Joint test system, silicon carbide metasurface grating detection method and test device

Abstract

The invention discloses a joint test system, a silicon carbide metasurface grating detection method and a silicon carbide metasurface grating detection device, and belongs to the technical field of semiconductor detection. A synergistic excitation signal is generated by regulating a light source and a microwave source and is applied to the silicon carbide metasurface grating to be measured to form a light intensity and microwave field interference pattern. The method comprises the following steps: synchronously acquiring data through multiple channels, constructing a multi-dimensional matrix through time alignment, marker feature extraction and coordinate mapping, extracting defect features to establish a reference model, calculating deviation degree to judge quality, and carrying out abnormal point screening, candidate region construction and coupling verification on a defect grating to divide a potential defect region. For a potential defect area, excitation parameters are dynamically adjusted through a gradient descent algorithm, and defect grades are divided by combining the characteristic defect degree and the area. According to the invention, the problems of insufficient single-mode detection, low multi-mode alignment precision and blind adjustment of excitation parameters are solved, cross-band cooperative detection and intelligent grading are realized, and the detection reliability is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of semiconductor detection, and more specifically, to a combined test system, a detection method and a test device for a silicon carbide metasurface grating. Background Art

[0002] The performance of the silicon carbide metasurface grating to be measured is closely related to its structural parameters and material properties. However, due to its complex cross-band electromagnetic response characteristics and extreme environment adaptability requirements, the requirements for testing technology are constantly increasing.

[0003] Currently, the measurement of structural parameters mainly relies on scanning electron microscopes and atomic force microscopes. Scanning electron microscopes require a vacuum environment and have high equipment costs, and sample preparation may introduce damage. Atomic force microscopes have slow detection speeds and are difficult to meet the requirements for large-area and rapid detection. In optical performance testing, spectrophotometers are easily interfered by stray light, spectrometers are difficult to balance broadband resolution and scanning speed, and polarization state analyzers have slow response speeds and cannot analyze dynamic processes in real time. Microwave parameter testing has insufficient sensitivity to the response of sub-wavelength structures and cannot achieve spatio-temporal synchronization with optical testing. In addition, traditional testing equipment has poor stability when simulating extreme environments such as high and low temperatures, humidity, and irradiation, and lacks the ability for multi-physical field coupling dynamic monitoring.

[0004] The above technical bottlenecks have led to many problems in the testing of silicon carbide metasurface gratings: there are contradictions between efficiency and accuracy in structural parameter detection, optical performance measurement is limited by the equipment principle, it is difficult to capture nanoscale defects in microwave testing, and there is a lack of effective means for evaluating dynamic characteristics in extreme environments. How to break through the limitations of single-modal detection, achieve high-precision detection under multi-band collaborative excitation and extreme environment adaptability evaluation has become a key problem to be solved urgently in this field. Therefore, in order to overcome these limitations, the present invention proposes a combined test system, a detection method and a test device for a silicon carbide metasurface grating. Summary of the Invention

[0005] Aiming at the deficiencies of the existing technology, the purpose of the present invention is to provide a combined test system, a detection method and a test device for a silicon carbide metasurface grating, to solve the problems of insufficient sensitivity of single-modal detection, low spatio-temporal alignment accuracy of multi-modal data, and inaccurate defect feature extraction caused by blind adjustment of excitation signal parameters in the existing detection of silicon carbide metasurface gratings, and to achieve high-precision fusion detection of multi-modal data under cross-band collaborative excitation and intelligent classification of defect levels.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] The combined test system includes

[0008] Obtain the initial parameters of the optical wave excitation signal and the microwave excitation signal, and regulate the light source and the microwave source to generate a cooperative excitation signal, which is used to be applied to the silicon carbide metasurface grating under test to form an interference pattern;

[0009] The detector synchronously acquires the interference pattern multiple times in multiple channels to obtain multiple interference pattern data and perform preprocessing. The interference pattern data includes light intensity distribution data and microwave field strength data;

[0010] Preprocessing is used to align the interference pattern data in the time dimension, and extract feature points from the markers configured on the silicon carbide metasurface grating under test to map and match the coordinates of the interference pattern data, and construct a multi-dimensional data matrix;

[0011] Extract defect features from the multi-dimensional data matrix to construct a multi-dimensional feature matrix, calculate the deviation index of the silicon carbide metasurface grating under test through the established feature reference model for quality determination, and screen out stable abnormal points, perform edge detection to construct candidate defect connected regions, and perform region growth on the silicon carbide metasurface grating under test determined to have defects, and combine abnormal coupling verification to divide potential defect regions;

[0012] If a potential defect region is identified, then according to the information of the potential defect region, dynamically adjust the parameters of the optical wave excitation signal and the microwave excitation signal to update and adjust the cooperative excitation signal to regulate the interference pattern, obtain the updated interference pattern data and perform in-depth quality evaluation to divide the defect level.

[0013] Specifically, the specific steps for updating and adjusting the cooperative excitation signal include:

[0014] Obtain the structural parameter data and electromagnetic characteristic data of the silicon carbide metasurface grating under test to determine the initial excitation parameters of the optical wave excitation signal and the microwave excitation signal;

[0015] According to the determined initial excitation parameters, set the light source and the microwave source to generate a cooperative excitation signal;

[0016] Apply the cooperative excitation signal to the silicon carbide metasurface grating under test, collect the formed interference pattern data and perform preprocessing;

[0017] Extract defect features from the preprocessed interference pattern data, perform preliminary quality evaluation based on the extracted defect features, and judge whether there are defects in the silicon carbide metasurface grating under test;

[0018] If it is judged that there are defects, then divide the potential defect region of the silicon carbide metasurface grating under test, record the position and range information of the potential defect region, and trigger the regulation of the cooperative excitation signal.

[0019] Specifically, the specific steps for updating and adjusting the cooperative excitation signal also include:

[0020] Taking the ratio of the edge gradient of the interference pattern data in the potential defect area to the standard deviation of the noise as the objective function, it is used to regulate the excitation parameters of the optical wave excitation signal and the microwave excitation signal;

[0021] Within the range of excitation parameter adjustment, the excitation parameters are co-regulated through the gradient descent algorithm, specifically including:

[0022] Based on the initial excitation parameters of the optical wave excitation signal and the microwave excitation signal, determine the initial frequency difference and phase difference, and obtain the objective function value of the potential defect area;

[0023] Calculate the partial derivatives of the objective function with respect to the frequency difference and the phase difference, and update the frequency difference and the phase difference according to the gradient direction;

[0024] According to the updated frequency difference and phase difference, recalculate the frequencies and phases of the optical wave excitation signal and the microwave excitation signal, synthesize the updated optical wave excitation signal and microwave excitation signal into a new co-excitation signal, apply it to the silicon carbide metasurface grating to be measured to form an updated interference pattern, and calculate the objective function value of the potential defect area;

[0025] Configure a convergence threshold, calculate the change amount of the objective function value between two adjacent times. If the change amount is less than the convergence threshold, stop the update and output the optimized excitation parameters; otherwise, continue the iterative update.

