Method and device for optimizing imaging parameters of scanning electron microscope, equipment and medium

Abstract

The embodiment of the invention discloses an optimization method and device for imaging parameters of a scanning electron microscope, equipment and a medium, and relates to the technical field of image processing. The method comprises the following steps: acquiring a target image of a target scanning electron microscope; based on preset brightness and contrast parameter combinations, parameterized image simulation is carried out on the target image, and simulation images corresponding to the parameter combinations are generated; respectively determining a representation value of each simulation image according to the visual characteristics of each simulation image; and determining a target imaging parameter from each parameter combination according to the characterization value, and sending the target imaging parameter to an imaging control unit of the scanning electron microscope. According to the scheme provided by the embodiment of the invention, the optimal imaging parameter can be quickly and accurately determined, so that the optimal image can be acquired, and help can be provided for subsequent research.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to a method, apparatus, device, and medium for optimizing imaging parameters of a scanning electron microscope. Background Technology

[0002] A scanning electron microscope (SEM) is a high-resolution microscope that uses a focused, narrow beam of high-energy electrons to scan the surface of a sample line by line, imaging the image by detecting various physical signals generated by the interaction between electrons and matter. SEM is widely used in materials science, nanotechnology, biology, medicine, geology, and other fields to study the microstructure, morphological characteristics, and chemical composition of materials.

[0003] In the imaging process of scanning electron microscopy, image quality is highly dependent on the proper setting of imaging parameters such as brightness and contrast. Traditional operation relies on technicians repeatedly adjusting based on experience, which is inefficient, highly subjective, and results in unstable image quality.

[0004] How to quickly and accurately determine the optimal imaging parameters and acquire the best images to help with subsequent research is a key issue in the field. Summary of the Invention

[0005] This invention provides a method, apparatus, device, and medium for optimizing imaging parameters of a scanning electron microscope, so as to quickly and accurately determine the optimal imaging parameters to acquire the best images, which can help subsequent research.

[0006] According to one aspect of the present invention, a method for optimizing imaging parameters of a scanning electron microscope is provided, the method comprising:

[0007] Acquire target images using a scanning electron microscope;

[0008] Based on a preset combination of brightness and contrast parameters, the target image is subjected to parametric image simulation to generate simulated images corresponding to each of the parameter combinations.

[0009] The representation value of each simulation image is determined based on its visual characteristics.

[0010] The target imaging parameters are determined from each of the parameter combinations based on the characterization values, and the target imaging parameters are sent to the imaging control unit of the scanning electron microscope.

[0011] According to another aspect of the present invention, an apparatus for optimizing imaging parameters of a scanning electron microscope is provided, the apparatus comprising:

[0012] The image acquisition module is used to acquire target images using a scanning electron microscope.

[0013] The simulation image generation module is used to perform parametric image simulation on the target image based on a preset combination of brightness and contrast parameters, and generate simulation images corresponding to each of the parameter combinations.

[0014] The characterization value determination module is used to determine the characterization value of each of the simulated images based on the visual characteristics of each simulated image.

[0015] The target imaging parameter determination module is used to determine the target imaging parameters from each of the parameter combinations based on the characterization values, and send the target imaging parameters to the imaging control unit of the scanning electron microscope.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the method for optimizing scanning electron microscope imaging parameters according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for optimizing scanning electron microscope imaging parameters according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method for optimizing scanning electron microscope imaging parameters as described in any embodiment of the present invention.

[0022] The technical solution of this invention involves acquiring a target image from a scanning electron microscope; performing parametric image simulation on the target image based on preset combinations of brightness and contrast parameters to generate simulated images corresponding to each parameter combination; determining the characterization value of each simulated image based on its visual characteristics; providing assistance for subsequent screening to obtain the optimal target imaging parameters; determining the target imaging parameters from each parameter combination based on the characterization value, and sending the target imaging parameters to the imaging control unit of the scanning electron microscope. This allows for the rapid and accurate determination of the optimal imaging parameters to acquire the best image, which can assist in subsequent research.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of a method for optimizing scanning electron microscope imaging parameters according to Embodiment 1 of the present invention;

[0026] Figure 2 This is a flowchart of a method for optimizing scanning electron microscope imaging parameters according to Embodiment 2 of the present invention;

[0027] Figure 3 This is a schematic diagram of a scanning electron microscope imaging parameter optimization device according to Embodiment 3 of the present invention;

[0028] Figure 4 This is a schematic diagram of the structure of an electronic device for implementing the method for optimizing scanning electron microscope imaging parameters according to embodiments of the present invention. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0031] Example 1

[0032] Figure 1 This is a flowchart of a method for optimizing imaging parameters of a scanning electron microscope according to Embodiment 1 of the present invention. This embodiment is applicable to situations where imaging parameters of a scanning electron microscope are optimized. This method can be executed by a device for optimizing imaging parameters of a scanning electron microscope. This device can be implemented in hardware and / or software, and can be configured in electronic devices such as computers, servers, tablet computers, or scanning electron microscopes. Figure 1 As shown, the method includes:

[0033] Step 110: Acquire the target image using a scanning electron microscope.

