A method of adjusting and electronic device

Abstract

The application discloses a kind of adjustment method and electronic equipment, it is related to semiconductor equipment technical field, for improving the adjustment efficiency and accuracy of detector layout position.The method comprises: obtaining the signal detected by detector, signal is the signal generated by at least one particle beam in particle beam column acting on sample to be measured;Wherein, the surface of sample to be measured has the periodic pattern array alternately constituted by at least one first material region of first preset geometry and at least one second material region of second preset geometry, the material of any one first material region is different from the material of any one second material region;Based on the detection image generated by signal, the adjustment information of detector is determined;Adjustment information is used to indicate the adjustment direction and / or adjustment amount of adjusting the layout position of detector in particle beam column;Output adjustment information.

Description

Technical Field

[0001] This application relates to the field of semiconductor equipment technology, and more particularly to a regulation method and electronic device. Background Technology

[0002] In charged particle beam apparatuses such as scanning electron microscopes (SEMs), a charged particle beam is focused onto the sample surface, generating secondary electron (SE) and backscattered electron (BSE) signals. These signals are detected by detectors to observe the sample surface morphology and analyze the material composition. To improve the overall consistency of the detector signal quality in particle beam apparatuses, the detector's position within the apparatus needs to be precisely calibrated to ensure alignment with the reference optical axis.

[0003] However, currently, aligning the detector with the reference optical axis requires manual disassembly and reassembly of the hardware, which is time-consuming and has low adjustment accuracy. Summary of the Invention

[0004] This application discloses an adjustment method and electronic device for improving the adjustment efficiency and accuracy of detector deployment position.

[0005] In a first aspect, this application provides an adjustment method, the method comprising: acquiring a signal detected by a detector, the signal being a signal generated by at least one particle beam in a particle beam tube acting on a sample to be tested; wherein the detector is disposed within the particle beam tube, and the surface of the sample to be tested has a periodic pattern array consisting of at least one first material region of a first preset geometric shape and at least one second material region of a second preset geometric shape alternating, wherein the material of any first material region is different from the material of any second material region; determining adjustment information of the detector based on a detection image generated from the signal; the detection image is used to reflect the distribution of signal intensity, wherein the signal intensity corresponding to any first material region is different from the signal intensity corresponding to any second material region, and the adjustment information is used to indicate the adjustment direction and / or adjustment amount of adjusting the placement position of the detector within the particle beam tube; and outputting the adjustment information.

[0006] Based on the above technical solution, this adjustment method defines a periodic pattern array consisting of alternating first and second material regions of the sample. It utilizes the physical property of the signal intensity difference generated by the interaction of the two materials with the particle beam to generate an image. This allows the detection image to intuitively reflect the periodic structural features of the sample surface, providing clear and quantifiable feature targets for subsequent determination of adjustment information through the image. Furthermore, by directly acquiring the raw signal generated by the particle beam interacting with the sample and automatically generating the detection image, adjustment information is determined based on the image features. This adjustment information can indicate the placement position of the adjustment detector, improving the efficiency and accuracy of determining the detector's adjustment information.

[0007] In one possible implementation of the first aspect, determining the adjustment information of the detector based on the signal-generated detection image includes: dividing the detection image into regions to obtain region division results; wherein the region division results include at least one first image region and at least one second image region, the at least one first image region corresponds one-to-one with at least one first material region, the grayscale feature of each first image region is used to reflect the signal intensity of the first material region corresponding to the first image region, the at least one second image region corresponds one-to-one with at least one second material region, the grayscale feature of each second image region is used to reflect the signal intensity of the second material region corresponding to the second image region; there is a pixel band between any first image region and any second image region connected to any first image region in the at least one first image region, the pixel band indicating the boundary region between any first image region and any second image region connected to any first image region; determining the edge symmetry information of at least one target image region in the region division results; the at least one target image region is one or more of at least one first image region or one or more of at least one second image region, the edge symmetry information is used to reflect the degree of symmetry of the edges of each target image region in the at least one target image region; and determining adjustment information based on the edge symmetry information. The image region of the detection image is divided by signal strength. The inherent symmetry features of the periodic pattern array on the sample surface are used to quantify the position deviation of the detector by the edge symmetry information of the target region, and the adjustment information is determined.

[0008] In one possible implementation of the first aspect, the above-mentioned region division of the probe image to obtain the region division result includes: traversing the probe image using multiple sliding windows to obtain multiple sets of local grayscale features of the probe image; wherein any two sliding windows in the multiple sliding windows have different sizes, each sliding window in the multiple sliding windows is used to extract the local grayscale features of the probe image within that sliding window, and the multiple sliding windows correspond one-to-one with the multiple sets of local grayscale features; determining the pixel classification result of the probe image based on the multiple sets of local grayscale features and a preset grayscale threshold; the pixel classification result includes multiple classes of pixels, including first class pixels, second class pixels, and third class pixels, where the first class pixels correspond to at least one first image region, the second class pixels correspond to at least one second image region, and the third class pixels correspond to a pixel band; and dividing the probe image into regions according to the image regions corresponding to each class of pixels in the pixel classification result to obtain the region division result. The use of sliding windows allows the category determination of each pixel to combine the grayscale distribution information within its neighborhood, thereby distinguishing pixels with similar grayscale values ​​but potentially different corresponding material regions. Furthermore, gradient information within the window can more accurately locate gray-level abrupt changes, improving the classification accuracy of boundary pixels. Sliding windows of different sizes can also comprehensively determine the category of each pixel at multiple scales, improving the classification accuracy of boundary pixels through the fusion of multi-scale features.

[0009] In one possible implementation of the first aspect, determining the pixel classification result of the probe image based on multiple sets of local grayscale features and preset grayscale thresholds includes: acquiring the global grayscale features of the probe image; comparing the global grayscale features with preset grayscale thresholds corresponding to the first type of pixels, the second type of pixels, and the third type of pixels respectively, and comparing each set of local grayscale features in the multiple sets of local grayscale features with the preset grayscale thresholds corresponding to the first type of pixels, the second type of pixels, and the third type of pixels respectively, to obtain the initial classification result corresponding to the global grayscale features and each set of local grayscale features in the multiple sets of local grayscale features, the initial classification result including the first type of initial pixels, the second type of initial pixels, and the third type of initial pixels; and weighting and fusing the initial classification result with preset weight coefficients corresponding to each set of local grayscale features in the multiple sets of local grayscale features to obtain the pixel classification result. A global grayscale feature is introduced, and combined with the global grayscale feature and each group of local grayscale features, the preliminary classification result is determined by comparing each group of local grayscale features with a preset grayscale threshold. This enables rapid preliminary segmentation of the first, second and third class pixel regions in the image. The initial classification result is then weighted and fused by combining the weights of the global grayscale feature and each group of local grayscale features to comprehensively determine the final pixel classification result, thereby improving the accuracy of pixel classification.

[0010] In one possible implementation of the first aspect, the at least one target image region is determined based on the following method: determining at least one first candidate image region from the region segmentation result; wherein, the at least one first candidate image region is at least one first image region and / or at least one second image region; extending the boundary of each first candidate image region outward by a predetermined distance to obtain an outer ring region of each first candidate image region; determining one or more first candidate image regions whose outer ring region satisfies a preset surrounding distribution condition as at least one second candidate image region; wherein, the preset surrounding distribution condition includes that the pixel ratio of the pixel band in the outer ring region of the candidate image region is greater than a preset threshold; and determining at least one target image region from the at least one second candidate image region. By extending the boundary of the first candidate region to form an outer ring region, and selecting regions whose pixel ratio of the third image region satisfies a preset threshold as targets, invalid interference regions in the image caused by occlusion, contamination, or sample edge effects are eliminated, so that subsequent symmetry calculations can be performed only based on target image regions with complete structure and clear features, thereby improving the reliability of adjustment information generation.

[0011] In one possible implementation of the first aspect, determining at least one target image region within at least one second candidate image region includes: generating prompt information based on at least one second candidate image region, the prompt information being used to guide a pre-trained segmentation model to identify the target image region; inputting the prompt information and probe images into the segmentation model to obtain at least one candidate segmentation mask of a preset geometric shape output by the segmentation model and the confidence level of each candidate segmentation mask; determining one or more candidate segmentation masks among the at least one candidate segmentation masks whose confidence level is greater than a preset confidence threshold as at least one target segmentation mask, the at least one target segmentation mask corresponding one-to-one with at least one target image region, and each target segmentation mask being used to indicate the target image region. When determining the target region, prompt information is first generated based on geometric distribution conditions, and then input into the preset segmentation model along with the image. The semantic understanding capability of the deep learning model is used to further refine the segmentation mask, thereby enabling the segmentation model to effectively handle deformations, distortions, or weak features present in the image, generating accurate preset geometric shape segmentation masks, and improving the accuracy of the finally determined target image region.

[0012] In one possible implementation of the first aspect, generating prompt information based on at least one second candidate image region includes: in response to an operator's selection operation in at least one second candidate image region, determining one or more second candidate image regions selected by the operator; and generating prompt information to indicate the one or more second candidate image regions. This scheme combines automatic filtering results with the user's subjective selection, utilizing the efficiency of the algorithm while retaining the experience and judgment of human experts, thus improving the accuracy and flexibility of target image region selection.

[0013] In one possible implementation of the first aspect, generating prompt information based on at least one second candidate image region includes: calculating the ratio of the area to the perimeter of each second candidate image region; normalizing the ratio with the proportion of pixels belonging to the pixel band in the outer annular region, and weighting and fusing it based on a preset weight coefficient comparison value and the pixel proportion to obtain the shape fit degree of each second candidate image region; generating first sub-information, which indicates one or more second candidate image regions whose shape fit degree is greater than a first preset shape fit degree threshold, and the prompt information includes the first sub-information. Calculating shape compactness using the area-to-perimeter ratio and constructing a shape confidence evaluation model together with the edge pixel proportion can effectively eliminate candidate regions whose shapes do not meet the requirements.

[0014] In one possible implementation of the first aspect, the method further includes: acquiring the spatial location of each second candidate image region whose shape fitting degree is greater than a first preset shape fitting degree threshold; merging the second candidate image regions whose spatial locations are less than a preset distance threshold to obtain at least one merged candidate region; removing candidate regions whose shape fitting degree is less than a second preset confidence threshold from the at least one candidate region to obtain at least one third candidate image region; generating second sub-information, which is used to indicate at least one third candidate image region, and the prompt information also includes the second sub-information. Clustering and merging adjacent candidate regions using a spatial distance threshold can integrate scattered, nearby regions of the same type of feature into a unified image region to be detected, facilitating subsequent detection symmetry and optimizing the topological integrity of the target region. Furthermore, it can filter out interference points with insufficient confidence, helping to improve the accuracy of adjusting information determination.

[0015] In one possible implementation of the first aspect, the aforementioned edge symmetry information includes the ratio between the average gray values ​​of two parts obtained by dividing the outer annular region on both sides along the first direction and / or the second direction with the centroid of the target image region as a reference; the first direction and the second direction are both parallel to the bearing surface used to support the sample to be tested, and the first direction and the second direction are not collinear.

[0016] In one possible implementation of the first aspect, determining the adjustment information based on the edge symmetry information includes: analyzing the edge symmetry information to determine the positional deviation of the detector in the first direction and / or the second direction; and determining the adjustment information to indicate adjusting the positional deviation so that the ratio between the grayscale mean values ​​of the two parts is greater than or equal to 0.95 and less than or equal to 1.05.

[0017] In one possible implementation of the first aspect, the aforementioned adjustment information is used to instruct the adjustment mechanism of the detector to adjust the placement position of the detector within the particle beam tube. The adjustment mechanism includes a fixing member and an adjustment assembly. The fixing member is used to fixably connect to the particle beam tube, and the adjustment assembly connects the detector to the fixing member and is used to adjust the placement position of the detector. The adjustment assembly includes a first adjustment member for adjusting the placement position of the detector in a first direction and a second adjustment member for adjusting the placement position of the detector in a second direction. The adjustment information is used to instruct the adjustment of the first adjustment member and / or the second adjustment member to adjust the placement position of the detector. By using the first and second adjustment members arranged on the mounting member for adjusting the positions in the first and second directions, and defining the two adjustment directions as parallel to the bearing surface and forming non-zero, non-flat angles with each other, the adjustment assembly can drive the detector to independently displace in two directions within a plane parallel to the sample plane, thereby improving the flexibility and accuracy of the adjustment.