[0026] Specifically, the specific steps for constructing the multi-dimensional data matrix include:

[0027] Using the time stamp of the synchronous clock signal, align the light intensity distribution data collected by the photodetector and the microwave field strength data collected by the microwave detector in the time dimension;

[0028] After removing the noise of the light intensity distribution data and enhancing the contrast, construct a scale space to detect the extreme points in the enhanced light intensity distribution data, determine the feature point positions of the markers on the silicon carbide metasurface grating to be measured, calculate the scale and direction information of each feature point, and generate a feature descriptor;

[0029] Adopt a threshold segmentation method for the microwave field strength data, set a segmentation threshold to separate the marker area, optimize the contour of the marker area through morphological processing, calculate the geometric center of the marker area as a feature point, and record the coordinate position of the feature point in the microwave field strength data;

[0030] Adopt a geometric calibration algorithm to map the coordinates of the light intensity distribution data to the physical coordinate positions of the silicon carbide metasurface grating to be measured, and at the same time match the coordinates of the microwave field strength data with the coordinates of the light intensity distribution data;

[0031] A fixed position on the silicon carbide metasurface grating to be measured is selected as the origin of the space coordinate system and the direction of the coordinate axis is determined, and the coordinates of the light intensity distribution data and the microwave field intensity data are converted into coordinates in the space coordinate system;

[0032] The light intensity distribution data and the microwave field intensity data are integrated according to the spatial coordinate system to construct a multidimensional data matrix, wherein the elements of the multidimensional data matrix correspond to the light intensity distribution data and the microwave field intensity data at corresponding positions in the silicon carbide metasurface grating to be tested.

[0033] Specifically, the specific steps of extracting defect features include:

[0034] Obtain the preprocessed multidimensional data matrix, and extract the corresponding defect features for the local area where each coordinate position in the multidimensional data matrix is ​​located. The defect features exist in the form of multidimensional feature vectors, including light intensity distribution features, microwave field features, and cross-modal correlation features;

[0035] The light intensity distribution feature is extracted in the following way: the adjacent area is demarcated with the coordinate position of the multidimensional data matrix as the center to calculate the light intensity contrast index; the actual fringe period corresponding to the coordinate position of the multidimensional data matrix is ​​compared with the configured theoretical period, and the deviation rate is calculated as the fringe spacing uniformity index; the gradient amplitude of the edge position of the multidimensional data matrix is ​​calculated, and the standard deviation of the gradient amplitude of the adjacent edge position is calculated as the edge gradient consistency index;

[0036] The microwave field characteristics are extracted in the following ways: the local area centered on the coordinate position in the multidimensional data matrix is ​​divided into analysis units, and the variance of the microwave phase value in the local area is calculated as a microwave phase stability index; the microwave field intensity in the local area is compared with the standard microwave field intensity, and the ratio of the microwave field intensity to the standard microwave field intensity is calculated as a field intensity attenuation rate characteristic index;

[0037] The cross-modal correlation feature is extracted by calculating the light intensity gradient and microwave phase gradient respectively for the light intensity distribution data and microwave field intensity data corresponding to the coordinate position in the multidimensional data matrix, and calculating the correlation index of the light intensity gradient and the microwave phase gradient through the correlation coefficient;

[0038] The extracted defect features are dedimensionalized so that each coordinate position in the multidimensional data matrix generates a set of defect feature vectors to construct a multidimensional feature matrix.

[0039] Specifically, the steps for conducting a preliminary quality assessment include:

[0040] Establishing a standard feature set, the standard feature set includes a multi-dimensional feature matrix of a defect-free standard silicon carbide metasurface grating sample;

[0041] Statistically calculate the mean vector and covariance matrix of each defect feature in the multi-dimensional feature matrix within the statistical standard feature set, and construct a distribution model of the multi-dimensional feature matrix in the standard feature set as the feature benchmark model;

[0042] Obtain the interference pattern data of the to-be-tested silicon carbide metasurface grating, construct a multi-dimensional data matrix and a multi-dimensional feature matrix, calculate the distance between the defect feature vectors at each coordinate position in the multi-dimensional feature matrix and the feature benchmark model, and generate a deviation matrix;

[0043] Perform global averaging on the deviation matrix to obtain the deviation index for each interference pattern data; configure a distance threshold, compare the deviation index with the distance threshold. If there is a deviation index greater than the distance threshold, it is determined that the to-be-tested silicon carbide metasurface grating has defects, otherwise it is determined to be qualified.

[0044] Specifically, the specific steps for preliminary quality assessment further include:

[0045] If it is determined that the to-be-tested silicon carbide metasurface grating has defects, then based on the deviation matrix and the distance threshold, for the multi-dimensional data matrix of the interference pattern data collected multiple times, mark the coordinate points with deviation indices greater than the distance threshold as suspected defect points to form a binary abnormal point matrix, statistically calculate the abnormal occurrence frequency of the same coordinate point, and screen out the stable abnormal points with abnormal occurrence frequencies greater than the preset frequency threshold;

[0046] Perform edge detection on the stable abnormal points to extract continuous edge contours, and form candidate defect connected regions through morphological dilation operations;

[0047] Select seed points in the candidate defect connected regions according to the deviation index ranking. Taking the seed points as the center, perform region growing according to the light intensity distribution characteristics, microwave field characteristics, and cross-modal correlation characteristics until the preset growth termination condition is met;

[0048] Configure an effective threshold, calculate the proportion of coordinates with both abnormal light intensity distribution and abnormal microwave field in each potential defect region. If the proportion is greater than the effective threshold, it is confirmed as an effective potential defect region, otherwise it is excluded.

[0049] Specifically, the specific steps for in-depth quality assessment include:

[0050] Obtain the multi-dimensional data matrix and its multi-dimensional feature matrix of each updated interference pattern data, map the coordinates of the potential defect regions to the updated multi-dimensional data matrix, and generate a potential defect region mask;

[0051] Based on the potential defect region mask, calculate the mean and standard deviation of the defect feature vectors in the potential defect regions and non-potential defect regions respectively, and calculate the difference degree of each defect feature vector;

[0052] According to the mean and standard deviation of the defect feature vectors of the calculated potential defect regions and non-potential defect regions, calculate the defect degree of the defect feature vectors of each potential defect region, which is used to quantify the difference degree between the potential defect regions and non-potential defect regions in each defect feature dimension;

[0053] According to the defect degree of each defect feature vector of the potential defect region and the area of the potential defect region, divide the defect levels into mild defect level, moderate defect level, and severe defect level.