[0034] The target scanning electron microscope can be any scanning electron microscope; in this embodiment, it is simply named the target scanning electron microscope for ease of description. The target image of the target scanning electron microscope can be an image acquired by the target scanning electron microscope for any object, such as biological samples such as metal alloys, cells, or nanowires; in this embodiment, it is not limited to any particular object.

[0035] Optionally, in this embodiment, when the target scanning electron microscope is in a stable imaging state, the current frame image can be read in real time through the image output interface; the current frame image can be effectively cropped or its resolution scaled to generate a target image for parameter simulation.

[0036] In an optional implementation of this embodiment, when the target scanning electron microscope is in a stable imaging state, the raw image data of the current frame can be captured in real time through its image output interface (e.g., camera link, USB interface, or dedicated image stream interface of SEM). Furthermore, based on preset region of interest coordinates or automatically detected effective field of view, the raw image can be cropped to remove edge distortion or invalid pixel areas. In this embodiment, if the original image resolution is too high, leading to excessive computational overhead in subsequent simulations, or too low, affecting the accuracy of quality assessment, a resolution scaling operation (e.g., bilinear interpolation or Gaussian pyramid downsampling) can be further performed to generate a standardized target image with appropriate size and complete content.

[0037] For example, when a target scanning electron microscope acquires an image with a resolution of 4096×3072 at a magnification of 10,000, the effective imaging area is detected to be within the central 2560×1920 pixel range. Therefore, this area is cropped and downsampled to a resolution of 1024×768 to balance computational efficiency and image detail preservation. Finally, the 1024×768 image is used as the target image and input to the simulation module to generate a set of simulation images under different combinations of brightness and contrast.

[0038] Step 120: Based on the preset combination of brightness and contrast parameters, perform parametric image simulation on the target image to generate simulation images corresponding to each parameter combination.

[0039] Brightness can refer to the global offset applied to the grayscale values ​​of all pixels in the image; contrast can be a scaling factor for the dynamic range of grayscale in the image, which can be used to enhance or compress the difference between light and dark. In this embodiment, the combination of brightness and contrast parameters is a set of discrete (brightness, contrast) paired values. These parameter combinations constitute the candidate set of imaging parameters to be evaluated. For example, (B=-20, C=0.8), (B=0, C=1.0), or (B=15, C=1.2), etc., where B is brightness and C is contrast.

[0040] Optionally, in this embodiment, after obtaining the standardized target image, a pixel-by-pixel affine transformation can be performed on the target image according to a preset combination of brightness and contrast parameters: for example, the original grayscale value can be linearly scaled according to the contrast parameter to adjust the dynamic range, and then the brightness parameter can be superimposed to achieve an overall brightness shift; further, the output value is truncated (e.g., limited to the range of 0-255) and quantized to generate a simulation image corresponding to each set of parameters.

[0041] Step 130: Determine the representation value of each simulation image based on its visual characteristics.

[0042] Among them, the visual characteristics of each simulated image can be the quality attributes of each image that are quantified and perceived, such as edge sharpness (reflecting the sharpness of the sample's microstructure boundary, usually positively correlated with the local gray-level gradient intensity), gray-level distribution complexity (reflecting the amount of effective visual information carried by the image, manifested as the discreteness and non-uniformity of the gray-level distribution), or imaging defect features (e.g., negative visual phenomena such as overall overbrightness or underbrightness, low contrast leading to gray fogging, or lack of effective gray levels).

[0043] In this embodiment, the representation value of each simulated image is a scalar value, which can be used to comprehensively quantify the overall visual quality of a simulated image. The higher the value, the better the image performs in terms of sharpness, information richness, and imaging rationality. Essentially, it is a fusion evaluation result of multi-dimensional visual characteristics.

[0044] Optionally, in this embodiment, the visual characteristics of each simulated image can be determined. A comprehensive numerical value, namely the representation value involved in this embodiment, is calculated based on these visual characteristics. In specific implementation, this process may include: analyzing the spatial structure information and grayscale distribution characteristics of the image to extract objective indicators that reflect human visual perception preferences; inputting these indicators into a preset evaluation function to generate a scalar output as the representation value of the simulated image. This representation value can be used to compare the relative merits of images under different parameter combinations; a higher value indicates better overall visual quality of the image.