[0018] In one possible implementation of the first aspect, the first adjusting member includes at least two first set screws, which are arranged opposite to each other along a first direction and are used to push the detector from opposite directions to adjust the detector's placement position in the first direction. By providing at least two oppositely arranged first set screws in the first direction, and utilizing their respective pushes against the detector from opposite directions, adjustment of the detector in the first direction is achieved. Furthermore, by providing at least two bidirectionally opposing set screws, the reverse backlash phenomenon caused by thread fit clearance and mechanical wear when adjusting with only unidirectional set screws can be avoided, thus improving the adjustment accuracy in the first direction.

[0019] In one possible implementation of the first aspect, the second adjusting member includes at least four second adjusting screws, each of which is used to push against the detector along the second direction to adjust the detector's placement position in the second direction. By providing at least four second adjusting screws that all push against the detector along the second direction, the detector's movement in the second direction is made smoother and more evenly stressed through multi-point coordinated support and pushing. This helps maintain the detector's stable attitude during movement in the second direction, reduces measurement errors caused by attitude tilt, and improves the stability and accuracy of the detector during adjustment.

[0020] In one possible implementation of the first aspect, the method further includes: reacquiring a reference detection image after adjusting the deployment position; determining reference adjustment information based on the reference detection image if the reference edge symmetry information of the reference detection image does not meet preset conditions, wherein the reference edge symmetry information is used to reflect the result after the deployment position adjustment; and determining that the adjustment is complete if the reference edge symmetry information meets preset conditions or the number of adjustments reaches a preset number. Immediately after adjustment, the image is reacquired and the symmetry of the new image is checked. If it does not meet the standard, adjustment continues; if it meets the standard or the maximum number of adjustments is reached, adjustment stops. This reduces nonlinear errors, mechanical hysteresis, or environmental disturbances during the dynamic compensation adjustment process, improving the accuracy of the final adjustment result.

[0021] In one possible implementation of the first aspect, the reacquisition of the reference detection image after adjusting the deployment position includes: acquiring multiple consecutive detection images in the time dimension after adjusting the deployment position; calculating the variance of the edge symmetry information of the multiple consecutive detection images; and reacquiring the reference detection image if the variance is less than a preset stability threshold. By acquiring multiple consecutive frames and determining that the adjustment is complete only when the variance of the edge symmetry information is less than the preset threshold, the reliability of the detection data is improved by filtering out instantaneous noise, electronic jitter, or occasional fluctuations caused by mechanical micro-vibrations during particle beam scanning.

[0022] Secondly, this application provides an electronic device for implementing the various methods described above. The electronic device includes: The acquisition module is used to acquire the signal detected by the detector. The signal is generated by at least one particle beam in the particle beam tube acting on the sample to be tested. The detector is located inside the particle beam tube. The surface of the sample to be tested has a periodic pattern array composed of at least one first material region of a first preset geometry and at least one second material region of a second preset geometry, and the material of any first material region is different from the material of any second material region.

[0023] The determination module is used to determine the adjustment information of the detector based on the detection image generated by the signal. The detection image reflects the distribution of signal intensity, and the signal intensity corresponding to any first material region is different from the signal intensity corresponding to any second material region. The adjustment information is used to indicate the adjustment direction and / or adjustment amount of adjusting the placement position of the detector within the particle beam tube. The output module is used to output adjustment information.

[0024] For a more detailed description of each of the above modules, as well as a more detailed description of each of their technical features and a description of their beneficial effects, please refer to the methods in any of the possible designs in the first aspect above.

[0025] Thirdly, this application provides an electronic device for implementing the various methods described above. The electronic device includes a processor and a memory; the memory stores computer instructions that, when executed by the processor, cause the electronic device to perform any of the possible designs in the first aspect described above.

[0026] In some possible designs, the electronic device may include: a processor and a communication interface; the communication interface for communicating with modules outside the electronic device; and the processor for executing computer programs or instructions to cause the electronic device to perform the adjustment method in any of the possible designs of the first aspect described above.

[0027] Fourthly, this application provides a computer-readable storage medium that stores computer instructions that, when executed on a computer, cause the computer to perform any of the possible design adjustments in the first aspect.

[0028] Fifthly, this application provides a computer program product including computer instructions that, when executed on a computer, cause the computer to perform any of the possible design adjustments in the first aspect.

[0029] In a sixth aspect, this application provides a chip comprising: a processor for executing instructions that cause a device including the chip to perform any of the possible design adjustments described in the first aspect.

[0030] In conjunction with the sixth aspect mentioned above, in one possible implementation, the chip also includes a memory for storing instructions.

[0031] The technical effects of any possible implementation of aspects two through six can be found in the first aspect above, as well as the technical effects of any possible design of the first aspect above, which will not be repeated here. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 A schematic diagram of a detector adjustment system provided in an embodiment of this application; Figure 2 A schematic diagram of a detector provided for an embodiment of this application; Figure 3 A schematic diagram of another detector adjustment system provided in an embodiment of this application; Figure 4 A schematic diagram of an adjustment mechanism provided for an embodiment of this application; Figure 5 A schematic diagram of an electronic device provided in an embodiment of this application; Figure 6 A flowchart illustrating an adjustment method provided in an embodiment of this application; Figure 7 A schematic diagram of a detection image provided in an embodiment of this application; Figure 8 A schematic diagram of another detection image provided in an embodiment of this application; Figure 9 A flowchart illustrating another adjustment method provided in an embodiment of this application; Figure 10 A flowchart illustrating another adjustment method provided in an embodiment of this application; Figure 11 A flowchart illustrating another adjustment method provided in an embodiment of this application; Figure 12 A flowchart illustrating another adjustment method provided in an embodiment of this application; Figure 13 This is a schematic diagram of the composition of an electronic device provided in an embodiment of this application.

[0034] Explanation of reference numerals in the attached figures: 100-Detector adjustment system; 10-Particle beam tube; 20-Detector; 21-Electronic signal detection probe; 22-Optical waveguide; 23-Flange with sealing ring; 24-Signal processing module; 30-Adjustment mechanism; 31-First adjustment component; 32-Second adjustment component; 331-First mounting component; 332-Second mounting component; 333-Mounting bracket; 500 - First electronic device; 510 - First processor; 520 - Communication line; 530 - Communication interface; 540 - Memory; 550 - Output device; 560 - Input device; 570 - Second processor; 1300 - Second electronic device; 1301 - Acquisition module; 1302 - Determination module; 1303 - Output module. Detailed Implementation

[0035] The following sections will discuss the fabrication and use of various embodiments in detail. However, it should be understood that many applicable concepts provided in this application can be implemented in a variety of specific environments. The specific embodiments discussed are merely illustrative of specific ways to implement and use this description and technology, and do not limit the scope of this application.

[0036] Unless otherwise defined, all technical terms used herein have the same meaning as commonly known to one of ordinary skill in the art.

[0037] Each circuit or other component may be described or referred to as "for" performing one or more tasks. In this context, "for" is used to imply a structure by indicating that the circuit / component includes a structure (e.g., a circuit system) that performs one or more tasks during operation. Therefore, even when the specified circuit / component is currently inoperable (e.g., not turned on), it can still be referred to as "for performing that task." Circuits / components used with the term "for" include hardware, such as circuits that perform operations.

[0038] The technical solutions in the embodiments of this application will be described below with reference to the accompanying drawings. In this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, or B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the related objects before and after are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, a and b, a and c, b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0039] The embodiments of this application use terms such as "first" and "second" to distinguish objects with similar names, functions, or effects. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or order of execution. The term "coupling" is used to indicate an electrical connection, including direct connection via wires or terminals or indirect connection via other devices. Therefore, "coupling" should be considered as a broad type of electronic communication connection.

[0040] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0041] Particle beam detection technology is widely used in fields such as semiconductor detection, material composition analysis, and nanostructure characterization. For example, in particle beam equipment (such as electron beam or ion beam equipment), the detector is typically installed inside the microscope tube to receive secondary electron or backscattered electron signals generated by the interaction between the particle beam and the sample, thereby reconstructing the morphology image of the sample surface or performing material composition analysis. To improve the uniformity of the detection signal and the imaging quality, the detector needs to be precisely aligned with the system's reference optical axis, which is a key step in improving instrument performance.

[0042] However, current detector calibration methods typically require manual disassembly and adjustment of the hardware for each adjustment, followed by restoring the hardware to a test state and analyzing the signal detection results to determine if the adjustment has been achieved. This usually requires multiple iterations to achieve the final result. This calibration process involves numerous blind trials and iterations, resulting in low efficiency. Furthermore, the calibration process heavily relies on the operator's subjective experience and professional skills, demanding a high level of expertise. In addition, repeated hardware disassembly and screw adjustments not only prolong equipment debugging time but also increase the risk of damage to precision detector components during disassembly and assembly, impacting equipment lifespan and maintenance costs.

[0043] Therefore, how to achieve high-precision and rapid calibration of the detector position is a technical problem that urgently needs to be solved in this field.

[0044] In view of this, this application provides an adjustment method, which includes: acquiring a signal detected by a detector, wherein the signal is generated by at least one particle beam in a particle beam tube acting on a sample to be tested; wherein the detector is disposed inside the particle beam tube, and the surface of the sample to be tested has a periodic pattern array composed of at least one first material region of a first preset geometric shape and at least one second material region of a second preset geometric shape, wherein the material of any first material region is different from the material of any second material region; determining the adjustment information of the detector based on a detection image generated from the signal; the detection image is used to reflect the distribution of signal intensity of the signal, wherein the signal intensity corresponding to any first material region is different from the signal intensity corresponding to any second material region, and the adjustment information is used to indicate the adjustment direction and / or adjustment amount of adjusting the placement position of the detector in the particle beam tube; and outputting the adjustment information. In this way, the adjustment method, by defining a periodic pattern array composed of alternating first and second material regions of the sample, utilizes the physical characteristic that the two materials produce signal intensity differences when acting on the particle beam to generate an image, so that the detection image can intuitively reflect the periodic structural features of the sample surface, providing clear and quantifiable feature targets for subsequent determination of adjustment information through the image. Furthermore, by directly acquiring the original signal generated by the particle beam acting on the sample and automatically generating the detection image, the adjustment information is determined based on the image features. This adjustment information can indicate the placement position of the adjustment detector, thus improving the efficiency and accuracy of the determination of the detector adjustment information.

[0045] The adjustment method provided in this application can be applied to various beam imaging and analysis systems. For example, SEM focused ion beam systems (FIB), transmission electron microscopes (TEM), or other systems involving beam-matter interaction imaging, such as X-ray microscopy and neutron scattering imaging systems, as well as systems that require online calibration and alignment of the detection position of the signal receiving device using image features, all fall within the scope of this application.

[0046] In the above system, regardless of whether the detector is used to detect secondary electrons, backscattered electrons, characteristic X-rays, or ion signals, if there are imaging problems caused by detector installation deviations, the adjustment method provided in this application can be used to analyze sample images with preset geometric features (such as periodic arrays, regular gratings, or specific markings), automatically calculate the detector offset, and output adjustment information, thereby improving the determination efficiency and accuracy of detector adjustment information.

[0047] For example, a particle beam detection scenario will be used for illustration. Figure 1As shown, this application provides a detector adjustment system 100, which includes a particle beam mirror tube 10, a detector 20, and an adjustment mechanism 30.

