatic focus astigmatism correction method for a charged particle beam imaging device as described above.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an automatic focus astigmatism correction program for a charged particle beam imaging device, and when the automatic focus astigmatism correction program for a charged particle beam imaging device is executed by a processor, it implements the steps of the automatic focus astigmatism correction method for a charged particle beam imaging device as described above.

[0015] The beneficial effects of this invention are as follows: This invention utilizes a phase correlation algorithm to calculate the displacement between the first image and the second image, and aligns and superimposes the first image and the second image based on the displacement to obtain a speckle image. This effectively suppresses image distortion caused by equipment drift or sample movement. Through highly robust displacement estimation and image superposition, it significantly improves the signal-to-noise ratio and spatial consistency of the speckle image, providing a stable and reliable data foundation for subsequent aberration parameter extraction.

[0016] This invention enhances the symmetrical structure dominated by aberrations in the spectrum and suppresses asymmetric noise by accumulating symmetrical regions using a second Fourier transform. This allows ellipse fitting to directly and accurately decouple the astigmatic components in the focusing and orthogonal directions, improving the accuracy of aberration coefficient extraction and its anti-interference capability.

[0017] This invention obtains the focal midpoint by varying the focusing coefficients at multiple sampling points using the objective lens current and fitting these coefficients together. This achieves high-precision positioning of the optimal focusing location, ensuring the system operates in the most compact beam pattern. This provides a highly sensitive and well-defined reference working point for subsequent astigmatism correction, thereby improving the overall accuracy of the correction.

[0018] This invention constructs a linear response model within the "bi-peak" region where astigmatism is most sensitive to defocus, accurately determines the correction direction, and combines local optimization to efficiently solve for the optimal control parameters, thereby achieving fast, stable, and high-precision automatic astigmatism correction and significantly improving imaging quality. Attached Figure Description

[0019] Figure 1 This is a flowchart of a preferred embodiment of the automatic focusing astigmatism correction method for the charged particle beam imaging device of the present invention; Figure 2 This is a schematic diagram of the original beam spot image in the automatic focusing astigmatism correction method of the charged particle beam imaging device of the present invention; Figure 3 This is a schematic diagram of the accumulated spectrum after second Fourier transform, symmetrical region accumulation and ellipse fitting in the automatic correction method for focusing astigmatism of the charged particle beam imaging device of the present invention. Figure 4 This is a schematic diagram illustrating the change of the focusing coefficient with objective lens distance and the fitting of the focusing midpoint in the automatic focusing astigmatism correction method of the charged particle beam imaging device of the present invention. Figure 5 This is a schematic diagram of the linear regression fitting between the X²-Y² type astigmatism coefficient B and the objective lens distance in the automatic correction method for focusing astigmatism of the charged particle beam imaging device of the present invention. Figure 6 This is a schematic diagram of the linear regression fitting between the 2XY type astigmatism coefficient C and the objective lens distance in the automatic correction method for focusing astigmatism of the charged particle beam imaging device of the present invention. Figure 7 This is a structural diagram of a preferred embodiment of the automatic focusing astigmatism correction system for the charged particle beam imaging device of the present invention; Figure 8 This is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0020] This application provides a method, system, terminal, and storage medium for automatic focus astigmatism correction in a charged particle beam imaging device. To make the objectives, technical solutions, and effects of this application clearer and more explicit, the following detailed description, with reference to the accompanying drawings and embodiments, further illustrates the application. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0021] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0022] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0023] The preferred embodiment of the present invention describes an automatic focus astigmatism correction method for charged particle beam imaging equipment, such as... Figure 1 As shown, the automatic focus astigmatism correction method of the charged particle beam imaging device includes the following steps: S10. Within a preset time period, continuously acquire a first image and a second image at the same physical location, calculate the displacement between the first image and the second image using a phase correlation algorithm, and align and superimpose the first image and the second image according to the displacement to obtain a speckle image.

[0024] Further, in step S10, calculating the displacement between the first image and the second image using a phase correlation algorithm includes the following steps: A1. Regarding the first image , and the second image , Mean removal and window function weighting are performed separately to obtain the first preprocessed image and the second preprocessed image.