[0045] For example, suppose there are three simulated images A, B, and C: Image A: sharp edges, rich grayscale levels, but overall too bright (average brightness = 230); Image B: blurred edges, grayscale concentrated in the middle area (standard deviation = 8), but moderate brightness; Image C: clear edges, wide grayscale distribution (standard deviation = 45, effective grayscale levels = 60), normal brightness (average = 140). Calculations show that Image A has a high base characteristic value, but is attenuated by 20% because its brightness exceeds a preset upper limit (e.g., greater than 220); Image B has a low base characteristic value (weak in both sharpness and complexity), with no attenuation; Image C has the highest base characteristic value and is defect-free, with no attenuation. The final characteristic value ranking is: C > A > B. Therefore, the parameter combination corresponding to C is selected as the target imaging parameter.

[0046] Step 140: Determine the target imaging parameters from each parameter combination based on the characterization values, and send the target imaging parameters to the imaging control unit of the scanning electron microscope.

[0047] Among them, the target imaging parameters are the optimal parameter set selected from all candidate parameter combinations. The corresponding simulated image has the highest characterization value and is expected to achieve the best actual imaging effect. The imaging control unit is the control module inside the scanning electron microscope responsible for adjusting imaging-related hardware parameters such as electron beam intensity, detector gain, and signal amplification. It typically receives external commands through device drivers or communication protocols.

[0048] Optionally, in this embodiment, after obtaining the characterization values ​​corresponding to each simulated image, all characterization values ​​can be compared, and the parameter combination associated with the characterization value with the largest value can be selected as the target imaging parameter. Furthermore, through a preset device communication interface, the target imaging parameter is sent to the imaging control unit of the scanning electron microscope in the form of a standard control command, triggering it to automatically update the current brightness and contrast settings, thereby realizing closed-loop optimization of the imaging parameters.

[0049] For example, suppose five sets of brightness-contrast parameter combinations are generated, and the corresponding characteristic values ​​are calculated as follows: 82.3, 76.5, 91.7, 88.2, and 79.4. The maximum characteristic value is identified as 91.7, and its corresponding parameter combination is (brightness = 18, contrast = 1.25). Subsequently, the device driver interface of the target scanning electron microscope can be invoked to send the command to the imaging control unit: SET BRIGHTNESS 18; SET CONTRAST 1.25. After receiving the command, the scanning electron microscope immediately adjusts the high-voltage bias and signal gain circuits so that the subsequently acquired images are imaged according to these parameters, completing the automatic optimization process.

[0050] The technical solution of this embodiment involves acquiring a target image from a scanning electron microscope; performing parametric image simulation on the target image based on preset combinations of brightness and contrast parameters to generate simulated images corresponding to each parameter combination; determining the characterization value of each simulated image based on its visual characteristics; providing assistance for subsequent screening to obtain the optimal target imaging parameters; determining the target imaging parameters from each parameter combination based on the characterization values, and sending the target imaging parameters to the imaging control unit of the scanning electron microscope. This allows for the rapid and accurate determination of the optimal imaging parameters to acquire the best image, which can assist in subsequent research.

[0051] Example 2

[0052] Figure 2 This is a flowchart of a method for optimizing imaging parameters of a scanning electron microscope according to Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown, the method includes:

[0053] Step 210: Acquire the target image using a scanning electron microscope.

[0054] Step 220: Based on the preset combination of brightness and contrast parameters, perform parametric image simulation on the target image to generate simulation images corresponding to each parameter combination.

[0055] Optionally, in this embodiment, parametric image simulation is performed on the target image based on a preset combination of brightness and contrast parameters to generate simulated images corresponding to each parameter combination. This may include: selecting multiple brightness values ​​within a preset brightness range and multiple contrast values ​​within a preset contrast range; generating multiple sets of brightness and contrast parameter combinations by combining the brightness and contrast values; shifting the pixel values ​​of the target image based on the target brightness value in the target parameter combination; scaling the shifted pixel values ​​based on the contrast value in the target parameter combination; adjusting the processing result within a preset grayscale dynamic range; and generating a target simulated image corresponding to the target parameter combination.

[0056] In this embodiment, the preset brightness range can be a brightness adjustment interval pre-set according to the imaging characteristics and grayscale representation capability of the target scanning electron microscope, for example, [-30, +30], with the unit being grayscale offset. This range ensures that the simulation results are neither excessively distorted nor overly distorted, while still covering typical manual adjustment ranges. The preset contrast range can be used to control the gain coefficient range for scaling the image's grayscale dynamic range, for example, [0.7, 1.5]. It is understood that a contrast value greater than 1 enhances the difference between light and dark areas, while a contrast value less than 1 compresses the dynamic range. The preset grayscale dynamic range refers to the effective grayscale range supported by the image data format, such as 0, 255 for an 8-bit image. Pixel values ​​exceeding this range need to be limited or normalized to ensure the legal usability of the output image.