[0048] The particle beam tube 10 is used to create and maintain an internal vacuum environment to minimize gas scattering interference and guide the stable transmission of the particle beam. Exemplarily, the particle beam tube 10 integrates a particle beam emitting device for emitting and accelerating at least one high-energy particle beam. The particle beam travels along the tube axis, bombarding the surface of the sample to be tested, placed at the bottom of the tube, inducing interactions between sample atoms and exciting physical excitation signals, including secondary electrons, backscattered electrons, or characteristic X-rays. These excitation signals radiate outwards from the sample surface, and a portion of these signals is received by a detector 20 located inside the particle beam tube 10. For example, the particle beam tube 10 is an electron beam tube, meaning the particle beam is an electron beam. Exemplarily, the electron beam tube integrates an electron gun assembly for emitting and accelerating at least one electron beam.

[0049] The detector 20 is disposed inside the particle beam mirror tube 10 (or mounted through its side wall). Exemplarily, the particle beam mirror tube 10 itself serves as the mounting cavity for the detector, and its side wall has a central through-hole through which the detector 20 passes radially, allowing the probe of the detector 20 to extend into the mirror tube and face the sample chamber. Multiple (e.g., four) threaded connection holes are evenly distributed circumferentially around the central through-hole; during assembly, the detector 20 is inserted into the central through-hole and secured by fastening bolts passing through corresponding through-holes on the detector 20 flange and screwed into the threaded connection holes. Optionally, an elastic sealing ring is press-fitted between the detector 20 flange and the side wall of the particle beam mirror tube 10 to maintain a vacuum seal.

[0050] For example, such as Figure 2 As shown, the detector 20 includes an electronic signal detection probe 21, an optical waveguide 22, a flange with a sealing rubber ring 23, and a signal processing module 24.

[0051] The electron signal detection probe 21 has a square groove (or square through hole). One end of the optical waveguide 22 is inserted into the square groove and fixedly connected to the electron signal detection probe 21 by pressing, so as to realize the coupled acquisition of particle signals (such as secondary electrons, backscattered electrons, or characteristic X-rays). The end of the optical waveguide 22 away from the electron signal detection probe 21 extends to the outside of the particle beam tube 10 and is connected to the signal processing module 24 for transmitting the acquired particle signals to the signal processing module 24 for subsequent processing.

[0052] A square through-hole extending along its thickness is provided on the flange 23 with a sealing ring. The optical waveguide 22 passes through this square through-hole, and the outer wall of the optical waveguide 22 is fixedly connected to the inner wall of the square through-hole. A sealing ring is fixed on each of the two opposite end faces of the flange 23 with a sealing ring (for example, when the detector 20 is assembled to the particle beam tube 10, the pressure difference generated by the vacuum inside the mounting cavity causes the sealing ring to be squeezed and thus produce elastic deformation to achieve a sealing effect). The sealing ring is arranged around the outer periphery of the square through-hole to achieve a vacuum seal between the detector 20 and the mounting cavity when the detector 20 is assembled to the particle beam tube 10.

[0053] For example, the flange 23 with sealing ring has multiple (e.g., four) threaded through holes distributed circumferentially. The signal processing module 24 has a protrusion for mounting, or mounting lug, on the side facing the flange 23, which has multiple threaded holes. By passing fastening bolts through the threaded holes on the mounting lug and screwing them into the threaded through holes on the flange 23, the signal processing module 24 can be fixedly connected to the flange 23. The flange 23 with sealing ring is further fixedly mounted to the outer wall of the particle beam tube 10 through its threaded through holes and bolts, thereby realizing the overall assembly and fixation of the detector 20 and the particle beam tube 10.

[0054] During signal acquisition, detector 20 receives particle signals such as secondary electrons, backscattered electrons, or characteristic X-rays generated after the sample is bombarded by a particle beam, and converts them into initial electrical signals. Signal processing module 24 acquires the initial electrical signals and sequentially performs amplification, filtering, and analog-to-digital conversion to generate digitized image signal data.

[0055] In some embodiments, the detector adjustment system 100 may further include a control component connected to the signal processing module 24, for acquiring the image signal data and calculating and determining adjustment information based on the image signal data according to the adjustment method provided in this application (the specific calculation process is detailed in the following embodiments of the adjustment method, and will not be repeated here).

[0056] The adjustment mechanism 30 is mounted on the outer wall of the particle beam tube 10 and is located radially outside the central through hole. Optionally, the adjustment mechanism 30 can be located directly below the central through hole. The outer wall of the particle beam tube 10 has multiple (e.g., four) threaded mounting holes distributed circumferentially. The base of the adjustment mechanism 30 is screwed into these threaded mounting holes using fastening bolts to fix it to the particle beam tube 10.

[0057] The adjustment mechanism 30 includes multiple adjustment components (such as set screws). By adjusting the adjustment components in different positions, a radial thrust can be applied to the detector 20, thereby compressing the sealing ring sandwiched between the outer peripheral wall of the detector 20 and the inner wall of the central through hole. The elastic compression and recovery of the sealing ring allows for adjustment of the detector 20's installation position. Thus, the entire adjustment process does not require disassembling the detector 20 or disrupting the vacuum seal inside the particle beam tube 10. Whether the equipment is in normal operation or standby mode, the spatial position of the detector can be adjusted in real time via external operation of the adjustment mechanism 30, achieving online adjustment of the detector 20.

[0058] like Figure 3 As shown, the adjustment directions include a first direction and a second direction, wherein both the first and second directions are parallel to the sample bearing surface (e.g., a horizontal plane), and the first and second directions are not collinear. In one example, the first direction is perpendicular to the second direction.

[0059] like Figure 4 As shown, the adjustment mechanism 30 specifically includes a mounting component fixedly installed on the outer wall of the particle beam tube 10, and a first adjustment component 31 and a second adjustment component 32 disposed on the mounting component. The first adjustment component 31 is used to adjust the placement position of the detector 20 in the first direction, and the second adjustment component 32 is used to adjust the placement position of the detector 20 in the second direction.

[0060] For example, the first adjusting member 31 includes at least two first adjusting screws (also referred to as differential adjusting screws), which are disposed opposite to each other on both sides of the detector 20 along a first direction, and are used to push against the outer peripheral wall of the detector 20 from opposite directions. During adjustment, one side of the first adjusting screw is screwed in to advance, and the other side of the first adjusting screw is screwed out to retract. The two work alternately to avoid the reverse backlash phenomenon caused by thread fit clearance and mechanical wear when adjusting with only one adjusting screw, thereby improving the adjustment accuracy in the first direction.

[0061] For example, the second adjusting member 32 includes at least four second adjusting screws (also referred to as differential head adjusting screws). These four second adjusting screws are arranged along the second direction and are located on opposite sides of the detector 20 along the second direction (e.g., two on each side). All the second adjusting screws are used to push against the detector 20 along the second direction. In this way, by setting four second adjusting screws that are all used to push against the detector along the second direction, the detector's movement in the second direction is made smoother and the force is more balanced by using multi-point coordinated support and pushing. This helps to keep the detector's attitude stable when moving in the second direction, reduces measurement errors caused by attitude tilt, and improves the stability and accuracy of the detector during adjustment.

[0062] Optionally, the exposed ends of each of the aforementioned set screws (e.g., the first set screw or the second set screw) are provided with scale markings. The scale values ​​of the markings directly correspond to the actual displacement of the detector 20 in the corresponding direction. Each scale mark corresponds to a displacement of 0.01 mm, 0.001 mm, or other possible displacements. The operator only needs to turn the corresponding set screw to the target scale position according to the required displacement to achieve quantitative online fine-tuning of the detector 20 in the first and second directions.

[0063] In some embodiments, such as Figure 4 As shown, the mounting components include a first mounting component 331, a second mounting component 332, and a mounting bracket 333, which is used to fix the component to the outer wall of the particle beam mirror tube 10. The first mounting component 331 and the second mounting component 332 are arranged opposite each other along a second direction. Both the first mounting component 331 and the second mounting component 332 have a first surface for mounting the first adjusting component 31 (e.g., the first adjusting component 31 is connected to the first surface by a nut), a second surface for mounting the second adjusting component 32 (e.g., the second adjusting component 32 is connected to the second surface by a nut), and a third surface for connecting to the fixing component between the adjusting mechanism 30 and the particle beam mirror tube 10. The first surfaces of the first mounting component 331 and the second mounting component 332 are arranged opposite each other on both sides of the detection mechanism. Exemplarily, the fixing component is bolted to the particle beam mirror tube 10, and the bottoms of the first mounting component 331 and the second mounting component 332 are bolted to the fixing component. For example, the fixing component is the mounting bracket 333.

[0064] It should be noted that the accompanying drawings provided above are for illustrative purposes only. The number of components and the names of the components are not limited. In addition to the components shown in the drawings, other components may also be included, such as control components for calculating adjustment information.

[0065] This application also provides an electronic device that serves as the execution subject of the aforementioned automatic detector position adjustment method. This electronic device can be a general-purpose computer, server, workstation, embedded industrial control computer, edge computing node, intelligent microscope control system, high-performance image acquisition and processing terminal, etc., and is not limited thereto. For example, this electronic device can be a control component of the aforementioned detector adjustment system 100 or a separate electronic device connected to the detector adjustment system 100.

[0066] The following is combined with Figure 5 This paper introduces one type of hardware structure for electronic devices.

[0067] like Figure 5 As shown, the first electronic device 500 includes a first processor 510, a communication line 520, and a communication interface 530.

[0068] Optionally, the first electronic device 500 may further include a memory 540. The first processor 510, the memory 540, and the communication interface 530 can be connected via a communication line 520.

[0069] The first processor 510 may be a central processing unit (CPU), a general-purpose processor, a network processor (NP), a digital signal processor (DSP), a microprocessor, a microcontroller, a programmable logic device (PLD), or any combination thereof. The first processor 510 may also be any other device with processing capabilities, such as a circuit, device, or software module, without limitation.

[0070] In one example, the first processor 510 may include one or more CPUs, for example... Figure 5 CPU0 and CPU1 in the CPU.

[0071] As an optional implementation, the first electronic device 500 includes multiple processors; for example, in addition to the first processor 510, it may also include a second processor 570. A communication line 520 is used to transmit information between the components included in the first electronic device 500.

[0072] Communication interface 530 is used for communication with other devices or other communication networks. This other communication network can be Ethernet, a radio access network (RAN), a wireless local area network (WLAN), etc. Communication interface 530 can be a module, circuit, transceiver, or any device capable of enabling communication.

[0073] Memory 540 is used to store instructions. These instructions can be computer programs.

[0074] The memory 540 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and / or instructions; it may also be a random access memory (RAM) or other type of dynamic storage device capable of storing information and / or instructions; it may also be an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media, or other magnetic storage devices, etc., without limitation.

[0075] It should be noted that the memory 540 can exist independently of the first processor 510 or can be integrated with the first processor 510. The memory 540 can be used to store instructions, program code, or some data, etc. The memory 540 can be located inside or outside the first electronic device 500, without restriction.

[0076] The first processor 510 is configured to execute instructions stored in the memory 540 to implement the communication method provided in the following embodiments of this application. For example, when the first electronic device 500 is a terminal or a chip or system-on-a-chip in the terminal, the first processor 510 can execute instructions stored in the memory 540 to implement the adjustment method provided in this application.

[0077] As an optional implementation, the first electronic device 500 also includes an output device 550 and an input device 560. The output device 550 can be a display screen, speaker, or other device capable of outputting data from the first electronic device 500 to a user, such as outputting adjustment information. The input device 560 can be a keyboard, mouse, microphone, joystick, or other device capable of inputting data into the first electronic device 500.

[0078] It should be pointed out that, Figure 5 The structure shown does not constitute a limitation on the electronic device, except... Figure 5 In addition to the components shown, the electronic device may include more or fewer components than illustrated, or combine certain components, or have different component arrangements.

[0079] The adjustment method of this application will be described in detail below with reference to the accompanying drawings.

[0080] In some embodiments, this application also provides an adjustment method, such as Figure 6As shown, the method includes: S101, Acquire the signal detected by the detector.