[0025] in, and These are the pixel counts for the length and width of the image, respectively, where the second image is obtained by translating the first image. A2. Perform Fourier transform on the first preprocessed image and the second preprocessed image to obtain the first target image. Second target image Calculate the cross-power spectrum between the first target image and the second target image (the cross-power spectrum is used to extract displacement information from the phase difference between two independent spectra and concentrate it into a single phase term): ; in, For cross power spectrum, yes The complex conjugate value.

[0026] A3. Perform an inverse Fourier transform on the cross-power spectrum and determine the displacement by finding the peak position in the modulus image.

[0027] Specifically, regarding the cross-power spectrum Performing an inverse Fourier transform yields the complex correlation image in the spatial domain. And obtain the modulus image by taking its modulus value. And by finding the modulus image peak position Determine the displacement.

[0028] The purpose of step S10 is to eliminate the relative displacement between two frames of images caused by device drift or sample movement, so as to accurately align and superimpose them to restore the true beam pattern shape unaffected by motion blur.

[0029] In this embodiment, the specific process and effect of step S10 are as follows: By rapidly and continuously acquiring two frames of images at the same physical location, system drift information is preserved. Subpixel-level displacement is calculated using the phase correlation method. This method is insensitive to image content and relies only on global translation, thus exhibiting strong robustness. The aligned images are then superimposed, effectively averaging random noise and significantly improving the signal-to-noise ratio, providing a high-quality data foundation for subsequent high-precision aberration analysis.

[0030] It is important to emphasize that this invention does not perform subpixel precision sampling because measurement accuracy is typically improved at the source by reducing the physical sampling interval (e.g., increasing the number of pixels to be sampled or decreasing the pixel spacing). Subpixel refinement may introduce additional interpolation errors and computational noise, thereby reducing reliability.

[0031] S20. Perform a second Fourier transform on the speckle image to obtain an initial spectrum. Select multiple symmetrically distributed regions of interest in the initial spectrum and accumulate them to obtain an accumulated spectrum. Perform ellipse fitting on the accumulated spectrum to obtain the astigmatism coefficient.

[0032] Further, in step S20, performing a second Fourier transform on the speckle image to obtain the initial spectrum includes the following steps: For the beam spot image Performing the first Fourier transform yields a spectrum. ; Take the amplitude of the first spectrum After centering, a second Fourier transform is performed to obtain the initial spectrum. .

[0033] Further, in step S20, the model used for elliptic fitting of the accumulated spectrum is: ; in, For focusing coefficient, For the astigmatism coefficients corresponding to the 0° or 90° directions, This is the astigmatism coefficient corresponding to the 45° or 135° direction. and These are the number of pixels for the image's length and width, respectively. It should be noted that the focusing factor is the reciprocal of the defocusing factor; the more rounded the ellipse, the better the focus, and the larger the focusing factor. Astigmatism factor... To correspond to the degree of stretching of the ellipse in the X and Y directions, Greater than 0, stretching in the Y direction. Less than 0, X-axis stretching; astigmatism coefficient This corresponds to the stretching of the ellipse in the 45-degree and 135-degree directions.

[0034] The purpose of step S20 is to transform the spatial domain information of the beam spot into a highly symmetrical spectral feature with enhanced signal-to-noise ratio through a unique second Fourier transform and symmetric region accumulation strategy, so that the subsequent ellipse fitting can directly and accurately decouple the focusing and astigmatic components in two orthogonal directions.

[0035] In this embodiment, the specific process and effect of step S20 are as follows: The first Fourier transform converts the beam spot image to the frequency domain, and its amplitude spectrum reflects the frequency components of the beam spot. After taking the amplitude and centering it, a second Fourier transform is performed. This operation can effectively suppress background noise and asymmetric interference, highlighting the centrally symmetric structure dominated by aberrations. On this basis, the symmetrical regions of the four quadrants are accumulated, further improving the signal-to-noise ratio and forcing the fourfold symmetry of the result, perfectly matching the physical characteristics of electromagnetic lens astigmatism. Finally, by fitting a simple quadratic elliptical model, the focusing coefficient A and astigmatism coefficients B and C with clear physical meaning can be directly obtained, avoiding complex polar coordinate transformations, making the calculation efficient and intuitive.