[0054] A detection method for a silicon carbide metasurface grating by joint testing, comprising the following steps:

[0055] Step S1: Obtain the initial parameters of the optical wave excitation signal and the microwave excitation signal, regulate the light source and the microwave source to generate a cooperative excitation signal, and apply the cooperative excitation signal to the silicon carbide metasurface grating to be measured to form an interference pattern including the light intensity distribution and the microwave field intensity distribution;

[0056] Step S2: Synchronously collect the interference pattern through a detector multiple times in multiple channels to obtain multiple interference pattern data, and the interference pattern data includes light intensity distribution data and microwave field intensity data; Align the interference pattern data in the time dimension, and by extracting the feature points of the markers on the silicon carbide metasurface grating to be measured, map and match the coordinates of the interference pattern data to construct a multi-dimensional data matrix;

[0057] Step S3: Extract defect features from the multi-dimensional data matrix to construct a multi-dimensional feature matrix, establish a feature reference model based on the multi-dimensional feature matrix of the defect-free standard silicon carbide metasurface grating sample, and calculate the deviation index of the silicon carbide metasurface grating to be measured for quality determination; If it is determined that there are defects, then screen stable abnormal points, perform edge detection to construct candidate defect connected regions, perform region growth based on the multi-modal feature similarity condition, and verify by combining the coupling of the light intensity and microwave field anomalies to divide the potential defect regions;

[0058] Step S4: If potential defect regions are identified, dynamically adjust the parameters of the optical wave excitation signal and the microwave excitation signal according to the information of the potential defect regions, update the cooperative excitation signal and obtain the updated interference pattern data; Based on the defect degrees of the defect regions and non-defect regions in the updated multi-dimensional feature matrix and the area of the potential defect regions, divide the defect levels.

[0059] A test device, comprising:

[0060] An optical wave signal source, configured to generate optical wave excitation signals with different wavelengths, powers, and polarization states;

[0061] A microwave signal source, configured to generate microwave excitation signals with different frequencies, phases, and powers;

[0062] A trigger controller for generating a synchronous clock signal to achieve time synchronization between an optical wave signal source and a microwave signal source;

[0063] A signal synthesizer for synthesizing an optical wave excitation signal and a microwave excitation signal into a collaborative excitation signal;

[0064] An optoelectronic detection array for collecting light intensity distribution data and identifying marker feature points;

[0065] A microwave scanning probe system for collecting microwave field strength data and recording scanning coordinates;

[0066] A data processing unit for performing marker feature extraction and coordinate mapping algorithms, constructing a multi-dimensional data matrix, extracting light intensity distribution features, microwave field features, and cross-modal correlation features, generating defect feature vectors, and constructing a multi-dimensional feature matrix;

[0067] A defect detection unit for performing potential defect area division and dividing defect levels based on feature defect degree, area ratio, and cross-modal coincidence degree;

[0068] A parameter regulation actuator for dynamically adjusting excitation parameters according to potential defect area information and optimizing excitation parameters through a gradient descent algorithm.

[0069] Advantages of the present invention:

[0070] Through multi-modal collaborative excitation and multi-channel synchronous acquisition, the complementary of cross-band information is realized by fusing light intensity and microwave field data, solving the problems of insufficient detection sensitivity of a single mode and weak ability to capture nano-scale defects, and significantly improving the detection accuracy; the construction of a multi-dimensional data matrix and multi-modal feature extraction realize the comprehensive characterization of the grating performance, and combined with the feature reference model and deviation analysis, the existence of defects can be accurately determined; during the potential defect area division process, through stable outlier screening, region growing, and coupling verification, noise interference is effectively excluded, and accurate defect positioning is achieved; the closed-loop feedback mechanism for dynamically adjusting the excitation signal parameters can adaptively optimize the detection conditions, enhance the defect feature contrast, and improve the detection reliability; the overall solution breaks through the limitations of traditional equipment in terms of efficiency, accuracy, and environmental adaptability, realizes the efficient, accurate detection and intelligent grading of the silicon carbide metasurface grating to be measured, and provides an effective means for the performance evaluation of the silicon carbide metasurface grating to be measured in extreme environments. Description of the Drawings

[0071] Figure 1 It is a schematic structural diagram of a joint test system of the present invention;

[0072] Figure 2 It is a flow chart of the specific steps for updating and adjusting the collaborative excitation signal of the present invention;

[0073] Figure 3Flow chart of the specific steps for preprocessing the collected interference pattern data in the present invention;

[0074] Figure 4 Flow chart of the present invention for preliminary quality assessment and division of potential defect regions;

[0075] Figure 5 Flow chart of a joint - testing method for detecting a silicon carbide metasurface grating in the present invention. Detailed implementation manners

[0076] Example 1:

[0077] Please refer to Figure 1 , this example introduces a joint - testing system, including a multi - source excitation module, a signal acquisition module, a signal processing module, and a quality assessment module:

[0078] The multi - source excitation module aims to generate a cross - band collaborative excitation signal containing light waves and microwaves, apply it to the silicon carbide metasurface grating to be tested to form an interference pattern, and dynamically adjust the parameters of the collaborative excitation signal according to the information of potential defect regions to update and optimize the interference pattern. By changing the period, spatial distribution, and phase distribution of the interference pattern, it can more clearly reflect the possible defect information in the target area, improving the sensitivity and accuracy of defect detection.

[0079] Specifically, obtain the structural parameters and electromagnetic characteristics of the silicon carbide metasurface grating to be tested. The structural parameters include the period, height, and duty cycle of the silicon carbide metasurface grating to be tested, and the electromagnetic characteristics include the dielectric constant and permeability. Combining the detection requirements for different sizes and types of defects, determine the excitation signal parameters such as the frequency range, amplitude range, and phase relationship of light waves and microwaves. Arrange multiple markers with obvious features on the surface of the silicon carbide metasurface grating to be tested, and the markers need to be clearly identifiable in the scans of the photodetector and microwave detector. For example, use tiny metal dots or patterns with high contrast as markers, and the positions of the markers need to be accurately measured and their coordinates in the actual physical coordinate system of the silicon carbide metasurface grating to be tested need to be recorded. Regulate the light wave source and microwave source to make each signal source stably output signals that meet specific frequency, intensity, and phase requirements. And to ensure signal coordination, use a synchronous clock signal to trigger the light wave and microwave signal sources to achieve time synchronization. On this basis, by regulating the frequencies of light waves and microwaves, change the frequency difference between the two, and then adjust the period and spatial distribution of the interference pattern to meet the detection requirements for different sizes and types of defects. Use a phase modulator to adjust the phases of light waves and microwaves, change the phase distribution of the interference pattern, and input the regulated, synchronized, and phase - adjusted light wave and microwave signals into a signal synthesizer for synthesis to form a cross - band collaborative excitation signal.

[0080] Please refer toFigure 2 , preferably, the specific steps for updating and adjusting the collaborative excitation signal include:

[0081] Obtain the structural parameter data and electromagnetic characteristic data of the silicon carbide metasurface grating to be measured. Using multi-physics field simulation software, establish an interference model of the silicon carbide metasurface grating to be measured based on the collected data. Conduct optical wave and microwave excitation simulations under different parameters on this interference model, and analyze the formation of interference patterns under different excitation parameter combinations. Combining the simulation results, as well as the structural parameter data and electromagnetic characteristic data of the silicon carbide metasurface grating to be measured, preliminarily determine the initial excitation parameter values of the optical wave excitation signal and the microwave excitation signal. The excitation parameters include frequency, amplitude, and phase.