[0081] Here, the signal is the signal generated when at least one particle beam in the particle beam tube acts on the sample under test. For example, the signal here is the signal sensed by the detector during the operation of the particle beam tube, generated after at least one particle beam acts on the sample under test, and the signal carries characteristic information of the surface or internal structure of the sample under test.

[0082] For example, in a particle beam microscope tube, at least one particle beam is focused and incident on the surface of the sample to be tested. The particle beam interacts with the sample to be tested, exciting a signal with detectable physical properties. During the process of the particle beam scanning the sample to be tested, the detector can continuously acquire the signal at a preset sampling frequency, and convert the acquired signal into a processable electrical signal or a processable data format, that is, obtain the aforementioned "signal".

[0083] In some embodiments, the surface of the sample to be tested has a periodic pattern array consisting of at least one first material region of a first preset geometry and at least one second material region of a second preset geometry, wherein the material of any first material region is different from the material of any second material region.

[0084] Here, the first preset geometry refers to the planar contour of the first material region on the sample surface, the second preset geometry refers to the planar contour of the second material region on the sample surface, and the periodic pattern array refers to the pattern combination formed by alternating arrangements of the above two material regions along one or more directions on the sample surface according to a fixed spatial period. Since the material of any first material region is different from that of any second material region, this material difference causes differences in the signal intensity generated by the different material regions under particle beam bombardment, thereby enabling the detection image to present different signal intensity distributions (different signal intensities have different brightness).

[0085] For example, the periodic structure can be any of a square, equilateral triangle, rectangle, or other possible geometric shapes, which can be extended two-dimensionally in a plane according to the same spatial period to form a continuous and complete close-packed structure, thus obtaining the periodic structure. That is, the first preset geometric shape or the second preset geometric shape can be a square, equilateral triangle, rectangle, or other geometric shapes that can form the periodic structure.

[0086] Optionally, the first preset geometry and the second preset geometry can be the same geometry.

[0087] For example, both the first and second preset geometric shapes are squares with equal side lengths. Thus, the periodic pattern array on the surface of the sample to be tested is formed by multiple square units with equal side lengths arranged without gaps in two mutually orthogonal directions within a plane, creating this periodic structure. In the periodic structure, each square unit is alternately set as a first material region and a second material region in a checkerboard pattern; that is, any square unit in the first material region whose adjacent units in both the horizontal and vertical directions are second material regions.

[0088] For example, the first preset geometric shape is a square, and the second preset geometric shape is a rectangle, where the side length of the square is equal to the width of the rectangle. Thus, the periodic pattern array on the surface of the sample to be tested is formed by alternating square and rectangular units arranged without gaps in two mutually orthogonal directions within a plane, creating this periodic structure. In the periodic structure, square and rectangular units are alternately set as first and second material regions in a checkerboard pattern; that is, any square unit in the first material region is adjacent to a rectangular unit in both the horizontal and vertical directions as a second material region.

[0089] For example, the first material region can be chromium (Cr) and the second material region can be silicon dioxide (SiO2). This creates a periodic pattern array on the sample surface consisting of alternating Cr and SiO2 regions. Due to the significant difference in atomic numbers between Cr and SiO2, the backscattered electron signal intensities generated by the two material regions differ when the particle beam scans the sample surface. Cr has a higher average atomic number, resulting in a larger backscattering cross-section for incident particles and thus a higher backscattered electron signal intensity. SiO2 has a lower average atomic number, resulting in a smaller backscattering cross-section and a lower backscattered electron signal intensity. Therefore, the backscattered electron signal image (detection image) obtained by the detector appears as a periodic, closely packed structure of alternating bright and dark squares, equilateral triangles, or rectangles.

[0090] S102. Based on the detected image generated by the signal, determine the adjustment information of the detector.

[0091] The detection image is used to reflect the distribution of signal intensity, and the signal intensity corresponding to any first material region is different from the signal intensity corresponding to any second material region.

[0092] In some embodiments, a detection image can also be generated based on the detected signal. For example, the detection image can be a SEM image, formed by converting the signal intensity output by the detector at each scanning position into corresponding pixel brightness values.

[0093] For example, during the process of the particle beam scanning the surface of the sample to be tested point by point along a preset path, the detector collects the signal at each scanning position in real time. At this time, the signal intensity obtained at each scanning position can be quantized into grayscale values, and the grayscale values ​​can be arranged into a two-dimensional matrix according to the spatial coordinates of the scanning position. This two-dimensional matrix constitutes the detection image. In the detection image, the position with higher signal intensity corresponds to higher brightness (i.e., brighter), and the position with lower signal intensity corresponds to lower brightness (i.e., darker).

[0094] In one example, when the detector is ideally positioned within the particle beam tube, the generated image exhibits a clear, sharp pattern of periodically close-packed structures. For example... Figure 7 As shown, the first image region corresponding to the first material region (e.g., the Cr material region) and the second image region corresponding to the second material region (e.g., the SiO2 material region) are arranged alternately, with clear boundaries between the two image regions. In this case, the spatial period, region size, and arrangement direction of the periodic densely packed structure pattern with alternating bright and dark areas presented on the detection image are the same as the spatial period, region size, and arrangement direction of the periodic pattern array on the sample surface composed of the first and second material regions. In some embodiments, a pixel band also exists between the first and second image regions. When the detector is located in an ideal placement position, the brightness distribution of this pixel band at the boundary exhibits a uniform and symmetrical characteristic.

[0095] In another example, when the detector deviates from its ideal placement within the particle beam tube and requires adjustment, such as... Figure 8 As shown, in the generated detection image, there exists a pixel band between the first image region corresponding to the first material region and the second image region corresponding to the second material region. This pixel band refers to the region formed by one or more columns of transition pixels with gray values ​​between the bright and dark areas, appearing along the boundary between the first and second material regions. It should be understood that... Figure 8 This is for illustrative purposes only, and the pixel bands may be darker or brighter in actual implementations, and the edges of each image region may be irregular.

[0096] The adjustment information is used to instruct the adjustment mechanism of the detector to adjust the placement of the detector within the particle beam tube, including, for example, the adjustment direction and / or adjustment amount.

[0097] The adjustment direction refers to the direction in which the detector is to be adjusted within the particle beam tube, such as the aforementioned first direction and / or second direction. The adjustment amount refers to the displacement amplitude of the detector in the corresponding adjustment direction.

[0098] In some embodiments, the adjustment assembly includes a first adjustment member for adjusting the placement position of the detector in a first direction and a second adjustment member for adjusting the placement position of the detector in a second direction, wherein adjustment information is used to instruct the adjustment of the first adjustment member and / or the second adjustment member to adjust the placement position of the detector by means of the first adjustment member and / or the second adjustment member.

[0099] For example, the adjustment information includes the adjustment direction and / or adjustment amount in the first direction. This adjustment information instructs the first adjustment member to be driven to adjust the detector's adjustment direction and / or adjustment amount in the first direction. For instance, if the adjustment information indicates an adjustment amount of +0.5mm in the first direction, the operator needs to drive the first adjustment member to move the detector 0.5mm forward in the first direction. The adjustment information in the second direction is similar to that in the first direction and will not be described further.

[0100] It should be noted that, since the surface of the sample under test has a periodic pattern array composed of alternating first and second material regions, and the signal intensities of the two material regions are different, the image regions corresponding to the two material regions in the detection image exhibit different brightness distributions. This brightness distribution and its contrast characteristics directly reflect the spatial positional relationship between the current placement of the detector and the optical axis of the particle beam and the surface of the sample under test. Therefore, the detection images acquired by the detector can be used to determine the adjustment information.

[0101] In some embodiments, adjustment information can be determined based on the edge symmetry information of the image region corresponding to the first material region and / or the image region corresponding to the second material region in the probe image.

[0102] Among them, edge symmetry information refers to the degree of symmetry of the edge contour of an image region relative to its geometric center in the probe image.

[0103] This edge symmetry information reflects the spatial uniformity of the signal received by the detector. When the detector is in an ideal placement position, the signal generated by the particle beam acting on the sample surface is uniformly and symmetrically distributed on the detector receiving surface. Therefore, the edge lines between bright and dark areas in the detected image are clear, and the edge contours of each repeating unit are mirror-symmetrical with respect to its geometric center. When the detector deviates from the ideal placement position, the brightness distribution of the pixel bands in the image regions corresponding to the two material regions mentioned above will change. Due to the angular selectivity of signal reception, the brightness of the side facing the detector and the side facing away from the detector in the image region will differ, thereby reducing the symmetry of the edge contours. Therefore, adjustment information can be determined based on the degree of reduction in edge symmetry or the asymmetry of the brightness distribution.

[0104] The specific process for determining the adjustment information based on edge symmetry information can be referred to as follows: Figure 9The embodiments shown are not described in detail here.

[0105] S103, Output adjustment information.

[0106] For example, the adjustment information can be displayed to the operator via a display device, so that the operator can operate the adjustment mechanism based on the adjustment information to adjust the detector.

[0107] For example, adjustment information can be presented visually on a display device, showing the adjustment direction and / or adjustment amount through numerical values, directional arrows, or graphical prompts on the display interface. For instance, the display interface could display the text "It is recommended to adjust 2mm to the left," or overlay an indicator arrow on the probe image, with the arrow pointing in the direction of adjustment and the arrow length indicating the adjustment amount, so that the operator can intuitively understand the current adjustment recommendation.

[0108] In one example, if the determined adjustment information indicates that the current adjustment amount is zero, that is, the current deployment position of the detector is already in the ideal deployment position, then the output adjustment information is a "no adjustment required" prompt message, indicating that no adjustment action needs to be performed at present.

[0109] Based on the adjustment method provided in this application, a periodic pattern array consisting of alternating first and second material regions of the sample is defined. An image is generated using the physical characteristic that the two materials produce signal intensity differences when interacting with the particle beam. This allows the detection image to intuitively reflect the periodic structural features of the sample surface, providing clear and quantifiable feature targets for subsequent determination of adjustment information from the image. Furthermore, by directly acquiring the original signal generated by the particle beam acting on the sample and automatically generating the detection image, adjustment information is determined based on the image features. This adjustment information can indicate the placement position of the adjustment detector, improving the efficiency and accuracy of determining the detector's adjustment information.

[0110] In some embodiments, the adjustment information in this application can be determined based on the edge symmetry information of the image region in the probe image, combined with Figure 6 The illustrated embodiments, such as Figure 9 As shown, step S102 above can be specifically implemented as follows: S201. Divide the detected image into regions to obtain the region division results.

[0111] The region segmentation result refers to the spatial segmentation of the detection image according to the gray value distribution characteristics of the pixels, and the aggregation and labeling of pixels in the image corresponding to the same material region as the same image region to obtain multiple image regions.

[0112] The region segmentation result includes at least one first image region and at least one second image region. Each first image region corresponds one-to-one with at least one first material region, and the grayscale feature of each first image region reflects the signal intensity of the corresponding first material region. Similarly, each second image region corresponds one-to-one with at least one second material region, and the grayscale feature of each second image region reflects the signal intensity of the corresponding second material region. A pixel band exists between any first image region and any adjacent second image region within the at least one first image region. This pixel band indicates the boundary region between any two first image regions.

[0113] In some embodiments, the probe image may be denoised before performing step S201.

[0114] For example, the local variance and gradient magnitude of the probed image can be calculated to determine the noise level of the image region, and then noise reduction processing can be performed based on the noise level.

[0115] Local variance reflects the dispersion of pixel grayscale values ​​within a local region of an image. In uniform regions, local variance mainly originates from noise; a larger local variance indicates a higher noise level. In edge regions, local variance also includes grayscale jumps caused by edges, requiring differentiation in conjunction with gradient magnitude. Gradient magnitude reflects the drasticness of grayscale changes within a local region of the image, obtained by calculating the gradient components of pixels in the horizontal and vertical directions and determining their magnitudes.