[0057] In this embodiment, the offset processing set adds a brightness value B to the grayscale value of each pixel in the target image to achieve overall brightness adjustment. The scaling processing involves linearly scaling the offset pixel values ​​by a contrast value C, centered on the grayscale median.

[0058] In an optional implementation of this embodiment, after obtaining the target image, multiple values ​​can be discretely sampled within a preset range of brightness and contrast values, and multiple sets of brightness-contrast parameter combinations can be generated through a full combination method. Further, for any target parameter combination, its brightness value is first superimposed onto each pixel of the target image to complete a global offset; then, using a preset grayscale center as a reference, the offset pixels are linearly scaled using the contrast value; finally, the scaling result is cropped or mapped to a preset grayscale dynamic range (e.g., 0-255) to generate a simulated image conforming to image format specifications for subsequent quality evaluation.

[0059] Step 230: Determine the representation value of each simulation image based on the visual characteristics of each simulation image.

[0060] Optionally, in this embodiment, determining the characterization value of each simulation image based on the visual characteristics of each simulation image may include: determining a first quantization index corresponding to each simulation image based on the severity of local grayscale changes in each simulation image; determining a second quantization index corresponding to each simulation image based on the discreteness and non-uniformity of grayscale value distribution in each simulation image; and weighting and fusing the first quantization index and the second quantization index to obtain the characterization value.

[0061] The first quantitative index is used to reflect the edge sharpness of each simulated image, i.e., the sharpness index; the second quantitative index is used to reflect the grayscale distribution complexity of each simulated image, i.e., the information content index.

[0062] In an optional implementation of this embodiment, for each simulated image, a first quantization index can be calculated. For example, the local grayscale changes of the image in the horizontal and vertical directions can be obtained by applying a gradient operator, and the edge sharpness can be quantified based on the statistics of the gradient magnitude (e.g., square, mean). Then, a second quantization index is calculated. For example, the distribution characteristics of the image grayscale histogram can be analyzed to extract statistical features reflecting the dispersion and non-uniformity of grayscale values ​​(e.g., standard deviation, number of effective grayscale levels, or normalized entropy) to characterize the richness of visual information contained in the image. Finally, the first quantization index and the second quantization index are linearly weighted and fused according to preset weights to generate a comprehensive scalar value, that is, the characterization value of the simulated image, for subsequent parameter optimization.

[0063] In an optional implementation of this embodiment, the representation value of each simulated image can be determined using the following formula:

[0064] ;

[0065] in, H is the first quantitative indicator; P is the second quantitative indicator; b P c P e P g These correspond to luminance penalty, combined luminance and contrast penalty, exposure penalty, and grayscale penalty, respectively; α and β are weighting coefficients, which can take values ​​of 0.6 and 0.4, respectively; w b w c w e w g These represent the weights of each penalty item, with values ​​of 0.6, 0.6, 2.0, and 0.6.

[0066] In this embodiment, the first quantitative index (Laplace variance) can be calculated in the following manner:

[0067] ;

[0068] in, N is the number of effective pixels. y is the mean; I(x,y) is the original image.

[0069] The first quantitative indicator (entropy) can be calculated in the following way:

[0070] ;

[0071] in, ; To prevent a small constant log0.

[0072] Brightness penalty P b It can be calculated in the following way:

[0073] ;

[0074] in, Center of target brightness This is the default setting, with a default value of 10.

[0075] Brightness and contrast combined penalty P c It can be calculated as follows: First, calculate the brightness deviation: Luminance mask: ;

[0076] This is the brightness tolerance threshold, with a default value of 10. The contrast tolerance range is:

[0077] ;

[0078] in, .

[0079] Contrast deviation penalty: ;

[0080] Brightness and contrast combined penalty P c :

[0081] .

[0082] Exposure and punishment P e It can be calculated in the following way:

[0083] ;

[0084] in, As punishment for the secret service, Punishment for Liang; ;

[0085] ; .

[0086] Optionally, in this embodiment, determining the characterization value of each simulated image based on its visual characteristics may further include: determining the overall brightness level, grayscale distribution broadening, and effective grayscale level count of each simulated image; determining that the target simulated image has a brightness anomaly when the overall brightness level of the target simulated image is lower than a first brightness threshold or higher than a second brightness threshold; determining that the target simulated image has a contrast anomaly when the grayscale distribution broadening of the target simulated image is lower than a first contrast threshold; determining that the target simulated image has grayscale level degradation when the effective grayscale level count of the target simulated image is lower than a first level threshold; and, in cases where the target simulated image has brightness anomaly, contrast anomaly, or grayscale level degradation, attenuating the characterization value based on the deviation of the overall brightness level from the corresponding brightness threshold, the difference between the grayscale distribution broadening and the first contrast threshold, or the difference between the effective grayscale level count and the first level threshold, to obtain a corrected characterization value for the target simulated image.