[0082] According to the initial excitation parameters, set the optical wave source and the microwave source. For the optical wave source, adjust parameters such as the cavity length and operating current of the laser to achieve the required frequency; for the microwave source, set the output frequency and amplitude through the control panel of the signal generator. At the same time, set the synchronous clock signal to ensure the temporal synchronization of the optical wave signal source and the microwave signal source; input the set optical wave signal and microwave signal into the signal synthesizer for synthesis to form a collaborative excitation signal.

[0083] Apply the collaborative excitation signal to the silicon carbide metasurface grating to be measured, and use a photodetector and a microwave detector to collect the interference pattern data formed; preprocess the collected interference pattern data, remove noise interference, and perform normalization processing to unify the signal data of different acquisition channels and different acquisition times within the same scale range.

[0084] Extract defect features from the preprocessed interference pattern data, including contrast, fringe spacing, fringe shape, amplitude change, phase change, etc. Conduct a preliminary quality assessment based on the extracted defect features to determine whether there are defects in the silicon carbide metasurface grating to be measured;

[0085] If there are no defects, it is determined that the quality of the silicon carbide metasurface grating to be measured is qualified;

[0086] If it is determined that there are defects, divide the potential defect area of the silicon carbide metasurface grating to be measured, record the position and range information of the potential defect area, and trigger the regulation of the collaborative excitation signal, that is:

[0087] Configure the excitation parameter adjustment ranges of the optical wave excitation signal and the microwave excitation signal, including the frequency adjustment range, amplitude adjustment range, and phase adjustment range;

[0088] Take maximizing the ratio of the edge gradient to the noise standard deviation of the interference pattern data in the potential defect area as the objective function, and regulate the excitation parameters of the optical wave excitation signal and the microwave excitation signal. The objective function The expression is as follows:

[0089]

[0090] Among them, is the frequency difference between the optical wave excitation signal and the microwave excitation signal, is the phase difference between the optical wave excitation signal and the microwave excitation signal, is the intensity distribution of the interference pattern, is the edge gradient of the interference pattern, is the noise standard deviation of the interference pattern. The objective function is to maximize the ratio of the edge gradient and the phase difference of the interference pattern by adjusting the frequency difference and the phase difference of the optical wave excitation signal and the microwave excitation signal, so as to enhance the contrast of the defect edge and improve the accuracy of defect detection.

[0091] Within the adjustment range of the excitation parameters of the optical wave excitation signal and the microwave excitation signal, the excitation parameters are jointly regulated by the gradient descent algorithm, that is:

[0092] According to the initial excitation parameters of the optical wave excitation signal and the microwave excitation signal, determine the frequency difference and phase difference of the initial optical wave excitation signal and the microwave excitation signal, and obtain the objective function value of the potential defect area.

[0093] Calculate the partial derivatives of the objective function with respect to the frequency difference and the phase difference, and update the frequency difference and the phase difference according to the gradient direction. The update formula is:

[0094]

[0095]

[0096] Among them, is the learning rate, and its value range is (0, 1), is the th updated frequency difference, is the th updated frequency difference, is the partial derivative of the objective function with respect to the frequency difference, is the th updated phase difference, is the th updated phase difference, is the partial derivative of the objective function with respect to the phase difference.

[0097] Obtain the updated frequency difference and phase difference, recalculate the frequencies and phases of the optical wave excitation signal and the microwave excitation signal, re-enter the updated optical wave signal and microwave signal into the signal synthesizer for synthesis to form an updated co-excitation signal, and apply it to the silicon carbide metasurface grating to be measured to form an updated interference pattern, and calculate the objective function value of the potential defect area;

[0098] Configure a convergence threshold, calculate the change amount of the objective function value between two adjacent times. If it is less than the convergence threshold, stop the update and output the excitation parameter values of the optimized optical wave excitation signal and microwave excitation signal. Otherwise, continue the iterative update.

[0099] The signal acquisition module is used to acquire the interference pattern data generated by the silicon carbide metasurface grating to be measured under the action of the co-excitation signal, including the interference pattern data under the initial excitation parameter values, the interference pattern data under the co-excitation signal regulation state, and the updated interference pattern data under the excitation parameter values of the optimized optical wave excitation signal and microwave excitation signal, and perform multiple acquisitions according to the set sampling frequency and sampling time to improve the accuracy of the data and provide data support for the subsequent signal processing and quality evaluation links.

[0100] The signal acquisition module is equipped with multiple detectors, including a photodetector for collecting optical wave signals and a microwave detector for collecting microwave signals, so as to realize multi-channel synchronous acquisition of the interference pattern data. The interference pattern data includes the light intensity distribution data collected by the photodetector and the microwave field strength data collected by the microwave detector.

[0101] Use a photodetector to image the silicon carbide metasurface grating to be measured arranged with markers and collect the light intensity distribution data containing the markers. At the same time, use a microwave detector to perform a spatial scan on the silicon carbide metasurface grating to be measured, record the position information of each scan point and the corresponding microwave field strength data. During the scanning process, ensure the consistency and accuracy of the scanning path.

[0102] The collected interference pattern data will be transmitted to the signal processing module in real time. The high-speed data transmission interface is used during the transmission process to ensure the stability and reliability of the data transmission. During the data transmission process, perform preliminary preprocessing operations such as denoising and filtering on the collected signals, and synchronize and calibrate the data collected from different channels to ensure the consistency and comparability of the data and provide a high-quality data basis for subsequent processing.

[0103] The signal processing module preprocesses each acquired interference pattern data, integrates the light intensity distribution data and the microwave field intensity data, constructs a multi-dimensional data matrix, extracts defect features helpful for quality assessment from the multi-dimensional data matrix to construct a multi-dimensional feature matrix, and under the condition of coordinated excitation signal regulation, real-time extracts the edge gradient and noise standard deviation of the potential defect area in the interference pattern data and feeds them back to the multi-source excitation module.

[0104] Please refer to Figure 3 , preferably, the specific steps for preprocessing the acquired interference pattern data include:

[0105] Through the time stamp of the synchronous clock signal, align the light intensity distribution data collected by the photodetector and the microwave field intensity data collected by the microwave detector in the time dimension, ensuring that the light intensity distribution data and the microwave field intensity data obtained at any same moment correspond to the same physical state of the silicon carbide metasurface grating to be measured, thereby laying a foundation for subsequent data processing and analysis in terms of time consistency.

[0106] Remove the noise of the light intensity distribution data through a filtering algorithm and use a contrast enhancement algorithm to enhance the contrast of the light intensity distribution data.

[0107] By constructing a scale space, detect the extreme points in the enhanced light intensity distribution data to determine the characteristic point positions of the markers. For each extracted characteristic point, calculate its scale and direction information, and then generate a feature descriptor for characterizing the characteristic point, and record the coordinate positions of each characteristic point in the light intensity distribution data to provide key information for subsequent coordinate transformation and matching.