[0116] For example, a neighborhood window can be set with each pixel as the center. The variance of the grayscale values ​​of all pixels within the window is calculated as the local variance, and the gradient magnitude of the central pixel of the window is calculated. The local variance is compared with a preset variance threshold, and the gradient magnitude is compared with a preset gradient threshold. When the local variance is greater than the variance threshold and the gradient magnitude is less than the gradient threshold, the region where the pixel is located is determined to be a noisy region, requiring strong noise reduction processing; when the local variance is less than the variance threshold and the gradient magnitude is less than the gradient threshold, the region where the pixel is located is determined to be a flat region, requiring moderate noise reduction processing; when the local variance is greater than the variance threshold and the gradient magnitude is greater than the gradient threshold, the region where the pixel is located is determined to be an edge region, requiring weak noise reduction processing to reduce edge blurring.

[0117] In some embodiments, a nonlocal mean denoising algorithm or a guided filtering algorithm can be dynamically selected to process the probe image based on a determined noise level.

[0118] In one example, when the determined noise level is higher than a preset noise threshold, a non-local means denoising algorithm is selected to denoise the current image region. The non-local means denoising algorithm suppresses noise in a region by weighted averaging of other pixel blocks with similar gray-level distributions within the search area. When the local variance of a region is lower than the variance threshold and the gradient magnitude is higher than the gradient threshold, the region is identified as a low-noise edge region. A guided filtering algorithm is then used, with the original probe image as a guide image, to filter this region, ensuring that the denoised image retains the original gray-level gradient at the edges and reduces edge blunting.

[0119] In some embodiments, grayscale features of the probe image can be acquired through sliding windows of different sizes, and these grayscale features can be combined to determine the region segmentation result. For example, as shown... Figure 10 As shown, step S201 can be specifically implemented as follows: S2011. Multiple sliding windows are used to traverse the detection image to obtain multiple sets of local grayscale features of the detection image.

[0120] In this context, a sliding window refers to a rectangular sampling frame that extracts a local image region from the probe image at a preset size. The sliding window slides across the pixel matrix of the probe image according to a set step size and path, capturing image data within the current window range at each position. For example, the sliding window can cover the probe image row by row and column by column, extracting local grayscale features of the local region at each window position. These local grayscale features are statistics or vectors calculated based on the grayscale values ​​of each pixel within the local image region covered by the sliding window, used to describe the signal intensity distribution characteristics of that region.

[0121] In some embodiments, any two sliding windows in the plurality of sliding windows have different sizes, and each sliding window in the plurality of sliding windows is used to extract local grayscale features of the probe image within that sliding window, with the plurality of sliding windows corresponding one-to-one with multiple sets of local grayscale features.

[0122] For example, two or more sliding windows of different sizes (e.g., 10×10 pixels, 50×50 pixels, and 100×100 pixels) can be used to traverse the same probe image sequentially, with each window independently performing the complete traversal process. Any two sliding windows have different sizes, meaning each sliding window captures a different area of ​​the probe image. After completing the traversal, each sliding window outputs a set of local grayscale features, which consists of the set of local grayscale feature values ​​extracted by that window at all traversed positions. The feature sets output by different windows are independent of each other.

[0123] It's important to note that by employing a sliding window, the classification of each pixel incorporates the grayscale distribution information of its neighborhood, thus distinguishing pixels with similar grayscale values ​​but potentially different corresponding material regions. Furthermore, gradient information within the window allows for more precise location of grayscale abrupt changes, improving the classification accuracy of boundary pixels. Sliding windows of different sizes can also comprehensively classify pixels at multiple scales, further enhancing the classification accuracy of boundary pixels through the fusion of multi-scale features.

[0124] S2012. Based on multiple sets of local grayscale features and preset grayscale thresholds, determine the pixel classification results of the detected image.

[0125] The pixel classification results include multiple types of pixels, including first type pixels, second type pixels and third type pixels. The first type of pixels corresponds to at least one first image region, the second type of pixels corresponds to at least one second image region, and the third type of pixels corresponds to a pixel band.

[0126] For example, based on a preset grayscale threshold, the above-mentioned local grayscale features can be classified to obtain the initial classification results corresponding to each group of local grayscale features. The initial classification results include the first type of initial pixels, the second type of initial pixels, and the third type of initial pixels.

[0127] For example, the preset grayscale threshold may include a first grayscale threshold and a second grayscale threshold, wherein the first grayscale threshold is greater than the second threshold. For any pixel in the probe image, its grayscale value is compared with the first threshold and the second threshold respectively: when the grayscale value of the pixel is greater than or equal to the first threshold, the pixel is classified as a first type of initial pixel; when the grayscale value of the pixel is less than or equal to the second threshold, the pixel is classified as a third type of initial pixel; when the grayscale value of the pixel is between the second threshold and the first threshold, the pixel is classified as a second type of initial pixel.

[0128] For example, when the first image region corresponds to the Cr material region (grayscale value approximately 95~115) and the second image region corresponds to the SiO2 material region (grayscale value approximately 75~85), the pixel band at the junction of the first material region and the second material region may have a grayscale value as low as 50~60 due to edge effects. In this case, the first threshold of the preset grayscale threshold can be set to 90 and the second threshold can be set to 65.

[0129] It should be noted that the third type of initial pixels corresponds to a pixel band located at the boundary between the first and second image regions in the detection image. Due to the edge effect, the intensity of the backscattered electron signal at the boundary between the first and second material regions may locally decrease or even become lower than the signal intensity inside the second material region due to the abrupt change in particle beam scattering conditions. Therefore, in the detection image, the gray value of the pixel band may be lower than the gray value of the second type of initial pixels, meaning the gray range corresponding to the third type of initial pixels is lower, while the gray range corresponding to the second type of initial pixels is located in the middle region. Based on this physical phenomenon, pixels below the second threshold are classified as third type of initial pixels (corresponding to the pixel band / edge transition band), pixels between the second and first thresholds are classified as second type of initial pixels (corresponding to the second image region / dark area), and pixels above the first threshold are classified as first type of initial pixels (corresponding to the first image region / bright area).

[0130] Therefore, the initial classification results can be weighted and fused by the preset weight coefficients corresponding to each group of local grayscale features to obtain the pixel classification results.

[0131] In one example, the weight coefficients of the initial classification results at each scale are determined based on the size of the sliding window. For instance, the weight coefficients are negatively correlated with the window size; the smaller the window size, the larger the weight coefficient, and vice versa. Alternatively, the weight coefficients may be positively correlated with the window size, and so on. Taking a positive correlation as an example, when there are three sliding windows with sizes of 10×10 pixels, 50×50 pixels, and 100×100 pixels, their corresponding weight coefficients can be set to 0.6, 0.3, and 0.1, respectively.

[0132] In another example, the weight coefficients of the classification results at each scale can also be determined based on the similarity between the initial region division results corresponding to the pixel classification results and the periodic pattern array on the sample surface.

[0133] For example, the weighting coefficients of the initial classification results at each scale can be obtained by merging adjacent pixels with the same category label in the probe image into connected regions to obtain the initial region segmentation results. This allows calculation of the geometric matching degree between the region segmentation results and the periodic pattern array. A higher similarity indicates that the pixel classification results at that scale more accurately reproduce the true material distribution pattern of the sample surface, and should be assigned a higher weighting coefficient in the fusion process. Conversely, a lower similarity indicates a greater deviation between the classification results at that scale and the true pattern, and should be assigned a lower weighting coefficient in the fusion process.

[0134] In another example, the weight coefficients of the initial classification results at each scale can be preset. When performing weighted fusion, the pre-stored weight coefficients of each scale can be directly called and weighted fusion calculations can be performed with the corresponding scale classification results.

[0135] For example, weighted fusion can employ a voting-based weighting method. For each pixel in the probe image, its category (Class 1, Class 2, or Class 3) is recorded in the classification results at each scale. Each scale assigns a vote to the category determination of that pixel, and the vote values ​​at different scales are weighted according to the weight coefficient corresponding to that scale. The total number of weighted votes for that pixel in each category is counted, and the category with the highest total number of weighted votes is taken as the fused category for that pixel. For example, when using three sliding windows of different sizes (small-scale window with a weight of 0.5, medium-scale window with a weight of 0.3, and large-scale window with a weight of 0.2) to classify a pixel, if the small-scale window determines that the pixel is Class 1, the medium-scale window determines that it is Class 3, and the large-scale window determines that it is Class 1, then the weighted vote for that pixel in Class 1 is 0.5 + 0.2 = 0.7, and the weighted vote for Class 3 is 0.3. After fusion, the pixel is classified as Class 1.

[0136] In some embodiments, the initial classification results corresponding to the global grayscale features and the above-mentioned multiple sets of local grayscale features can be weighted and fused to obtain the pixel classification result.

[0137] For example, the global grayscale features of the probe image can also be obtained, and the global grayscale features can be compared with the preset grayscale thresholds corresponding to the first type of pixels, the second type of pixels, and the third type of pixels, respectively. Furthermore, each set of local grayscale features can be compared with the preset grayscale thresholds corresponding to the first type of pixels, the second type of pixels, and the third type of pixels, respectively, to obtain the initial classification result corresponding to the global grayscale features and each set of local grayscale features. Then, the initial classification result is weighted and fused using the preset weight coefficients corresponding to the global grayscale features and each set of local grayscale features to obtain the pixel classification result.

[0138] The preset grayscale threshold and weighted fusion process can be referred to the above description, and will not be repeated here.

[0139] This embodiment introduces global grayscale features and combines them with local grayscale features of each group. By comparing each group of local grayscale features with a preset grayscale threshold, a preliminary classification result is determined, which realizes rapid preliminary segmentation of the first, second and third class pixel regions in the image. The initial classification result is weighted and fused by combining the weights corresponding to the global grayscale features and each group of local grayscale features, and the final pixel classification result is determined comprehensively, thereby improving the accuracy of pixel classification.

[0140] S2013. According to the image regions corresponding to each type of pixel in the pixel classification results, the detection image is divided into regions to obtain the region division results.

[0141] For example, based on the category labels of each type of pixel in the pixel classification results and their spatial distribution in the probe image, the probe image can be segmented into multiple non-overlapping image regions. Each image region corresponds to a set of the aforementioned type of pixels (e.g., the first type of pixels, the second type of pixels, and the third type of pixels). Specifically, the first type of pixels corresponds to the first image region, the second type of pixels corresponds to the second image region, and the third type of pixels corresponds to a pixel band.

[0142] In some embodiments, the region partitioning result can also be processed by opening and closing operations.

[0143] Morphological opening refers to a combination of erosion and dilation operations performed sequentially on a binary or labeled image to reduce small noise regions and fine connectivity structures. Morphological closing refers to a combination of dilation and erosion operations performed sequentially on an image to fill small holes and narrow breaks within the target region of the image.

[0144] In some embodiments, a mask is generated for the region partitioning results.

[0145] In this context, a mask refers to a binary or multi-valued label matrix with the same spatial dimensions as the probe image. The value of each element in this matrix identifies the region category or region number to which the pixel belongs. The mask of the region segmentation result is a data form that encodes the region segmentation result, assigning the same mask value to pixels belonging to the same image region in the probe image, and assigning different mask values ​​to pixels belonging to different image regions.

[0146] Thus, the region division result is obtained, and a mask of the region division result can also be generated to facilitate subsequent calculations and processing.

[0147] S202. Determine the edge symmetry information of at least one target image region in the region segmentation result.

[0148] Wherein, at least one target image region is one or more of at least one first image region or one or more of at least one second image region.

[0149] Among them, edge symmetry information is used to reflect the degree of symmetry of the edges of each target image region in at least one target image region.

[0150] In some embodiments, edge symmetry information includes the ratio between the mean gray values ​​of two portions of the outer annular region, which are divided along a first direction and / or a second direction with the centroid of the first candidate image region as a reference.

[0151] The outer ring region refers to the annular band formed between the original boundary and the expanded boundary after extending the boundary of the first candidate image region outward by a predetermined distance. This outer ring region surrounds the outside of the target image region, and its width is equal to the predetermined distance.