[0036] To more intuitively demonstrate the effects of speckle restoration and coefficient extraction, please refer to [link / reference]. Figure 2 and 3 .like Figure 2As shown, raw speckle image data typically contains noise, background interference, and irregular edges, making it difficult to use directly for accurate aberration analysis. After undergoing the second Fourier transform, symmetrical region accumulation, and contour filtering described in this invention, the resulting image is as follows: Figure 3 The accumulated spectrum is shown. The central symmetry of this spectrum is significantly enhanced, noise is effectively suppressed, and the focus and astigmatism information is clearly decoupled through elliptical fitting (dashed line in the figure), laying the foundation for subsequent high-precision parameter extraction.

[0037] S30. Near the predicted focal point, the focusing coefficients of multiple sampling points are collected by changing the objective lens current, and the multiple focusing coefficients are fitted to obtain the focal midpoint.

[0038] Further, in step S30, the astigmatism intensity is defined as: ; in, This is the objective lens distance (or the equivalent objective lens current value). and At objective lens distance The astigmatism coefficients measured in two orthogonal directions.

[0039] With focus coefficient (or its reciprocal) As a focused evaluation indicator, within the scanning interval... Focusing coefficient at the location When performing curve fitting (such as parabolic fitting), the objective lens distance corresponding to the extreme points is the focal midpoint. .

[0040] The purpose of step S30 is to precisely locate the optimal focusing position that makes the beam spot most compact (i.e., the focusing coefficient A is the largest) by scanning the objective lens current and evaluating the focusing state, so as to provide a stable and highly sensitive working point for subsequent astigmatism correction.

[0041] In this embodiment, the specific process and effect of step S30 are as follows: N sampling points are set within a small range, centered on the current objective lens position. At each sampling point, steps S10 and S20 are repeated to obtain the corresponding focusing coefficient A. Since the beam spot is smallest and the corresponding A value is largest when focusing is optimal, plotting the A value sequence against the objective lens current typically results in a single-peak curve. By fitting this curve with models such as parabolas, the peak position can be interpolated with high precision, which is the focus midpoint. This process achieves high-precision autofocus.

[0042] It should be noted that determining the focal midpoint is a prerequisite for subsequent astigmatism correction, because the effects of astigmatism can only be most clearly separated and quantified when at or near the optimal focal point.

[0043] For an explanation of the autofocus process, see [link to documentation]. Figure 4 . Figure 4 The trend of the focusing coefficient A as a function of objective lens distance z is shown. In this embodiment, three sampling points (N=3) are set with the current objective lens position as the center. The corresponding focusing coefficients are obtained by measurement and fitted using a parabolic model (curve in the figure). The peak point of the fitted curve is the focusing midpoint with high precision positioning, ensuring that the system operates in the state of the most compact beam spot.

[0044] S40. Identify the two feature points with the greatest astigmatism intensity on both sides of the focal midpoint, and perform linear regression fitting on the astigmatism coefficient and the objective lens distance within the interval defined by the two feature points to obtain the astigmatism gradient vector. Determine the correction direction based on the astigmatism gradient vector, perform fine-tuning sampling along the correction direction, and solve for the optimal astigmatism control parameter that minimizes the astigmatism intensity. Adjust the astigmatism correction coil in the charged particle beam imaging device based on the optimal astigmatism control parameter.

[0045] Further, in step S40, the step of performing linear regression fitting on the astigmatism coefficient and the objective lens distance within the interval defined by the two feature points to obtain the astigmatism gradient vector includes the following steps: S411. Within the interval defined by the two feature points, perform linear regression fitting on the astigmatism coefficient and the objective lens distance, and calculate the P-value through the F-test.

[0046] In this embodiment, in the case of two feature points and Within the defined interval, a linear regression fit is performed on the astigmatism coefficient and the objective lens distance: ; ; in, and All are intercepts, reflecting inherent astigmatism shift.