[0108] Adopt a threshold segmentation method for the microwave field intensity data. According to the characteristics of the microwave field intensity data, set the segmentation threshold to separate the marker area from the background data to achieve the preliminary identification and marking of the marker area. For the segmented marker area, use a morphological processing method to optimize the contour of the marker area, remove the noise and isolated points in the marker area, and make the marker area clearer and more accurate. On this basis, calculate the geometric center of each marker area and use it as the characteristic point of the marker in the microwave field intensity data, and at the same time record the coordinate positions of each characteristic point in the microwave field intensity data.

[0109] Adopt a geometric calibration algorithm. According to the feature descriptors of the feature points in the light intensity distribution data and the coordinate positions of the feature points in the microwave field intensity data, map the coordinates of the light intensity distribution data after feature extraction to the physical coordinate positions of the silicon carbide metasurface grating to be measured, realizing the conversion of the light intensity distribution data from image pixel coordinates to actual physical space coordinates. At the same time, use this geometric calibration algorithm to match the microwave scanning coordinates in the microwave field intensity data with the coordinates of the light intensity distribution data, ensuring an accurate corresponding relationship between the two under the same spatial reference frame, and providing a prerequisite for the subsequent establishment of the spatial coordinate system and data integration.

[0110] Select a fixed and easily recognizable position on the silicon carbide metasurface grating to be measured as the origin of the spatial coordinate system, such as a corner point of the silicon carbide metasurface grating to be measured; and determine the directions of the coordinate axes according to the structural characteristics and detection requirements of the silicon carbide metasurface grating to be measured.

[0111] For the coordinates of the light intensity distribution data after mapping and the coordinates of the microwave field intensity data after conversion, convert them to the coordinates in this spatial coordinate system according to the origin position and the directions of the coordinate axes of the established spatial coordinate system. Through this conversion process, the light intensity distribution data and the microwave field intensity data have a consistent coordinate expression form in the unified spatial coordinate system.

[0112] Integrate the light intensity distribution data and the microwave field intensity data according to the established spatial coordinate system to construct a multi-dimensional data matrix containing multi-modal features. In this multi-dimensional data matrix, each element of the multi-dimensional data matrix corresponds to multi-faceted data information at a specific coordinate position on the silicon carbide metasurface grating to be measured, including light intensity distribution data, microwave field intensity data, and associated feature information. Through this integration method, it provides a rich and orderly data basis for the subsequent comprehensive analysis and defect detection of the silicon carbide metasurface grating to be measured.

[0113] Preferably, the specific steps for extracting defect features include:

[0114] Obtain the preprocessed multi-dimensional data matrix, and extract the corresponding defect features for the local area where each coordinate position is located in the multi-dimensional data matrix. The defect features exist in the form of multi-dimensional feature vectors, specifically including three categories: light intensity distribution features, microwave field features, and cross-modal correlation features.

[0115] The light intensity distribution features include: for any coordinate position in the multi-dimensional data matrix, demarcate a square or rectangular adjacent area centered on this position, and obtain the light intensity distribution contrast index by calculating the light intensity difference in the adjacent area. The value of this contrast index reflects the brightness difference degree of the surrounding area of this position.

[0116] Based on the periodic characteristics of the fringes in the light intensity distribution data, measure the actual fringe period of the area where the position is located, compare it with the theoretical period determined by the grating design parameters, and calculate the deviation rate as the fringe spacing uniformity index to detect abnormal changes in the fringe period.

[0117] Determine whether the position is an edge pixel through an edge detection algorithm. If it is an edge pixel, calculate its gradient amplitude, and calculate the standard deviation of the gradient amplitudes of adjacent edge pixels as the edge gradient consistency index to characterize the fluctuation degree of the light intensity change at the edge.

[0118] The microwave field characteristics include: dividing the local area centered on the coordinate position in the multi-dimensional data matrix into analysis units, and calculating the variance of the microwave phase values in this local area as the microwave phase stability index to reflect the fluctuation of the microwave phase in this local area.

[0119] Compare the microwave field strength of this local area with the standard microwave field strength measured from a defect-free standard sample or calculated by a theoretical model, and calculate the ratio of the microwave field strength to the standard microwave field strength as the field strength attenuation rate index to measure the abnormal attenuation degree of the microwave field strength in this local area.

[0120] The cross-modal correlation characteristics include: for the light intensity distribution data and microwave field strength data corresponding to the coordinate position, calculate the light intensity gradient and the microwave phase gradient respectively. Among them, the light intensity gradient reflects the spatial change rate of the light intensity, and the microwave phase gradient reflects the spatial change rate of the microwave phase. Calculate the correlation index between the two through the correlation coefficient to identify the abnormal coupling state of the electromagnetic characteristics and geometric structure at this position.

[0121] Perform dimensionless processing on the extracted defect characteristics so that each coordinate position in the multi-dimensional data matrix generates a set of defect feature vectors with unified dimensions including light intensity distribution, microwave field, and cross-modal correlation information, and construct a multi-dimensional feature matrix.

[0122] The quality evaluation module extracts the multi-dimensional feature matrix based on the interference pattern data to evaluate the quality of the to-be-tested silicon carbide metasurface grating. If the interference pattern data is collected by setting the optical wave source and microwave source according to the initial excitation parameters to generate a cooperative excitation signal, then extract the defect characteristics from the interference pattern data for preliminary quality evaluation, judge whether there is a potential defect area, and identify and divide the potential defect area; if the updated interference pattern data is collected based on the updated cooperative excitation signal, then extract the defect characteristics from the updated interference pattern data to perform in-depth quality evaluation through the quality evaluation module.

[0123] Please refer to Figure 4 , preferably, the specific steps for performing preliminary quality evaluation and dividing the potential defect area include:

[0124] Based on multiple defect-free standard silicon carbide metasurface grating samples, a standard feature set is established. The standard feature set contains the multi-dimensional feature matrices of multiple defect-free standard silicon carbide metasurface grating samples. The mean vector and covariance matrix of each defect feature are statistically calculated, and a distribution model of the multi-dimensional feature matrices in the standard feature set is established as the feature reference model.

[0125] Obtain the multi-dimensional data matrix and its multi-dimensional feature matrix of each collected interference pattern data. For each coordinate position in each multi-dimensional feature matrix, calculate the distance between its defect feature vector and the feature reference model to generate a deviation matrix, and perform global averaging on the deviation matrix as the deviation index of each interference pattern data. The distance measurement methods include Mahalanobis distance, Euclidean distance, etc.

[0126] According to the distance distribution of the defect-free standard silicon carbide metasurface grating samples in the standard feature set, configure a distance threshold, and compare the deviation index value of the multi-dimensional feature matrix of each interference pattern data with this distance threshold. If there is an interference pattern data whose deviation index value of the multi-dimensional feature matrix is greater than the distance threshold, it is determined that the silicon carbide metasurface grating to be tested has defects; otherwise, it is determined that the silicon carbide metasurface grating to be tested passes the test.

[0127] If it is determined that the silicon carbide metasurface grating to be tested has defects, potential defect area division is carried out, specifically including:

[0128] For each collected interference pattern data, based on the deviation matrix and the distance threshold, mark the coordinate points in the multi-dimensional feature matrix whose deviation index is greater than the distance threshold as suspected defect points to form a binary abnormal point matrix.