[0152] For example, the predetermined distance can be determined based on the pixel band width between image regions in the detected image. For example, when the size of the first image region or the second image region is 50 pixels × 50 pixels, the pixel band width between the first image region and the second image region is 5 pixels. In this case, the predetermined distance can be set to 5 pixels to 10 pixels, so that the outer ring-shaped region falls within the range of the pixel band region, and also reduces the extension of the ring-shaped region to the boundary of the adjacent image region.

[0153] For example, this outward expansion can employ a morphological dilation operation. For instance, a binary mask of the target image region can be used as input, and a circular or square structuring element can be used to dilate the binary mask, with the radius or side length of the structuring element equal to a predetermined distance. The dilated mask covers the original target image region and the total region after expanding outward by the predetermined distance. Subtracting the original target image region's mask from the dilated mask yields the pixel set of the difference region, which represents the outer ring-shaped region of the target image region.

[0154] Therefore, based on the centroid of the target image region, the outer ring region can be divided into two parts along the first and / or second directions, and the ratio between the grayscale mean values ​​can be calculated.

[0155] Here, the centroid refers to the geometric center of the target image region within the probed image, and its coordinates are calculated by the arithmetic mean of the coordinates of all pixels within the region. The portion of the outer annular region above the centroid of the target image region is designated as the first part, and the portion below the centroid as the second part. The ratio of the average grayscale values ​​of the first and second parts can be calculated to obtain edge symmetry information. Alternatively, the portion to the left of the centroid can be designated as the first part, and the portion to the right of the centroid as the second part. The ratio of the average grayscale values ​​of the first and second parts is then calculated to obtain edge symmetry information.

[0156] For example, along the first direction, draw a straight line parallel to the vertical direction through the centroid. This line divides the outer annular region into a left part and a right part. Calculate the average gray value of all pixels in the left part and the average gray value of all pixels in the right part, and calculate the ratio of their average gray values. When the ratio is close to 1 (e.g., within the range of [0.95, 1.05]), it indicates that the gray value distribution of the outer annular region is symmetrical in the first direction, and the detector's placement in the first direction does not need adjustment. When the ratio deviates from 1 (e.g., 0.8, 1.2, etc.), it indicates that the gray value distribution on both sides of the outer annular region is asymmetrical, and the detector needs adjustment in the first direction.

[0157] Similarly, along the second direction, a straight line parallel to the horizontal direction is drawn through the centroid to divide the outer annular region into an upper part and a lower part. The ratio of the average gray values ​​on both sides is calculated to evaluate the detector's offset in the second direction.

[0158] S203. Determine the adjustment information based on the edge symmetry information.

[0159] For example, when there are multiple target image regions, the mean value of the edge symmetry information of the multiple target image regions can be determined, and adjustment information can be determined based on the mean value.

[0160] In some embodiments, edge symmetry information (the mean of edge symmetry information in the case of multiple target image regions) can be analyzed to determine the positional deviation of the detector in a first direction and / or a second direction, thereby determining adjustment information. The adjustment information is used to indicate that the positional deviation is adjusted such that the ratio between the grayscale means of the two portions is greater than or equal to 0.95 and less than or equal to 1.05. It should be noted that 0.95 and 1.05 are merely exemplary values, and in actual applications, they can be adjusted accordingly based on the required adjustment precision of the system.

[0161] For example, edge symmetry information can be analyzed, and the ratio of the average gray values ​​on both sides of the calculated outer ring region can be compared with the reference value [0.95, 1.05]. Based on the degree and direction of the ratio deviating from the reference value, the spatial position offset and offset direction of the detector in the corresponding direction can be determined, thereby determining the adjustment direction and / or adjustment amount used to indicate the position adjustment of the detector.

[0162] For example, the grayscale ratios in the first and second directions can be analyzed separately to output adjustment information including the adjustment direction and / or adjustment amount.

[0163] For example, taking the first direction as an example, if the ratio of the average gray level of the left part to the average gray level of the right part of the outer ring area is 0.85, which is less than 0.95, it indicates that the average gray level of the left side is significantly lower than that of the right side. In this case, the detector needs to be adjusted to the left. Alternatively, if the ratio is 1.12, which is greater than 1.05, it indicates that the average gray level of the left side is significantly higher than that of the right side, and the detector is biased to the left, so the detector needs to be adjusted to the right. The determined adjustment amount is determined by the degree of deviation of the ratio from 1 combined with the preset mapping relationship: the greater the deviation (i.e., the farther the ratio is from [0.95, 1.05]), the greater the adjustment amount.

[0164] The second direction is similar to the first direction, so it will not be described again here.

[0165] The preset mapping relationship refers to the corresponding rule or functional relationship established between the ratio of the average gray values ​​on both sides of the outer annular region and the adjustment amount of the detector, which can be set based on empirical data. In some embodiments, the mapping relationship in the first direction and the mapping relationship in the second direction can be the same or different.

[0166] In some embodiments, the aforementioned first preset geometry may also have a specific asymmetry in a single dimension (e.g., a first direction dimension or a second direction dimension) (e.g., an equilateral triangle arrangement that is symmetrical in the first direction and asymmetrical in the second direction). In this case, a multi-directional layout strategy can be adopted, such as arranging multiple material compositions of the first preset geometry in an array with different rotation angles or orientations. For example, when the first preset geometry is an equilateral triangle, it may include: The first group of figures: its axis of symmetry is parallel to the first direction; The second set of graphics: Compared to the first set of images, its axis of symmetry is rotated by a predetermined angle (such as 90 degrees), so that the second set of graphics exhibits different edge features or asymmetry from the first set of graphics in the second direction.

[0167] With this orthogonal or intersecting multi-directional layout, the edge features in the target image region acquired by the detector are mainly determined by the distribution of the first set of patterns in the first direction, while the edge features in the second direction are mainly determined by the distribution of the second set of patterns.

[0168] The grayscale ratio in the first direction is calculated based on the edge grayscale distribution of the first set of images in the first direction, and the grayscale ratio in the second direction is calculated based on the edge grayscale distribution of the second set of images in the second direction. By obtaining the grayscale ratios in these two directions respectively, the positional offset of the detector in the first and second directions can be determined, thereby enabling the adjustment of the detector's deployment position.

[0169] The first preset geometric shape is an asymmetrical shape in the second direction, such as an equilateral triangle (symmetrical in the first direction). In this case, the edge symmetry information of the target image region corresponding to the first preset geometric shape can only include its grayscale ratio in the first direction.

[0170] Based on this embodiment, the image region of the detection image is divided by the signal strength, and the inherent symmetry features of the periodic pattern array on the sample surface are used to quantify the position deviation of the detector by the edge symmetry information of the target region, thereby determining the adjustment information.

[0171] In some embodiments, this application also provides a method for determining at least one target image region, such as... Figure 11 As shown, it includes the following steps: S301. Determine at least one first candidate image region from the region segmentation results.

[0172] Wherein, at least one first candidate image region is at least one first image region and / or at least one second image region.

[0173] In some embodiments, the image regions in the mask of the region segmentation result can be labeled with connected components (also known as connected domains) to obtain multiple independent connected regions (first candidate image regions).

[0174] Here, connected component labeling refers to assigning a region number to each pixel in the classification mask, so that all pixels within the same connected component have the same number, while pixels in different connected components have different numbers.

[0175] Therefore, multiple independent connected regions (first candidate image regions) can also be filtered to remove noise regions that are too small.

[0176] For example, for each first candidate image region, the number of pixels it contains is calculated as the area value of that region. The area value of each first candidate image region is compared with a preset area threshold. If the area value of a first candidate image region is less than the preset area threshold, the first candidate image region is determined to be a noise region and is removed from the list of first candidate image regions. If the area value of a first candidate image region is greater than or equal to the preset area threshold, the first candidate image region is retained as a valid region.

[0177] S302. Extend the boundary of each first candidate image region outward by a predetermined distance to obtain the outer ring region of each first candidate image region.

[0178] The outward expansion operation here can be referred to the description of the outward expansion operation for the target image region in step S2013 above, and will not be repeated here.

[0179] S303. Determine one or more first candidate image regions that satisfy the preset surrounding distribution conditions in the outer ring region as at least one second candidate image region.

[0180] Among them, the preset surrounding distribution conditions include that the pixel ratio of the pixel band (the pixel band between the first image region and the second image region) in the outer ring region of the candidate image region is greater than a preset threshold.

[0181] For example, for each first candidate image region, the corresponding outer ring region is obtained, the pixel ratio of the pixel band in the outer ring region is counted, the pixel ratio is compared with a preset threshold, and one or more first candidate image regions whose outer ring region meets the preset surrounding distribution conditions are selected, and the selected first candidate image regions are determined as at least one second candidate image region.

[0182] In this way, by expanding the boundary of the first candidate region to form an outer ring region, the region in which the pixel ratio of the third image region meets the preset threshold is selected as the target. This eliminates invalid interference regions in the image caused by occlusion, contamination or sample edge effects, which helps to ensure that subsequent symmetry calculations are based only on the target image region with complete structure and clear features, thereby improving the reliability of the adjustment information generation.

[0183] S304. Determine at least one target image region in at least one second candidate image region.

[0184] In one possible implementation, a cue message can be generated based on at least one second candidate image region. The cue message and the probe image are input into a segmentation model to obtain at least one candidate segmentation mask with a preset geometry output by the segmentation model and the confidence level of each candidate segmentation mask. One or more candidate segmentation masks with a confidence level greater than a preset confidence threshold are determined as at least one target segmentation mask.

[0185] The prompt information is used to guide the pre-trained segmentation model to identify target image regions. For example, the prompt information may include a set of prompts for bounding boxes and a set of prompts for centroid locations.

[0186] In some embodiments, the prompt message may also be generated based on a second candidate image region selected by the operator.

[0187] For example, in response to an operator's selection operation in at least one second candidate image region, one or more second candidate image regions selected by the operator are determined; a prompt message is generated to indicate one or more second candidate image regions.

[0188] For example, a detection image can be displayed to the operator, on which at least one second candidate image region identified above is marked. In response to a point, box, or touch selection operation performed by the operator in the at least one second candidate image region, one or more second candidate image regions specified by the operator are determined from the at least one second candidate image region based on the landing point position or selection range of the operation instruction. Thus, prompt information can be generated based on the geometric position parameters corresponding to each of these selected second candidate image regions.

[0189] This approach combines the results of automatic filtering with the subjective choices of operators, leveraging the efficiency of the algorithm while retaining the experience and judgment of human experts, thereby improving the accuracy and flexibility of target image region selection.

[0190] In some embodiments, the shape fit of each second candidate image region can also be determined, and first sub-information can be generated based on the shape fit, with the prompt information including the first sub-information.

[0191] For example, the ratio of the area to the perimeter of each second candidate image region in at least one second candidate image region can be calculated, the ratio can be normalized with the proportion of pixels belonging to the pixel band in the outer annular region, and the weighted fusion can be performed based on the preset weight coefficient comparison value and the pixel proportion to obtain the shape fitting degree of each second candidate image region.

[0192] The ratio of area to perimeter reflects the compactness of the second candidate image region; a larger ratio indicates a more circular shape, while a smaller ratio indicates a more elongated or irregular shape. The proportion of pixels belonging to the pixel band within the outer annular region characterizes the uniformity of pixel distribution near the region's edges. This ratio and pixel proportion can be normalized, and then weighted and fused based on preset weighting coefficients. The result of this weighted fusion is the shape fit degree of the second candidate image region. Therefore, the shape fit degree can be used to reflect the regularity of the geometric contour of the second candidate image region.

[0193] Thus, a first sub-information is generated, which is used to indicate one or more second candidate image regions whose shape fitting degree is greater than a first preset shape fitting degree threshold, and the prompt information includes the first sub-information.

[0194] The first preset shape fitting threshold is a pre-set numerical constant used to determine whether the shape of the region meets the preset regularity requirements. The first sub-information is included as a component of the prompt information and is input into the segmentation model along with other sub-information. This allows the segmentation model to not only focus on the spatial location indicated by the prompt information during segmentation but also to obtain prior indications about the regularity of the region's shape at that location.