[0047] S412. If the P value is greater than a preset threshold (e.g., 0.05), the fitting is deemed invalid and the astigmatism correction process is terminated. S413. If the P-value is less than or equal to the preset threshold, the fit is deemed valid, and the slope parameter of the linear regression model is extracted. and The astigmatic gradient vector constitutes ( , ).

[0048] The purpose of steps S411 to S413 is to verify whether there is a significant linear relationship between the astigmatism coefficient and the objective lens distance within the selected interval, so as to ensure that the subsequent gradient-based correction direction is reliable and effective, and to avoid incorrect correction in noise-dominated regions.

[0049] In this embodiment, the specific process and effects of steps S411 to S413 are as follows: Near the focal midpoint, the astigmatism intensity exhibits a "bimodal" characteristic as the defocusing amount changes. Linear fitting is performed on the interval between these two peak points because astigmatism is most sensitive to changes in objective lens current within this interval and is approximately linear. The P-value is calculated using the F-test to quantitatively determine whether the fitting result is driven by real physical laws rather than random noise. If the P-value is too large, it indicates that the data is unreliable; timely termination of the process can prevent system divergence and improve the robustness of the algorithm.

[0050] It should be noted that this significance test is a key security mechanism in this invention to ensure the stability and reliability of the correction.

[0051] To further illustrate the linear regression fitting process, see [link to documentation]. Figure 5 and 6 . Figure 5 This demonstrates the variation of the X²-Y² type astigmatism coefficient B with objective lens distance z. Figure 6 This demonstrates the variation of the astigmatism coefficient C of a 2XY type as a function of the objective lens distance z. (The text then abruptly shifts to a seemingly unrelated topic: "Based on feature points...") and Within the defined interval (between the two points in the diagram), the data points (red or blue) exhibit a good linear relationship. The slope of the straight line obtained through linear regression fitting (green curve in the diagram) is the component of the astigmatic gradient vector. and Its reliability has been verified by the F-test. This gradient vector directly determines the direction of subsequent astigmatism correction.

[0052] Further, in step S40, determining the correction direction based on the astigmatic gradient vector, performing fine-tuning sampling along the correction direction, and solving for the optimal astigmatic control parameters that minimize astigmatic intensity includes the following steps: S421. Set new sampling points along the correction direction and collect the corresponding astigmatism coefficient data; S422. Perform curve fitting on the astigmatism coefficient data to solve for the optimal astigmatism control parameters that minimize astigmatism intensity.

[0053] In one embodiment, the step of determining the correction direction based on the astigmatic gradient vector, performing fine-tuning sampling along the correction direction, and solving for the optimal astigmatic control parameters that minimize astigmatic intensity includes the following steps: along the correction direction (i.e. (Direction) Set new sampling points and collect corresponding astigmatism coefficient data; establish astigmatism control parameters. With astigmatism coefficient The linear mapping relationship, and solve for the linear mapping relationship that makes The minimum optimal solution. This optimal solution can be calculated using the following formula: ; in, For the mapping matrix, It is the matrix transpose operator. This is the offset vector.

[0054] The purpose of steps S421 and S422 is to transform global gradient information into a local fine-grained search, and to efficiently approximate the globally optimal astigmatism control parameters by collecting new data and optimizing it in the most promising direction.

[0055] In this embodiment, the specific processes and effects of steps S421 and S422 are as follows: The astigmatism gradient vector indicates the direction in which astigmatism increases most rapidly, and its opposite direction is the correction direction for reducing astigmatism. Setting a small number (e.g., 3-5) of new sampling points along this direction can greatly reduce the search space. By performing quadratic curve fitting on the astigmatism intensity data of these new sampling points, the minimum point can be directly and analytically determined, which is the optimal astigmatism control parameter for this iteration. This method has a fast convergence speed and is far more efficient than grid search or random search.

[0056] Furthermore, in step S40, between solving for the optimal astigmatism control parameters that minimize astigmatism intensity and the step of determining the optimal astigmatism control parameters, the following steps are also included: S431. Calculate the current astigmatism control parameters. With the optimal astigmatism control parameters Normalization bias between them; The expression for the normalized bias is: ; S432. If the normalization deviation is less than the preset convergence threshold (e.g., 0.1), the optimal astigmatism control parameter is output; otherwise, the optimal astigmatism control parameter is used as the starting value for the next cycle, and the fine-tuning sampling and the solution process are repeated.