[0087] In one optional implementation of this embodiment, when evaluating the quality of each simulated image, three key statistics for each image can be calculated: overall brightness level (which can be represented by the average gray value of the entire image), gray-level distribution broadening (which can be measured by the standard deviation of the gray-level histogram or the normalized entropy), and the number of effective gray levels (defined as the total number of gray levels whose pixel percentage exceeds a preset proportion (e.g., 0.1%)). Subsequently, the overall brightness level is compared with a preset first brightness threshold (lower limit) and a second brightness threshold (upper limit). If it is lower than the lower limit or higher than the upper limit, it is determined that there is a brightness anomaly. The gray-level distribution broadening is compared with a first contrast threshold. If it is lower than the threshold, it is determined that there is a contrast anomaly (manifested as a grayish image and blurred details). The number of effective gray levels is compared with a first level threshold. If it is lower than the threshold, it is determined that there is gray-level degradation (manifested as a lack of detail and insufficient information in the image).

[0088] In this embodiment, when any of the above-mentioned anomalies is confirmed, an attenuation factor between 0 and 1 can be calculated based on the degree of quantitative deviation between the anomaly index and the corresponding threshold (e.g., the absolute difference of brightness exceeding the upper limit, the relative difference of standard deviation below the threshold, the number of effective gray levels missing, etc.). This factor is then multiplied by the original characterization value to obtain the corrected characterization value, ensuring that the final score can truly reflect the imaging rationality of the image beyond sharpness and information content.

[0089] Step 240: Determine the target imaging parameters from the parameter combinations based on the characterization values.

[0090] Optionally, in this embodiment, determining the target imaging parameters from each parameter combination based on the characterization values ​​may include: comparing the characterization values ​​of each simulated image; if the maximum value among the characterization values ​​is not lower than a preset quality threshold, determining the parameter combination corresponding to the maximum value as the target imaging parameter; otherwise, prompting the user to adjust the parameter search range.

[0091] In one optional implementation of this embodiment, after obtaining the corrected characterization values ​​corresponding to each simulated image, all characterization values ​​can be iterated and compared to find the maximum value; further, it is determined whether the maximum value reaches a preset quality threshold (the threshold represents the lowest acceptable imaging quality level); if the maximum characterization value is not lower than the threshold, it is considered that there is a usable high-quality imaging scheme within the current parameter search range, and the brightness-contrast parameter combination corresponding to the maximum value can be determined as the target imaging parameter and used for subsequent control of target scanning electron microscope imaging.

[0092] Optionally, if the maximum characterization value is lower than the quality threshold, it means that the current parameter combination cannot generate an image that meets the basic visual quality requirements. A prompt message will be generated (e.g., through a pop-up window in the user interface or log output), suggesting that the user expand the search range of brightness or contrast, adjust the sample status, or reacquire the target image to avoid adopting inferior parameters that lead to invalid imaging.

[0093] In one example of this embodiment, it is assumed that 8 sets of parameter combinations were generated, and the calculated corrected characterization values ​​are 0.42, 0.51, 0.58, 0.63, 0.55, 0.49, 0.53, and 0.60, respectively; the preset quality threshold is set to 0.70. The maximum characterization value is 0.63, which is lower than the threshold of 0.70; it can be determined that the quality of all simulated images is substandard; an automatic prompt mechanism is triggered, displaying on the operation software interface: "No setting that meets the imaging quality requirements was found within the current parameter range. Please try expanding the brightness / contrast search range or checking the sample conductivity and focus status."

[0094] Conversely, if the maximum characterization value is 0.75 (greater than 0.70), the corresponding parameter combination (e.g., brightness = +15, contrast = 1.2) can be automatically selected as the target imaging parameters and sent to the imaging control unit of the target scanning electron microscope for execution.

[0095] Step 250: After determining the target imaging parameters, apply the target imaging parameters to a scanning electron microscope to acquire images and obtain new actual images; based on the new actual images, repeat the steps of generating simulation images, determining characterization values, and determining target imaging parameters until the preset convergence conditions are met.

[0096] The repeated execution process supports parameter optimization for sample regions at different magnifications, or parallel optimization for multiple sample regions.

[0097] Optionally, in this embodiment, after determining the target imaging parameters, the parameters can be sent to the imaging control unit of the scanning electron microscope through the device control interface, driving it to acquire a new actual image according to the brightness and contrast settings; then, the new image is used as the input target image for the next round of optimization, and the complete process of generating a simulation image, calculating the characterization value, and selecting the target imaging parameters is automatically repeated; this iterative process continues until the preset convergence condition is met (for example, the change in the target imaging parameters obtained from two consecutive iterations is less than the set tolerance, or the improvement of the characterization value is less than the threshold, or the maximum number of iterations is reached).