[0195] In some embodiments, candidate regions with shape fit less than a second preset confidence threshold may also be eliminated.

[0196] For example, the spatial location of each second candidate image region whose shape fit is greater than a first preset shape fit threshold is obtained. The second candidate image regions whose spatial locations are less than a preset distance threshold are merged to obtain at least one merged candidate region.

[0197] Clustering adjacent candidate regions by using a spatial distance threshold can integrate scattered, nearby regions with similar features into a unified image region to be detected, thus facilitating subsequent detection symmetry and optimizing the topological integrity of the target region.

[0198] Therefore, in at least one candidate region, candidate regions with shape fit less than a second preset confidence threshold are eliminated to obtain at least one third candidate image region. Second sub-information is generated to indicate the at least one third candidate image region, and the prompt information also includes the second sub-information.

[0199] It can filter out interference points with insufficient confidence, which helps to improve the accuracy of the adjustment information determination.

[0200] Furthermore, after determining the prompt information based on the above embodiments, the prompt information and the probe image are input into the segmentation model to obtain at least one candidate segmentation mask of the preset geometry output by the segmentation model and the confidence level of each candidate segmentation mask.

[0201] The segmentation model can be a pre-trained segment anything model (SAM). This model can be built on a deep learning architecture, trained on a large-scale dataset, and can receive various forms of input prompts such as bounding boxes and points, outputting candidate segmentation masks corresponding to the prompts and their confidence scores. The confidence score quantifies the model's certainty that the target image region actually exists within the area covered by the candidate segmentation mask, and its value ranges from [0, 1].

[0202] Therefore, one or more candidate segmentation masks with a confidence level greater than a preset confidence threshold can be identified as at least one target segmentation mask. Each target segmentation mask corresponds one-to-one with at least one target image region, and each target segmentation mask is used to indicate a target image region. The confidence threshold is a preset numerical constant used to determine whether a candidate segmentation mask is valid, for example, set to 0.8 or other possible values.

[0203] Based on this embodiment, when determining the target region, prompt information is first generated based on geometric distribution conditions, and then input into a preset segmentation model along with the image. The semantic understanding capability of the deep learning model is used to further refine the segmentation mask. Thus, the segmentation model can effectively handle deformation, distortion, or weak features in the image, generate an accurate preset geometric shape segmentation mask, and improve the accuracy of the finally determined target image region.

[0204] In some embodiments, after the deployment location is adjusted, this application can also continuously monitor the detection image to monitor the adjustment effect. For example... Figure 12 The adjustment method provided in this application further includes the following steps: S401. After adjusting the deployment position, reacquire the reference detection image.

[0205] For example, after outputting the adjustment information, the operator can adjust the detector's deployment position by controlling the adjustment mechanism based on the adjustment information. After the deployment position adjustment is completed, step S101 can be executed again to acquire the signal detected by the detector and generate a corresponding reference detection image based on the signal. This process will not be described in detail here.

[0206] In some embodiments, after adjusting the deployment position, multiple consecutive detection images are acquired in the time dimension, the variance of the edge symmetry information of the multiple consecutive detection images is calculated, and if the variance is less than a preset stability threshold, a reference detection image is acquired again.

[0207] For example, after adjusting the deployment position, step S101 can be repeated to acquire multiple consecutive detection images in the time dimension, and the edge symmetry information of each detection image can be obtained. Here, the edge symmetry information of a detection image can be the average of the edge symmetry information of all target image regions on that detection image.

[0208] The determination of the edge symmetry information of the target image region can be referred to the relevant description in the above embodiments, and will not be repeated here.

[0209] Therefore, the variance of the edge symmetry information of multiple consecutive detection images can be calculated. When the variance is less than a preset stability threshold, it indicates that the image acquisition device has become physically stable after completing the deployment position adjustment, and the symmetry characteristics of the acquired detection images fluctuate little in the time dimension. Conversely, if the variance is greater than or equal to the preset stability threshold, it indicates that the image acquisition device is still in an unstable state or the scene is still changing. In this case, the reference detection image is not reacquired, and the edge symmetry information of the subsequently acquired detection images is calculated.

[0210] In this way, multiple images are acquired continuously, and the adjustment is only considered complete when the variance of the edge symmetry information is less than a preset threshold. This filters out instantaneous noise, electronic jitter, or occasional fluctuations caused by mechanical micro-vibrations during particle beam scanning, thereby improving the reliability of the detection data.

[0211] S402. If the reference edge symmetry information of the reference probe image does not meet the preset conditions, determine the reference adjustment information based on the reference probe image.

[0212] Among them, the reference edge symmetry information is the edge symmetry information of the reference detection image, which is used to reflect the result after the deployment position is adjusted.

[0213] For example, the preset condition can be that the reference edge symmetry information reaches the above-mentioned benchmark value [0.95, 1.05].

[0214] S403. If the reference edge symmetry information meets the preset conditions or the number of adjustments reaches the preset number, the adjustment is confirmed to be complete.

[0215] The preset number of attempts is a pre-defined positive integer used to limit the maximum number of adjustment attempts, in order to avoid the adjustment process from continuing indefinitely due to the inability to achieve the ideal symmetry condition, and to reduce the time consumption and resource waste caused by over-adjustment.

[0216] Based on the above embodiments, after adjustment, the detection image is immediately reacquired and the symmetry of the new image is checked. If it does not meet the standard, the adjustment continues. If it meets the standard or the maximum number of times is reached, the adjustment stops. This can reduce nonlinear errors, mechanical lag or environmental disturbances in the dynamic compensation adjustment process and improve the accuracy of the final adjustment result.

[0217] The above embodiments can be combined arbitrarily without conflict, and the resulting technical solutions are also within the scope of this application. Any simple modifications or equivalent substitutions made based on the principles of this application do not depart from the protection scope of this application.

[0218] By way of example, the implementation of this application is described below with reference to a comprehensive example, which covers the technical features of the above-described embodiments. For example, the adjustment method of this application may include the following steps: S1. Acquire signal (corresponding to the above detection image).

[0219] S11. Adaptive noise reduction processing: Acquire the signal detected by the detector located in the particle beam tube. The signal is generated by the particle beam acting on the surface of the sample to be tested (a periodic pattern array composed of a first material region with a first preset geometry and a second material region with a second preset geometry). Adaptive noise reduction is performed on the initial detection image generated based on the signal: The local variance and gradient magnitude of the image are calculated to evaluate the noise level. The non-local mean denoising or guided filtering algorithm is dynamically selected according to the noise level to reduce noise while protecting the gray level relationship and structural features of the image.

[0220] S12. Multi-scale grayscale analysis: Perform multi-scale analysis on the denoised image. Use sliding windows of three different sizes (10×10, 15×15, and 20×20) to traverse the image and calculate local grayscale statistical features within each window. Based on global and local statistics, set thresholds to initially classify pixels into three categories: first category (corresponding to the first material region), second category (corresponding to the second material region), and third category (corresponding to the pixel band of the boundary region).

[0221] S13. Hierarchical Fusion and Post-processing: The classification results at different scales are weighted and fused to generate a preliminary classification map. Then, morphological opening operations are used to remove small noise regions, and closing operations are used to fill holes, generating the final region classification mask (i.e., the region partitioning result). At this point, the first type of pixel region constitutes the first image region, the second type of pixel region constitutes the second image region, and the third type of pixel region constitutes the pixel band connecting the two.

[0222] S2. Candidate region filtering and suggestion construction.

[0223] S21. Connected component analysis: Connected component labeling is performed on the first image region (or the second image region) in the region classification mask to obtain multiple candidate bright regions and filter out noise regions with too small area.

[0224] S22, Dark Band Enclosure Verification: Perform morphological dilation on each candidate region and calculate the proportion of pixel bands (third type pixels) in the surrounding annular region; verify whether this proportion meets the preset enclosure distribution condition of being greater than a preset threshold, thereby distinguishing real graphics from noise.

[0225] S23. Candidate Generation and Confidence Evaluation: For the validated candidate regions, calculate their bounding boxes and centroid locations, and evaluate the shape confidence based on the region's compactness and the integrity of the dark band enclosure.

[0226] S24. Optimize the prompts: merge duplicate candidates that are spatially close, remove low-confidence candidates, and generate the final set of point prompts and set of box prompts (i.e., the "prompt information" in the above embodiments).

[0227] S3. Fine segmentation and symmetry feature extraction based on the SAM model.

[0228] S31. Image encoding: Input the original detection image into a pre-trained segmentation model (such as the SAM model) to extract and embed image features.

[0229] S32, Cue Encoding: Input the point cue and box cue generated in step S2 into the segmentation model to generate cue feature embeddings.

[0230] S33, Mask Decoding: Combining image feature embedding and cue feature embedding, the mask decoder of the segmentation model generates multiple candidate segmentation masks and their confidence scores.

[0231] S34. Result Filtering: Select the segmentation mask with the highest confidence as the final result, and filter out unreliable segments with low confidence to obtain the target image region.

[0232] S35. Single Image Symmetry Calculation: Extract the dark bands at the edges of all complete regular graphics, calculate the ratio of the average grayscale values ​​of the symmetrical edges (such as two opposite edges) of the same graphic, and record it as the symmetry of the graphic; if there are multiple sets of symmetrical edges in the graphic, take the average value; traverse all complete regular graphics, take the average value of the symmetry values ​​of all graphics, and use it as the overall symmetry information of the current state for reference.

[0233] S4. Determine the adjustment information.

[0234] S41. Symmetry calculation: For a periodically densely packed regular graphic array, calculate the symmetry index of the upper and lower edges and the symmetry index of the left and right edges for each individual graphic obtained by segmentation.

[0235] For example, for the vertical (second direction) edge symmetry, the grayscale or positional deviation between the upper and lower halves of the graphic along the horizontal central axis is calculated; for the horizontal (first direction) edge symmetry, the deviation between the left and right halves along the vertical central axis is calculated. The indices of all graphics are statistically averaged to obtain the overall horizontal symmetry error (reflecting the left-right deviation) and vertical symmetry error (reflecting the vertical deviation).

[0236] S42. Movement Direction Determination and Execution: Based on the error calculated in S41, determine the movement direction of the detector. If the upper and lower edges are not asymmetrical, the indicator adjustment mechanism will move in the opposite direction of the longitudinal deviation. If the left and right edges are asymmetrical, the adjustment mechanism is instructed to move in the opposite direction of the lateral deviation. After the adjustment mechanism is driven to adjust, the system re-acquires the image and determines its edge symmetry information.

[0237] S43. Stability and Compliance Judgment: After adjustment, continuously monitor changes in the symmetry index. Once the index stabilizes (variance is less than the set threshold after multiple consecutive measurements), determine whether the current symmetry meets the preset compliance threshold.

[0238] If the target is met, the final adjustment information will be output and the debugging process will end. If the target is not met, repeat steps S41 to S43 until the symmetry is met or the maximum number of iterations is reached.

[0239] Furthermore, the specific implementation methods of each feature in steps S11 to S43 are the same as those in steps S43. Figures 6-12 The descriptions of the embodiments shown correspond to the relevant descriptions in the above embodiments, and will not be repeated here.

[0240] The foregoing primarily describes the solutions provided by the embodiments of this application from a methodological perspective. To achieve the aforementioned functions, it includes corresponding hardware structures and / or software modules for executing each function. Those skilled in the art should readily recognize that, in conjunction with the units and algorithm steps of the various examples described in the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed in hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0241] This application embodiment can group functional modules of electronic devices, etc., according to the above method examples. For example, each functional group can be assigned to a separate functional module, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware or as a software functional module. It should be noted that the grouping of modules in this application embodiment is illustrative and only represents one logical functional grouping. In actual implementation, there may be other grouping methods.