[0057] The purpose of steps S431 and S432 is to provide an objective, quantitative convergence criterion to determine whether the correction process has reached the required accuracy, thereby avoiding unnecessary iterations and ensuring the accuracy of the final result.

[0058] In this embodiment, the specific processes and effects of steps S431 and S432 are as follows: The normalized deviation measures the relative distance between the current control point and the theoretical optimal solution. When the normalized deviation is sufficiently small, it means that the improvement brought by further adjustments is negligible, and the correction can be considered complete. If the normalized deviation is still large, the optimal parameters found this time are used as the starting point for the next round of fine-tuning. This iterative mechanism can gradually approach the global optimal solution, and even if the initial guess is poor, it can guarantee eventual convergence.

[0059] In summary, the purpose of step S40 is to construct a closed-loop, adaptive optimization process that can intelligently identify effective correction intervals, verify data reliability, efficiently search for optimal solutions and determine convergence, and ultimately achieve accurate and robust automatic correction of image astigmatism.

[0060] In this embodiment, the specific process and effect of step S40 are as follows: The entire S40 process starts from the focal point and cleverly utilizes the sensitive characteristics of astigmatism in the defocused state, decomposing a complex multidimensional optimization problem into a series of simple and reliable sub-steps. This not only significantly improves the correction accuracy but also significantly enhances the universality and stability of the algorithm under different sample and device conditions.

[0061] It should be noted that this invention fundamentally solves the problem of insufficient correction accuracy caused by improper selection of the working point in traditional methods by deeply coupling the focusing and astigmatism correction processes and performing correction at the optimal working point.

[0062] The beneficial effects of this invention are as follows: This invention utilizes a phase correlation algorithm to calculate the displacement between the first image and the second image, and aligns and superimposes the first image and the second image based on the displacement to obtain a speckle image. This effectively suppresses image distortion caused by equipment drift or sample movement. Through highly robust displacement estimation and image superposition, it significantly improves the signal-to-noise ratio and spatial consistency of the speckle image, providing a stable and reliable data foundation for subsequent aberration parameter extraction.

[0063] This invention enhances the symmetrical structure dominated by aberrations in the spectrum and suppresses asymmetric noise by accumulating symmetrical regions using a second Fourier transform. This allows ellipse fitting to directly and accurately decouple the astigmatic components in the focusing and orthogonal directions, improving the accuracy of aberration coefficient extraction and its anti-interference capability.

[0064] This invention obtains the focal midpoint by varying the focusing coefficients at multiple sampling points using the objective lens current and fitting these coefficients together. This achieves high-precision positioning of the optimal focusing location, ensuring the system operates in the most compact beam pattern. This provides a highly sensitive and well-defined reference working point for subsequent astigmatism correction, thereby improving the overall accuracy of the correction.

[0065] This invention constructs a linear response model within the "bi-peak" region where astigmatism is most sensitive to defocus, accurately determines the correction direction, and combines local optimization to efficiently solve for the optimal control parameters, thereby achieving fast, stable, and high-precision automatic astigmatism correction and significantly improving imaging quality.

[0066] Furthermore, such as Figure 7 As shown, based on the above-mentioned automatic focus astigmatism correction method for charged particle beam imaging equipment, the present invention also provides an automatic focus astigmatism correction system for charged particle beam imaging equipment, the automatic focus astigmatism correction system for charged particle beam imaging equipment comprising: The speckle reconstruction module 51 is used to continuously acquire a first image and a second image at the same physical location within a preset time, calculate the displacement between the first image and the second image using a phase correlation algorithm, and align and superimpose the first image and the second image according to the displacement to obtain a speckle image. The aberration analysis module 52 is used to perform a second Fourier transform on the speckle image to obtain an initial spectrum, select multiple symmetrically distributed regions of interest in the initial spectrum for accumulation to obtain an accumulated spectrum, and perform ellipse fitting on the accumulated spectrum to obtain the astigmatism coefficient. The focusing module 53 is used to collect the focusing coefficients of multiple sampling points near the predicted focusing point by changing the objective lens current, and to fit the multiple focusing coefficients to obtain the focusing midpoint. The astigmatism correction module 54 is used to identify the two feature points with the greatest astigmatism intensity on both sides of the focal midpoint, and to perform linear regression fitting on the astigmatism coefficient and the objective lens distance within the interval defined by the two feature points to obtain the astigmatism gradient vector. The correction direction is determined according to the astigmatism gradient vector, fine-tuning sampling is performed along the correction direction, and the optimal astigmatism control parameter that minimizes the astigmatism intensity is solved. The astigmatism correction coil in the charged particle beam imaging device is adjusted according to the optimal astigmatism control parameter.