[0129] For the interference pattern data collected multiple times, count the occurrence frequency of the same coordinate point in the binary abnormal point matrices of each round. Configure a frequency threshold through experiments or historical data, and select the coordinate points with an occurrence frequency greater than the frequency threshold as stable abnormal points to generate a stable abnormal point matrix to suppress accidental misjudgment caused by single-time acquisition noise.

[0130] Extract continuous edge contours from the stable abnormal point matrix through edge detection, retain strong edges and connect weak edges to ensure the integrity of the defect boundary. And set a sliding window to perform morphological dilation operations on the edge contours, connect adjacent edge points and fill in small gaps to form closed candidate defect connected regions, so that discrete abnormal points are aggregated into continuous regions with physical significance.

[0131] Configure the seed point ratio In the candidate defect connected region, sort according to the deviation index from high to low, and select the top-ranked Use a coordinate point as the seed point to ensure that the seed point is located at the position with the most significant boundary features between the defect and the normal area, providing a typical starting point for the growth of the potential abnormal area.

[0132] Centered on the seed point, grow the potential abnormal area. The growth conditions comprehensively consider the light intensity distribution characteristics, microwave field characteristics, and cross-modal correlation characteristics.

[0133] For the light intensity distribution characteristics, the difference in the light intensity contrast index between the neighborhood pixels and the seed point is within the preset contrast range.

[0134] For the microwave field characteristics, the differences in the microwave phase stability index and the field strength attenuation rate characteristic index between the neighborhood pixels and the seed point do not exceed the preset phase difference threshold and attenuation rate threshold.

[0135] For the cross-modal correlation characteristics, the difference in the correlation index between the light intensity gradient and the microwave phase gradient of the neighborhood pixels and the seed point is within the preset range.

[0136] Merge the neighborhood pixels that meet the growth conditions into the current potential abnormal area until no new neighborhood meets the conditions or the potential abnormal area reaches the preset minimum detection unit size.

[0137] Configure an effective threshold. For each potential defect area, calculate the proportion of coordinates where both light intensity distribution anomalies and microwave field anomalies exist. If the proportion is greater than the effective threshold, confirm that the potential defect area is a valid potential defect area; otherwise, determine it as a pseudo-potential defect area and eliminate it to exclude non-structural defect interference.

[0138] Preferably, the specific steps of depth quality assessment include:

[0139] Obtain the multi-dimensional data matrix and its multi-dimensional feature matrix of each updated interference pattern data, map the coordinates of the potential defect area to the updated multi-dimensional data matrix, and generate a potential defect area mask.

[0140] Based on the potential defect area mask, calculate the mean and standard deviation of the defect feature vectors in the potential defect area and the non-potential defect area respectively to calculate the difference degree of each defect feature vector; the mean reflects the average performance of the potential defect area and the non-potential defect area in each feature dimension, and the standard deviation measures the degree of dispersion of the feature values in the potential defect area and the non-potential defect area.

[0141] According to the calculated mean and standard deviation of the defect feature vectors in the potential defect area and the non-potential defect area, calculate the defect degree of the defect feature vectors in each potential defect area. The calculation method of the defect degree can adopt various methods, such as Euclidean distance, cosine similarity, etc. By calculating the defect degree, quantify the difference degree between the potential defect area and the non-potential defect area in each feature dimension.

[0142] According to the defect degree of each defect feature vector in the potential defect area and the area of the potential defect area, defect levels are classified into mild defect level, moderate defect level, and severe defect level, and each level corresponds to different defect degree ranges and defect feature manifestations.

[0143] Specifically, the specific steps for defect level classification include:

[0144] Based on the multi-dimensional feature distribution of the defect-free standard silicon carbide metasurface grating sample and the specific requirements of actual detection, defect degree thresholds and area ratio thresholds are determined. The defect degree thresholds are divided into upper defect degree threshold and lower defect degree threshold, which are determined by statistically analyzing the feature difference ranges of defect-free areas and known defect areas. The area ratio thresholds include upper area ratio threshold and lower area ratio threshold, which are set by combining the minimum functional unit size of the grating and its influence on performance.

[0145] For the determination of mild defect level, potential defect areas with defect feature vector defect degrees less than the lower defect degree threshold and area ratios lower than the lower area ratio threshold are screened out from the potential defect areas. And further verify whether there is only a single-mode feature anomaly in this defect area, including the light intensity distribution feature or the microwave field feature deviating from the normal range alone. If the above conditions are met, it is determined as a mild defect, and its specific position and deviation degree in the physical coordinate system of the grating are recorded.

[0146] For the determination of moderate defect level, potential defect areas with defect feature vector defect degrees greater than or equal to the lower defect degree threshold and less than the upper defect degree threshold, or area ratios greater than or equal to the lower area ratio threshold and less than the upper area ratio threshold, are determined as moderate defects. For these areas, focus on checking whether at least two types of mode features appear abnormally at the same time. For example, the light intensity distribution feature may have a problem of stripe period deviation, while the microwave field feature also has an abnormal field strength attenuation at the same time. And also check whether there is a correlation in space for these abnormal areas, that is, the overlapping area ratio of the light intensity abnormal area and the microwave field abnormal area.

[0147] For the determination of severe defect level, potential defect areas with defect feature vector defect degrees greater than or equal to the upper defect degree threshold, or area ratios greater than or equal to the upper area ratio threshold, are determined as severe defects. For these areas, check the abnormalities of light intensity distribution, microwave field, and cross-modal correlation features, as well as the defect feature deviation degree.

[0148] Embodiment 2

[0149] Please refer to Figure 5, this embodiment introduces a detection method for a silicon carbide metasurface grating in a joint test, including the following steps:

[0150] Step S1: Obtain the initial parameters of the optical wave excitation signal and the microwave excitation signal, regulate the light source and the microwave source to generate a collaborative excitation signal, and apply the collaborative excitation signal to the silicon carbide metasurface grating to be tested to form an interference pattern including the light intensity distribution and the microwave field intensity distribution;

[0151] Step S2: Synchronously collect the interference pattern through the detector multiple times in multiple channels to obtain multiple interference pattern data. The interference pattern data includes light intensity distribution data and microwave field intensity data; align the interference pattern data in the time dimension, and map and match the coordinates of the interference pattern data by extracting the feature points of the markers on the silicon carbide metasurface grating to be tested to construct a multi-dimensional data matrix;

[0152] Step S3: Extract the defect features from the multi-dimensional data matrix to construct a multi-dimensional feature matrix, establish a feature reference model based on the multi-dimensional feature matrix of the defect-free standard silicon carbide metasurface grating sample, and calculate the deviation index of the silicon carbide metasurface grating to be tested for quality determination; if it is determined that there are defects, then screen the stable abnormal points in multiple rounds, detect the edges to construct candidate defect connected regions, perform region growing based on the multi-modal feature similarity condition, and combine the coupling verification of the light intensity and microwave field anomalies to divide the potential defect regions;

[0153] Step S4: If a potential defect region is identified, dynamically adjust the parameters of the optical wave excitation signal and the microwave excitation signal according to the information of the potential defect region, update the collaborative excitation signal and obtain the updated interference pattern data; based on the defect degrees of the defect region and the non-defect region in the updated multi-dimensional feature matrix and the area of the potential defect region, divide the defect levels.