[0242] Figure 13 The diagram shown is a schematic representation of the composition of an electronic device according to an embodiment of this application. Figure 13 As shown, the second electronic device 1300 can be used to perform the above-described adjustment method. As one possible implementation, such as... Figure 13 As shown, the second electronic device 1300 includes an acquisition module 1301, a determination module 1302, and an output module 1303.

[0243] The acquisition module 1301 is used to acquire the signal detected by the detector. The signal is generated by at least one particle beam in the particle beam tube acting on the sample to be tested. The detector is located inside the particle beam tube. The surface of the sample to be tested has a periodic pattern array composed of at least one first material region of a first preset geometric shape and at least one second material region of a second preset geometric shape. The material of any first material region is different from the material of any second material region.

[0244] The determination module 1302 is used to determine the adjustment information of the detector based on the detection image generated by the signal; the detection image is used to reflect the distribution of the signal intensity of the signal, and the signal intensity corresponding to any first material region is different from the signal intensity corresponding to any second material region; the adjustment information is used to indicate the adjustment direction and / or adjustment amount of the detector's placement position in the particle beam tube.

[0245] Output module 1303 is used to output adjustment information.

[0246] For a more detailed description of the acquisition module 1301, the determination module 1302, and the output module 1303, as well as a more detailed description of each technical feature therein and a description of the beneficial effects, please refer to the corresponding method embodiment section above, which will not be repeated here.

[0247] It should be noted that, Figure 13 A module can also be called a unit; for example, an acquisition module can be called an acquisition unit. Additionally, Figure 13 In the embodiments shown, the names of the modules may not be the same as those shown in the figures. For example, the determination module may also be called the processing module.

[0248] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the grouping of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0249] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative. For instance, the grouping of modules or units is only a logical functional grouping, and in actual implementation, there may be other grouping methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection of devices or units may be electrical, mechanical, or other forms.

[0250] The unit described as a separate component may or may not be physically separate. The component shown as a unit can be one physical unit or multiple physical units, that is, it can be located in one place or distributed in multiple different places. Some or all of the units can be selected to achieve the purpose of the solution in this embodiment according to actual needs.

[0251] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0252] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device, such as a microcontroller, chip, or processor, to execute all or part of the steps of the method of the various embodiments of this application.

[0253] In addition, this application also provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the methods in the various method embodiments of this application to be performed.

[0254] This application also provides a computer program product, which includes computer program code or instructions, such that when the computer program code or instructions are run on a computer, the methods in the various method embodiments of this application are executed.

[0255] Furthermore, this application also provides a chip including a processor. A memory for storing a computer program is provided independently of the chip, and the processor is used to execute the computer program stored in the memory to perform the methods in the various method embodiments of this application.

[0256] Furthermore, the chip may also include a communication interface. This communication interface can be an input / output interface or an interface circuit, etc. Furthermore, the chip may also include a memory.

[0257] It should be understood that the processor in the embodiments of this application can be an integrated circuit chip with the ability to process signals. In implementation, each step of the above method embodiments can be completed by the integrated logic circuitry in the processor's hardware or by instructions in software form. The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly implemented by a hardware encoding processor, or implemented by a combination of hardware and software modules in the encoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The storage medium is located in memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0258] The memory in the embodiments of this application can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which serves as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), and synchronous link dynamic memory (SLDRAM).

[0259] It should be noted that when the processor is a general-purpose processor, DSP, ASIC, FPGA, or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, the memory can be integrated into the processor.

[0260] It should also be noted that the memory described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0261] The above embodiments have provided a detailed description of the purpose, technical solutions, and advantages of this application. It should be understood that the above are merely embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. An adjustment method, characterized in that, The method includes: The detector acquires a signal, which is a signal generated by at least one particle beam in the particle beam tube acting on the sample to be tested; wherein the detector is located inside the particle beam tube, and the surface of the sample to be tested has a periodic pattern array composed of at least one first material region of a first preset geometric shape and at least one second material region of a second preset geometric shape, wherein the material of any first material region is different from the material of any second material region. The detected image generated by the signal is divided into regions to obtain region division results. The detected image reflects the signal intensity distribution of the signal. The signal intensity corresponding to any first material region is different from the signal intensity corresponding to any second material region. The region division results include at least one first image region and at least one second image region. Each first image region corresponds one-to-one with each of the at least one first material region. The grayscale feature of each first image region reflects the signal intensity of the corresponding first material region. Similarly, each second image region corresponds one-to-one with each of the at least one second material region. The grayscale feature of each second image region reflects the signal intensity of the corresponding second material region. A pixel band exists between any first image region and a second image region adjacent to it. This pixel band indicates the boundary region between the two regions. Determine the edge symmetry information of at least one target image region in the region segmentation result; the at least one target image region is one or more of the at least one first image region or one or more of the at least one second image region, and the edge symmetry information is used to reflect the degree of symmetry of the edges of each target image region in the at least one target image region; Based on the edge symmetry information, adjustment information is determined; the adjustment information is used to indicate the adjustment direction and / or adjustment amount for adjusting the placement position of the detector within the particle beam tube; Output the adjustment information.

2. The method according to claim 1, characterized in that, The process of dividing the generated detection image from the signal into regions to obtain region division results includes: Multiple sliding windows are used to traverse the detection image to obtain multiple sets of local grayscale features of the detection image; wherein, any two sliding windows in the multiple sliding windows have different sizes, each sliding window in the multiple sliding windows is used to extract local grayscale features of the detection image within that sliding window, and the multiple sliding windows correspond one-to-one with the multiple sets of local grayscale features; Based on the multiple sets of local grayscale features and the preset grayscale threshold, the pixel classification result of the probe image is determined; the pixel classification result includes multiple types of pixels, including a first type of pixel, a second type of pixel, and a third type of pixel, wherein the first type of pixel corresponds to at least one first image region, the second type of pixel corresponds to at least one second image region, and the third type of pixel corresponds to the pixel band; The detection image is divided into regions according to the image regions corresponding to each type of pixel in the pixel classification results, and the region division results are obtained.

3. The method according to claim 2, characterized in that, The step of determining the pixel classification result of the probed image based on the multiple sets of local grayscale features and a preset grayscale threshold includes: Obtain the global grayscale features of the probe image; The global grayscale features are compared with the preset grayscale thresholds corresponding to the first type of pixels, the second type of pixels, and the third type of pixels, respectively. Each set of local grayscale features is also compared with the preset grayscale thresholds corresponding to the first type of pixels, the second type of pixels, and the third type of pixels, respectively, to obtain the initial classification results corresponding to the global grayscale features and each set of local grayscale features. The initial classification results include the first type of initial pixels, the second type of initial pixels, and the third type of initial pixels. The initial classification result is weighted and fused using the global grayscale features and the preset weight coefficients corresponding to each of the multiple sets of local grayscale features to obtain the pixel classification result.

4. The method according to any one of claims 1-3, characterized in that, The at least one target image region is determined based on the following method: In the region segmentation results, at least one first candidate image region is determined; wherein, the at least one first candidate image region is the at least one first image region and / or the at least one second image region; The boundary of each first candidate image region is extended outward by a predetermined distance to obtain the outer ring-shaped region of each first candidate image region. One or more first candidate image regions that satisfy a preset surrounding distribution condition are determined as at least one second candidate image region; wherein, the preset surrounding distribution condition includes that the pixel ratio of the pixel band in the outer ring region of the candidate image region is greater than a preset threshold; The at least one target image region is determined in the at least one second candidate image region.

5. The method according to claim 4, characterized in that, Determining the at least one target image region in the at least one second candidate image region includes: Based on the at least one second candidate image region, a prompt message is generated, which is used to guide the pre-trained segmentation model to identify the target image region; The prompt information and the detection image are input into the segmentation model to obtain at least one candidate segmentation mask of the preset geometry output by the segmentation model and the confidence level of each candidate segmentation mask; One or more candidate segmentation masks with a confidence level greater than a preset confidence threshold are determined as at least one target segmentation mask. The at least one target segmentation mask corresponds one-to-one with the at least one target image region, and each target segmentation mask is used to indicate the target image region.

6. The method according to claim 5, characterized in that, The step of generating prompt information based on the at least one second candidate image region includes: In response to an operator's selection operation in the at least one second candidate image region, determine one or more second candidate image regions selected by the operator; The prompt information is generated to indicate the one or more second candidate image regions.

7. The method according to claim 5, characterized in that, The step of generating prompt information based on the at least one second candidate image region includes: Calculate the ratio of the area to the perimeter of each of the at least one second candidate image regions; The ratio is normalized to the proportion of pixels belonging to the pixel band in the outer annular region, and the ratio and the pixel proportion are weighted and fused based on a preset weight coefficient to obtain the shape fitting degree of each second candidate image region. A first sub-information is generated, which is used to indicate one or more second candidate image regions whose shape fitting degree is greater than a first preset shape fitting degree threshold. The prompt information includes the first sub-information.

8. The method according to claim 7, characterized in that, The method further includes: Obtain the spatial location of each second candidate image region whose shape fitting degree is greater than the first preset shape fitting degree threshold; The second candidate image regions whose distance between the spatial locations is less than a preset distance threshold are merged to obtain at least one merged candidate region. In the at least one candidate region, candidate regions with shape fitting degree less than the second preset confidence threshold are removed to obtain at least one third candidate image region; A second sub-information is generated, which is used to indicate the at least one third candidate image region, and the prompt information also includes the second sub-information.

9. The method according to claim 4, characterized in that, The edge symmetry information includes the ratio between the average gray values ​​of two parts obtained by dividing the outer annular region on both sides along the first and / or second directions, with the centroid of the target image region as the reference. Both the first direction and the second direction are parallel to the bearing surface used to support the sample to be tested, and the first direction and the second direction are not collinear.

10. The method according to claim 9, characterized in that, Determining the adjustment information based on the edge symmetry information includes: Analyze the edge symmetry information to determine the positional deviation of the detector in the first direction and / or the second direction; The adjustment information is determined to indicate that the positional deviation is adjusted so that the ratio between the grayscale mean values ​​of the two parts is greater than or equal to 0.95 and less than or equal to 1.

05.

11. The method according to claim 9, characterized in that, The adjustment information is used to instruct the adjustment mechanism of the detector to adjust the placement position of the detector within the particle beam tube. The adjustment mechanism includes a fixing member and an adjustment assembly. The fixing member is used to fix the detector to the particle beam tube. The adjustment assembly connects the detector to the fixing member and is used to adjust the placement position of the detector. The adjustment assembly includes a first adjustment member for adjusting the placement position of the detector in the first direction and a second adjustment member for adjusting the placement position of the detector in the second direction. The adjustment information is used to instruct the adjustment of the first adjustment member and / or the second adjustment member to adjust the placement position of the detector through the first adjustment member and / or the second adjustment member.

12. The method according to claim 11, characterized in that, The first adjusting member includes at least two first set screws, which are arranged opposite to each other along the first direction and are used to push the detector from opposite directions to adjust the placement position of the detector in the first direction.

13. The method according to claim 11, characterized in that, The second adjusting member includes at least four second set screws, each of which is used to push the detector along the second direction to adjust the placement position of the detector in the second direction.

14. The method according to claim 1, characterized in that, The method further includes: After adjusting the deployment location, the reference detection image was reacquired; If the reference edge symmetry information of the reference detection image does not meet the preset conditions, reference adjustment information is determined based on the reference detection image. The reference edge symmetry information is used to reflect the result after the deployment position is adjusted. When the reference edge symmetry information meets the preset conditions or the number of adjustments reaches the preset number, the adjustment is determined to be complete.

15. The method according to claim 14, characterized in that, The process of re-acquiring the reference detection image after adjusting the deployment position includes: After adjusting the deployment location, multiple consecutive detection images are acquired from the time dimension; Calculate the variance of the edge symmetry information of the consecutive multiple detection images; If the variance is less than a preset stability threshold, the reference detection image is reacquired.

16. An electronic device, characterized in that, include: Memory and processor; Memory and processor are coupled; The memory is used to store instructions that can be executed by the processor; When the processor executes the instructions, it performs the method as described in any one of claims 1-15.

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