[0067] Furthermore, such as Figure 8 As shown, based on the above-mentioned automatic focusing astigmatism correction method and system for charged particle beam imaging equipment, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 8 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0068] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores an automatic focus astigmatism correction program 40 for a charged particle beam imaging device, which can be executed by the processor 10 to implement the automatic focus astigmatism correction method for a charged particle beam imaging device in this application.

[0069] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the automatic focus astigmatism correction method of the charged particle beam imaging device.

[0070] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.

[0071] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an automatic focus astigmatism correction program for a charged particle beam imaging device, and the automatic focus astigmatism correction program for a charged particle beam imaging device, when executed by a processor, implements the steps of the automatic focus astigmatism correction method for a charged particle beam imaging device as described above.

[0072] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0073] Of course, 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, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0074] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method of automatic correction of focusing astigmatism of a charged particle beam imaging apparatus, characterized in that, The automatic focus astigmatism correction method for the charged particle beam imaging device includes the following steps: Within a preset time period, a first image and a second image are continuously acquired at the same physical location. The displacement between the first image and the second image is calculated using a phase correlation algorithm. The first image and the second image are then aligned and superimposed based on the displacement to obtain a speckle image. The speckle image is subjected to a second Fourier transform to obtain an initial spectrum. Multiple symmetrically distributed regions of interest are selected from the initial spectrum and accumulated to obtain an accumulated spectrum. The accumulated spectrum is then fitted with an ellipse to obtain the astigmatism coefficient. Near the predicted focal point, the focusing coefficients of multiple sampling points are collected by changing the objective lens current, and the multiple focusing coefficients are fitted to obtain the focal midpoint. Two feature points with the greatest astigmatism intensity are identified on both sides of the focal midpoint. Within the interval defined by the two feature points, a linear regression fitting is performed on the astigmatism coefficient and the objective lens distance to obtain the astigmatism gradient vector. The correction direction is determined based on the astigmatism gradient vector. Fine-tuning sampling is performed along the correction direction, and the optimal astigmatism control parameter that minimizes the astigmatism intensity is solved. Based on the optimal astigmatism control parameter, the astigmatism correction coil in the charged particle beam imaging device is adjusted.

2. The method of auto-correction of focus astigmatism of a charged particle beam imaging apparatus according to claim 1, characterized in that, The step of calculating the displacement between the first image and the second image using a phase correlation algorithm includes the following steps: The first image and the second image are respectively subjected to mean removal and window function weighting to obtain the first preprocessed image and the second preprocessed image; Perform Fourier transform on the first preprocessed image and the second preprocessed image to obtain the first target image and the second target image, and calculate the cross power spectrum between the first target image and the second target image; The cross-power spectrum is subjected to inverse Fourier transform, and the displacement is determined by finding the peak position in the modulus image.

3. The method of auto-correction of focus astigmatism of a charged particle beam imaging apparatus according to claim 1, characterized in that, The process of performing a second Fourier transform on the speckle image to obtain the initial spectrum includes the following steps: Perform a first Fourier transform on the speckle image to obtain a first-order spectrum; After taking the amplitude of the first spectrum and centering it, a second Fourier transform is performed to obtain the initial spectrum.

4. The method of auto-correction of focus astigmatism of a charged particle beam imaging apparatus according to claim 1, characterized in that, The model used for elliptic fitting of the accumulated spectrum is: ； wherein, is the focal length, is the astigmatism coefficient corresponding to the 0° or 90° direction, is the astigmatism coefficient corresponding to the 45° or 135° direction, and are the number of pixels in the image length and width, respectively.