[0154] Preferably, the specific steps for updating and adjusting the collaborative excitation signal include:

[0155] Obtain the structural parameter data and electromagnetic characteristic data of the silicon carbide metasurface grating to be tested to determine the initial excitation parameter values of the optical wave excitation signal and the microwave excitation signal;

[0156] Set the light source and the microwave source according to the determined initial excitation parameters to generate a collaborative excitation signal;

[0157] Apply the collaborative excitation signal to the silicon carbide metasurface grating to be tested, collect the formed interference pattern data and perform preprocessing;

[0158] Extract the defect features from the preprocessed interference pattern data, perform a preliminary quality assessment based on the extracted defect features, and determine whether there are defects in the silicon carbide metasurface grating to be tested;

[0159] If a defect is judged to exist, the potential defect area of the silicon carbide metasurface grating to be measured is divided, the position and range information of the potential defect area are recorded, and the coordinated excitation signal regulation is triggered;

[0160] Taking the ratio of maximizing the edge gradient of the interference pattern data in the potential defect area to the standard deviation of the noise as the objective function, which is used to regulate the excitation parameters of the optical wave excitation signal and the microwave excitation signal;

[0161] Within the range of excitation parameter adjustment, the excitation parameters are coordinately regulated by the gradient descent algorithm, specifically including:

[0162] Based on the initial excitation parameters of the optical wave excitation signal and the microwave excitation signal, the initial frequency difference and phase difference are determined, and the objective function value of the potential defect area is obtained;

[0163] Calculate the partial derivatives of the objective function with respect to the frequency difference and the phase difference, and update the frequency difference and the phase difference according to the gradient direction;

[0164] According to the updated frequency difference and phase difference, recalculate the frequencies and phases of the optical wave excitation signal and the microwave excitation signal, synthesize the updated optical wave excitation signal and the microwave excitation signal into a new coordinated excitation signal, apply it to the silicon carbide metasurface grating to be measured to form an updated interference pattern, and calculate the objective function value of the potential defect area;

[0165] Configure a convergence threshold, calculate the change amount of the objective function value between adjacent two times. If the change amount is less than the convergence threshold, stop the update and output the optimized excitation parameters; otherwise, continue the iterative update.

[0166] Preferably, the specific steps for preliminary quality assessment include:

[0167] Establish a standard feature set, which includes the multi-dimensional feature matrix of the defect-free standard silicon carbide metasurface grating sample;

[0168] Statistically analyze the mean vector and covariance matrix of each defect feature in the multi-dimensional feature matrix in the standard feature set, and construct the distribution model of the multi-dimensional feature matrix in the standard feature set as the feature reference model;

[0169] Obtain the interference pattern data of the silicon carbide metasurface grating to be measured, construct a multi-dimensional data matrix and a multi-dimensional feature matrix, calculate the distance between the defect feature vectors at each coordinate position in the multi-dimensional feature matrix and the feature reference model, and generate a deviation matrix;

[0170] Perform global averaging on the deviation matrix to obtain the deviation index of each interference pattern data; configure a distance threshold, compare the deviation index with the distance threshold. If there is a deviation index greater than the distance threshold, it is determined that the silicon carbide metasurface grating to be measured has a defect, otherwise it is determined to be qualified;

[0171] If it is determined that there are defects in the to-be-tested silicon carbide metasurface grating, based on the deviation matrix and the distance threshold, for the multi-dimensional data matrix of the interference pattern data collected multiple times, the coordinate points with deviation indexes greater than the distance threshold are marked as suspected defect points, forming a binary abnormal point matrix. The abnormal occurrence frequency of the same coordinate point is statistically analyzed, and the stable abnormal points with abnormal occurrence frequency greater than the preset frequency threshold are screened out;

[0172] Edge detection is performed on the stable abnormal points to extract continuous edge contours, and a closed candidate defect connected domain is formed through morphological dilation operation;

[0173] Seed points are selected in the candidate defect connected domain according to the deviation index ranking. Taking the seed points as the center, region growing is performed according to the light intensity distribution characteristics, microwave field characteristics, and cross-modal correlation characteristics until the preset growth termination condition is met;

[0174] An effective threshold is configured, and the coordinate proportion of the simultaneous presence of light intensity distribution anomalies and microwave field anomalies in each potential defect region is calculated. If the proportion is greater than the effective threshold, it is confirmed as an effective potential defect region, otherwise it is eliminated.

[0175] Embodiment 3

[0176] This embodiment introduces a testing device, which includes a hardware device for generating cross-band excitation signals, a detection unit for realizing multi-modal data acquisition, a calculation unit for performing data processing and feature analysis, and a control actuator for completing defect detection and parameter regulation. Each part works together to realize the joint test of the to-be-tested silicon carbide metasurface grating.

[0177] Specifically, a testing device includes:

[0178] The optical wave signal source selects a tunable semiconductor laser, which can output an optical wave excitation signal with controllable wavelength, power, and polarization state. By adjusting the cavity length and driving current of the laser, precise control of the optical wave parameters can be achieved;

[0179] The microwave signal source adopts a high-stability microwave signal generator, which can generate a microwave excitation signal with adjustable frequency, phase, and power. Its output parameters are dynamically adjusted by receiving control instructions through a digital interface.

[0180] The trigger controller, as the synchronization core, generates a high-precision synchronization clock signal, and simultaneously triggers the optical wave signal source and the microwave signal source to ensure that the output signals of the two are strictly synchronized in the time dimension, providing a benchmark for the time alignment of subsequent multi-modal data.

[0181] The signal synthesizer integrates a free-space coupling device that synthesizes optical and microwave excitation signals into a collaborative excitation signal. This device focuses the optical wave through a collimating lens and simultaneously uses a microstrip antenna to achieve the spatial radiation of the microwave signal, enabling the synthesized collaborative excitation signal to be uniformly applied to the silicon carbide metasurface grating under test, forming an interference pattern that includes the light intensity distribution and the microwave field intensity distribution.

[0182] The optoelectronic detection array uses a high-resolution camera with a microscopic objective lens to image the grating surface, collect the light intensity distribution data containing the markers, and extract the feature points of the markers through an image recognition algorithm, providing a key reference for subsequent coordinate mapping.

[0183] The microwave scanning probe system consists of a three-dimensional displacement stage and a near-field microwave probe. Under the precise control of the displacement stage, it scans the grating surface point by point, real-time collects the microwave field intensity data at each position and records the coordinate information, and keeps the distance between the probe and the grating surface constant during the scanning process to ensure the consistency of microwave signal acquisition.