[0098] Throughout the closed-loop optimization process, the above iterative optimization process can be executed independently for multiple field-of-view regions of the same sample at different magnifications to adapt to the differences in imaging characteristics caused by magnification changes; or multiple independent optimization tasks can be launched in parallel for multiple sample regions (e.g., multiple chip sites in the same batch) to achieve batch, high-throughput automated parameter tuning.

[0099] For example, in an automated parameter tuning task for a target scanning electron microscope, the user selects a sample region containing undulating structures and sets the initial magnification to 5000×. After the first optimization, the target parameters are obtained (B=+12, C=1.15), and the target scanning electron microscope is controlled to acquire new images according to these parameters. Analysis shows that the characterization value improved from 0.68 to 0.74, but still did not converge. The system automatically generates a simulation set again using the new images as input and optimizes, obtaining (B=+14, C=1.18) in the second round, with the characterization value increasing to 0.76. The result of the third iteration deviates from the parameters of the second iteration by less than the preset tolerance (ΔB<2, ΔC<0.03), indicating convergence, and the iteration stops.

[0100] The technical solution in this embodiment achieves adaptive iterative adjustment of imaging parameters by using newly acquired actual images as input for the next round of parameter simulation, effectively overcoming the evaluation bias caused by relying solely on a single simulation and the initial image. Simultaneously, a convergence criterion is introduced to avoid invalid loops, ensuring efficient and stable optimization. Furthermore, this method supports independent optimization at multiple magnifications and parallel processing of multiple sample regions, significantly improving the system's applicability and practicality in complex samples, high-throughput detection, and automated production line scenarios, truly realizing intelligent imaging control.

[0101] To better understand the method for optimizing scanning electron microscope imaging parameters involved in this embodiment, a specific example is used below, which mainly includes the following steps:

[0102] 1. Real-time image acquisition: The system acquires SEM images in real time and can scale or crop the ROI to reduce computational costs.

[0103] 2. Parameter combination generation and simulation adjustment: Automatically simulates manual knob operation, and uses traversal or search algorithms to generate multiple sets of brightness and contrast parameter combinations; each set of parameters corresponds to an image after simulation adjustment.

[0104] 3. Image quality scoring mechanism: A multi-index fusion scoring method is adopted, including sharpness and entropy value, while introducing brightness penalty, contrast penalty and exposure penalty; the image corresponding to each set of parameters is quantitatively scored to objectively reflect the comprehensive effect of brightness and contrast.

[0105] (1) Sharpness index: Laplace variance measures the sharpness of image edges;

[0106] (2) Information content index: Image grayscale entropy measures the complexity of texture information;

[0107] (3) Brightness and contrast penalty: Square penalty for median gray level deviating from the ideal brightness center; weighted penalty for contrast abnormality combined with brightness deviation; exposure penalty: exponential penalty based on low / high percentile pixels of the image to avoid being too dark or overexposed; gray level missing penalty (optional): exponential penalty for insufficient effective gray level to avoid "gray haze".

[0108] 4. Optimal parameter selection and mapping control: Based on the scoring results, select the parameter group with the highest score; map the selected parameters to the SEM control terminal to realize automatic hardware adjustment; optionally, combine with post-acquisition image enhancement methods to further optimize the imaging effect.

[0109] 5. Closed-loop optimization system: Constructs a "collection-scoring-feedback-optimization" closed loop to achieve real-time, automated parameter adjustment; supports continuous acquisition, multi-magnification sample adjustment, and batch optimization.

[0110] The solution of this invention avoids traditional sequential acquisition and manual observation, significantly improving parameter adjustment efficiency. It comprehensively considers sharpness, entropy, and brightness / contrast / exposure penalties to quantitatively evaluate overall image quality, resolving parameter coupling issues. It automatically selects the optimal parameters and maps them to hardware, achieving an organic combination of pre-acquisition parameter optimization and post-acquisition enhancement.

[0111] Example 3

[0112] Figure 3 This is a schematic diagram of a scanning electron microscope imaging parameter optimization device according to Embodiment 3 of the present invention. Figure 3 As shown, the device includes: an image acquisition module 310, a simulation image generation module 320, a characterization value determination module 330, and a target imaging parameter determination module 340.

[0113] The image acquisition module 310 is used to acquire target images from a target scanning electron microscope.

[0114] The simulation image generation module 320 is used to perform parametric image simulation on the target image based on a preset combination of brightness and contrast parameters, and generate simulation images corresponding to each parameter combination.

[0115] The characterization value determination module 330 is used to determine the characterization value of each simulation image based on the visual characteristics of each simulation image.