5. The method of auto-correction of focus astigmatism of a charged particle beam imaging apparatus according to claim 1, characterized in that, The step of performing linear regression fitting on the astigmatism coefficient and the objective lens distance within the interval defined by the two feature points to obtain the astigmatism gradient vector includes the following steps: Within the interval defined by the two feature points, a linear regression is performed on the astigmatism coefficient and the objective lens distance, and the P-value is calculated using the F-test. If the P value is greater than the preset threshold, the fitting is deemed invalid and the astigmatism correction process is terminated. If the P-value is less than or equal to the preset threshold, the fit is deemed valid, and the slope parameter of the linear regression model is extracted as the astigmatic gradient vector.

6. The automatic focusing astigmatism correction method for charged particle beam imaging equipment according to claim 1, characterized in that, The process of determining the correction direction based on the astigmatic gradient vector, performing fine-tuning sampling along the correction direction, and solving for the optimal astigmatic control parameters that minimize astigmatic intensity includes the following steps: Set new sampling points along the correction direction and collect the corresponding astigmatism coefficient data; Curve fitting is performed on the astigmatism coefficient data to solve for the optimal astigmatism control parameters that minimize astigmatism intensity.

7. The automatic focusing astigmatism correction method for charged particle beam imaging equipment according to claim 1, characterized in that, Between solving for the optimal astigmatism control parameters that minimize astigmatism intensity and the step of applying the optimal astigmatism control parameters, the following steps are also included: Calculate the normalized deviation between the current astigmatism control parameters and the optimal astigmatism control parameters; If the normalization deviation is less than the preset convergence threshold, the optimal astigmatism control parameter is output; otherwise, the optimal astigmatism control parameter is used as the starting value for the next cycle, and the fine-tuning sampling and the solution process are repeated.

8. A system for automatic correction of focus astigmatism of a charged particle beam imaging apparatus, characterized in that The automatic focus astigmatism correction system of the charged particle beam imaging device is used to implement the automatic focus astigmatism correction method of the charged particle beam imaging device as described in any one of claims 1-7, wherein the automatic focus astigmatism correction system of the charged particle beam imaging device comprises: The speckle reconstruction module is used to continuously acquire a first image and a second image at the same physical location within a preset time, calculate the displacement between the first image and the second image using a phase correlation algorithm, and align and superimpose the first image and the second image according to the displacement to obtain a speckle image. The aberration analysis module is used to perform a second Fourier transform on the speckle image to obtain an initial spectrum, select multiple symmetrically distributed regions of interest in the initial spectrum for accumulation to obtain an accumulated spectrum, and perform ellipse fitting on the accumulated spectrum to obtain the astigmatism coefficient. The focusing module is used to acquire the focusing coefficients of multiple sampling points near the predicted focusing point by changing the objective lens current, and to fit the multiple focusing coefficients to obtain the focusing midpoint; The astigmatism correction module is used to identify the two feature points with the greatest astigmatism intensity on both sides of the focal midpoint, and to perform linear regression fitting on the astigmatism coefficient and the objective lens distance within the interval defined by the two feature points to obtain the astigmatism gradient vector. The correction direction is determined based on the astigmatism gradient vector, fine-tuning sampling is performed along the correction direction, and the optimal astigmatism control parameter that minimizes the astigmatism intensity is solved. The astigmatism correction coil in the charged particle beam imaging device is adjusted based on the optimal astigmatism control parameter.

9. A terminal, characterized by comprising: The terminal includes: a memory, a processor, and an automatic focus astigmatism correction program for a charged particle beam imaging device stored in the memory and executable on the processor. When the processor executes the automatic focus astigmatism correction program for a charged particle beam imaging device, it implements the steps of the automatic focus astigmatism correction method for a charged particle beam imaging device as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an automatic focus astigmatism correction program for a charged particle beam imaging device, which, when executed by a processor, implements the steps of the automatic focus astigmatism correction method for a charged particle beam imaging device as described in any one of claims 1-7.

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