[0116] The target imaging parameter determination module 340 is used to determine the target imaging parameters from various parameter combinations based on the characterization values, and send the target imaging parameters to the imaging control unit of the scanning electron microscope.

[0117] In an optional implementation of this embodiment, the image acquisition module 310 is specifically used to read the current frame image in real time through the image output interface when the target scanning electron microscope is in a stable imaging state.

[0118] Perform effective region cropping or resolution scaling on the current frame image to generate a target image for parametric simulation.

[0119] In an optional implementation of this embodiment, the simulation image generation module 320 is specifically used to select multiple brightness values ​​within a preset brightness value range and multiple contrast values ​​within a preset contrast value range, and generate multiple sets of brightness and contrast parameter combinations by combining each brightness value with each contrast value.

[0120] The pixel values ​​of the target image are offset based on the target brightness value in the target parameter combination, and the offset pixel values ​​are scaled based on the contrast value in the target parameter combination. The processing result is adjusted within the preset grayscale dynamic range to generate a target simulation image corresponding to the target parameter combination.

[0121] In an optional implementation of this embodiment, the characterization value determination module 330 is specifically used to determine the first quantization index corresponding to each simulation image based on the severity of local grayscale changes in each simulation image.

[0122] Based on the degree of dispersion and non-uniformity of gray value distribution in each simulation image, a second quantization index corresponding to each simulation image is determined.

[0123] The first quantitative indicator and the second quantitative indicator are weighted and fused to obtain the representation value;

[0124] The first quantitative indicator is used to reflect the edge sharpness of each simulated image;

[0125] The second quantitative indicator is used to reflect the complexity of the grayscale distribution of each simulated image.

[0126] In an optional implementation of this embodiment, the characterization value determination module 330 is further specifically used to determine the overall brightness level, grayscale distribution broadening degree, and effective grayscale level number of each simulated image.

[0127] If the overall brightness level of the target simulation image is determined to be lower than the first brightness threshold or higher than the second brightness threshold, it is determined that the target simulation image has a brightness anomaly.

[0128] If the grayscale distribution broadening of the target simulation image is determined to be lower than the first contrast threshold, it is determined that the target simulation image has a contrast anomaly.

[0129] If the number of effective gray levels in the target simulation image is determined to be lower than the first level threshold, it is determined that the target simulation image has gray level degradation.

[0130] When the target simulation image has abnormal brightness, abnormal contrast, or gray level distortion, the characterization value is attenuated according to the deviation of the overall brightness level from the corresponding brightness threshold, the difference between the gray level distribution broadening degree and the first contrast threshold, or the difference between the effective gray level number and the first level threshold, so as to obtain the corrected characterization value of the target simulation image.

[0131] In an optional implementation of this embodiment, the target imaging parameter determination module 340 is specifically used to compare the characterization values ​​of each simulated image, and if the maximum value among the characterization values ​​is not lower than a preset quality threshold, the parameter combination corresponding to the maximum value is determined as the target imaging parameter.

[0132] Otherwise, prompt the user to adjust the parameter search range.

[0133] In an optional implementation of this embodiment, the scanning electron microscope imaging parameter optimization device further includes: an optimization module, used to apply the target imaging parameters to the scanning electron microscope for image acquisition after determining the target imaging parameters, thereby obtaining a new actual image;

[0134] Based on the new actual image, the steps of generating the simulation image, determining the characterization value and determining the target imaging parameter are repeated until the preset convergence condition is met.

[0135] The repeated execution process supports parameter optimization for sample regions at different magnifications, or parallel optimization for multiple sample regions.

[0136] The scanning electron microscope imaging parameter optimization device provided in this embodiment of the invention can execute the scanning electron microscope imaging parameter optimization method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0137] The collection, storage, use, processing, transmission, provision, and disclosure of target images involved in the technical solutions of this invention comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0138] Example 4

[0139] Figure 4 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0140] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from the storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0141] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0142] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for optimizing scanning electron microscope imaging parameters.

[0143] In some embodiments, the method for optimizing scanning electron microscope imaging parameters may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for optimizing scanning electron microscope imaging parameters described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method for optimizing scanning electron microscope imaging parameters by any other suitable means (e.g., by means of firmware).

[0144] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0145] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0146] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0147] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0148] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0149] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system. It addresses the shortcomings of traditional physical hosts and Virtual Private Servers (VPS) in terms of management difficulty and weak business scalability.

[0150] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0151] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

[0152] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements a database detection method as provided in any embodiment of this application.

[0153] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LANs or WANs—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0154] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0155] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A method for optimizing imaging parameters of a scanning electron microscope, characterized in that, The method includes: Acquire target images using a scanning electron microscope; Based on a preset combination of brightness and contrast parameters, the target image is subjected to parametric image simulation to generate simulated images corresponding to each of the parameter combinations. The representation value of each simulation image is determined based on its visual characteristics. The target imaging parameters are determined from each of the parameter combinations based on the characterization values, and the target imaging parameters are sent to the imaging control unit of the scanning electron microscope.

2. The method for optimizing scanning electron microscope imaging parameters according to claim 1, characterized in that, The target image acquired by the scanning electron microscope includes: When the target scanning electron microscope is in a stable imaging state, the current frame image is read in real time through the image output interface; The current frame image is cropped in an effective region or its resolution is scaled to generate the target image for parameter simulation.

3. The method for optimizing scanning electron microscope imaging parameters according to claim 1, characterized in that, The method of performing parametric image simulation on the target image based on preset combinations of brightness and contrast parameters to generate simulated images corresponding to each parameter combination includes: Multiple brightness values ​​are selected within a preset brightness range, and multiple contrast values ​​are selected within a preset contrast range. By combining each brightness value with each contrast value, multiple sets of brightness and contrast parameter combinations are generated. The pixel values ​​of the target image are offset based on the target brightness value in the target parameter combination, and the offset pixel values ​​are scaled based on the contrast value in the target parameter combination. The processing result is adjusted within a preset grayscale dynamic range to generate a target simulation image corresponding to the target parameter combination.

4. The method for optimizing scanning electron microscope imaging parameters according to claim 1, characterized in that, The step of determining the representation value of each simulated image based on its visual characteristics includes: Based on the degree of drastic change in local grayscale in each of the simulated images, a first quantization index corresponding to each of the simulated images is determined. Based on the degree of dispersion and non-uniformity of gray value distribution in each of the simulated images, a second quantization index corresponding to each of the simulated images is determined. The first quantitative indicator and the second quantitative indicator are weighted and fused to obtain the representation value; The first quantitative index is used to reflect the edge sharpness of each of the simulated images; The second quantification index characterizes the grayscale distribution complexity of each of the simulated images.

5. The method for optimizing scanning electron microscope imaging parameters according to claim 1, characterized in that, The step of determining the representation value of each simulated image based on the visual characteristics of each simulated image further includes: Determine the overall brightness level, grayscale distribution broadening degree, and effective grayscale level number of each simulated image; If the overall brightness level of the target simulation image is determined to be lower than a first brightness threshold or higher than a second brightness threshold, it is determined that the target simulation image has a brightness anomaly. If the grayscale distribution broadening of the target simulation image is determined to be lower than the first contrast threshold, it is determined that the target simulation image has a contrast anomaly. If the number of effective gray levels in the target simulation image is determined to be lower than the first level threshold, it is determined that the target simulation image has gray level degradation. If the target simulation image has abnormal brightness, abnormal contrast, or gray level distortion, the characterization value is attenuated according to the deviation of the overall brightness level from the corresponding brightness threshold, the difference between the gray level distribution broadening degree and the first contrast threshold, or the difference between the effective gray level number and the first level threshold, to obtain the corrected characterization value of the target simulation image.

6. The method for optimizing scanning electron microscope imaging parameters according to claim 1, characterized in that, Determining the target imaging parameters from each of the parameter combinations based on the characterization values ​​includes: Compare the characterization values ​​of each of the simulated images. If the maximum value among the characterization values ​​is not lower than a preset quality threshold, the parameter combination corresponding to the maximum value is determined as the target imaging parameter. Otherwise, prompt the user to adjust the parameter search range.

7. The method for optimizing scanning electron microscope imaging parameters according to any one of claims 1-6, characterized in that, The method further includes: After determining the target imaging parameters, the target imaging parameters are applied to a scanning electron microscope to acquire images and obtain new actual images; Based on the new actual image, the steps of generating the simulation image, determining the characterization value, and determining the target imaging parameters are repeated until the preset convergence condition is met. The repeated execution process supports parameter optimization for sample regions at different magnifications, or parallel optimization for multiple sample regions.

8. A device for optimizing imaging parameters of a scanning electron microscope, characterized in that, include: The image acquisition module is used to acquire target images using a scanning electron microscope. The simulation image generation module is used to perform parametric image simulation on the target image based on a preset combination of brightness and contrast parameters, and generate simulation images corresponding to each of the parameter combinations. The characterization value determination module is used to determine the characterization value of each of the simulated images based on the visual characteristics of each simulated image. The target imaging parameter determination module is used to determine the target imaging parameters from each of the parameter combinations based on the characterization values, and send the target imaging parameters to the imaging control unit of the scanning electron microscope.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which enables the at least one processor to perform the method for optimizing scanning electron microscope imaging parameters according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for optimizing scanning electron microscope imaging parameters according to any one of claims 1-7.

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