[0184] The data processing unit preprocesses the obtained interference pattern data, realizes the time dimension alignment of the light intensity and microwave field intensity data through the time stamps of the synchronous clock signals, extracts the sub-pixel coordinates of the markers from the light intensity distribution data using edge detection and feature matching algorithms, and combines the position information of the microwave scanning. Through a geometric calibration algorithm, it maps the coordinates of the light intensity distribution data and the microwave scanning coordinates to the physical coordinate system of the grating uniformly, constructing a multi-dimensional data matrix containing multi-modal information. Then, it extracts the light intensity distribution features, microwave field features, and cross-modal correlation features from the multi-dimensional data matrix, generates a standardized defect feature vector, and constructs a multi-dimensional feature matrix for quality assessment.

[0185] The defect detection unit uses the multi-dimensional feature matrix to perform potential defect area division and grade assessment. Based on a defect-free standard silicon carbide metasurface grating sample, it establishes a feature reference model, generates a deviation matrix by calculating the distance between each coordinate point of the silicon carbide metasurface grating under test and the feature reference model, and marks the suspected defect points with excessive deviation; then, through multiple rounds of data statistics, it screens out the stable abnormal points, uses edge detection and morphological operations to aggregate the discrete abnormal points into closed candidate defect connected regions, and performs region growth based on the multi-modal feature similarity condition. By verifying the effectiveness of the region with the coordinate ratio of the light intensity and microwave field anomalies, it divides the real potential defect regions. For the identified potential defect regions, according to the defect degree, the area ratio of the region, and the spatial coincidence degree of the cross-modal anomalies of the defect feature vector, the defect grade is divided into mild, moderate, and severe. Among them, the defects in the key functional regions are automatically upgraded in grade to reflect the significant impact on the performance.

[0186] The parameter control actuator drives the parameter adjustment modules of the optical wave signal source and the microwave signal source according to the potential defect area information output by the defect detection unit, and iteratively optimizes the excitation signal parameters through the gradient descent algorithm. Taking the ratio of the edge gradient to the noise standard deviation of the interference pattern in the potential defect area as the objective function, the frequency difference, phase difference, and power of the optical wave and the microwave are dynamically adjusted to enhance the contrast of the defect characteristics and improve the sensitivity and accuracy of defect detection.

[0187] Working principle and its effects:

[0188] The working principle and effects of this application are as follows: By regulating the light source and the microwave source, a collaborative excitation signal containing optical waves and microwaves is generated and applied to the silicon carbide metasurface grating to be measured. The cross-band coupling effect of the optical wave and the microwave is used to form an interference pattern reflecting the grating structure and electromagnetic characteristics. This interference pattern contains both the light intensity distribution and the microwave field intensity distribution information, breaking through the limitation of the insufficient response of single-mode detection to nanoscale defects and realizing the comprehensive excitation of the cross-band electromagnetic response characteristics of the grating. By synchronously collecting the interference pattern data multiple times through multiple channels of the detector, the time dimensions of the light intensity and microwave field data are aligned by the time stamp of the synchronous clock signal, and the light intensity pixel coordinates and microwave scan coordinates are uniformly mapped to the grating physical coordinate system through the marker feature point extraction and geometric calibration algorithm to construct a multi-dimensional data matrix integrating multi-modal information, solving the problems of spatio-temporal asynchrony and coordinate mismatch of multi-source data in traditional detection and providing a high-precision data basis with spatio-temporal consistency for subsequent analysis.

[0189] Defect characteristics such as light intensity distribution, microwave field, and cross-modal correlation are extracted from the multi-dimensional data matrix, a multi-dimensional feature matrix is constructed, and a feature reference model of a defect-free standard silicon carbide metasurface grating sample is established. The quality is judged by calculating the deviation index between the grating to be measured and the feature reference model, and weak anomalies that are difficult to detect by a single mode can be accurately identified. For the grating determined to have defects, stable anomaly points are screened by collecting data in multiple rounds, candidate defect connected regions are constructed by edge detection, region growing is performed based on the condition of multi-modal feature similarity, and the effectiveness of the potential defect area is verified by combining the coordinate ratios of the light intensity and microwave field anomalies, effectively suppressing noise interference and excluding pseudo-potential defect areas, and realizing the accurate positioning of real defects.

[0190] When a potential defect area is recognized, with the goal of maximizing the ratio of the edge gradient of the defect area interference pattern to the standard deviation of the noise as the objective function, the excitation parameters such as the frequency difference and phase difference between the light wave and the microwave are dynamically adjusted through the gradient descent algorithm to generate a synergistic excitation signal with targeted enhancement, and the period, phase distribution, and defect feature contrast of the interference pattern are optimized in real time to form a "detection - feedback - regulation" closed loop. Based on the updated interference pattern data, the characteristic defect degrees and regional areas of the defect area and the non - defect area are calculated, and the defect levels are classified into mild, moderate, and severe to achieve a quantitative assessment of the defect impact degree.

[0191] Through multi - modal synergistic excitation, multi - source data fusion, dynamic parameter optimization, and hierarchical evaluation, the overall solution breaks through the bottlenecks of traditional detection technologies in terms of efficiency, accuracy, and environmental adaptability, can efficiently capture nanoscale defects and accurately evaluate their impact on the grating performance, significantly improves the detection reliability and classification scientificity of the silicon carbide metasurface grating to be measured in complex electromagnetic environments and extreme conditions, and provides an advanced technical means for quality control in the manufacturing of high - end optoelectronic devices.

[0192] The above - mentioned is only the preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above - mentioned embodiments. All technical solutions falling within the idea of the present invention belong to the protection scope of the present invention. It should be pointed out that for those of ordinary skill in the art, several improvements and refinements made without departing from the principle of the present invention should also be regarded as within the protection scope of the present invention.

Claims

1. A combined test system, characterized in that Including Obtain the initial parameters of the optical wave excitation signal and the microwave excitation signal, and regulate the light source and the microwave source to generate a cooperative excitation signal, which is used to be applied to the silicon carbide metasurface grating to be measured to form an interference pattern; Collect the interference pattern synchronously and multiple times through multiple channels by a detector to obtain multiple interference pattern data and perform preprocessing. The interference pattern data includes light intensity distribution data and microwave field intensity data; The preprocessing is used to align the interference pattern data in the time dimension, and extract feature points from the markers configured on the silicon carbide metasurface grating to be measured, so as to map and match the coordinates of the interference pattern data and construct a multi-dimensional data matrix; Extract defect features from the multi-dimensional data matrix to construct a multi-dimensional feature matrix, calculate the deviation index of the silicon carbide metasurface grating to be measured through the established feature reference model for quality determination, and screen stable abnormal points, perform edge detection to construct candidate defect connected regions, perform region growth and combine abnormal coupling verification to divide potential defect regions for the silicon carbide metasurface grating determined to have defects; If a potential defect region is identified, according to the information of the potential defect region, dynamically adjust the parameters of the optical wave excitation signal and the microwave excitation signal to update and adjust the cooperative excitation signal to regulate the interference pattern, obtain the updated interference pattern data and perform in-depth quality evaluation to divide the defect level.

2. The combined test system according to claim 1, wherein, The specific steps of updating and adjusting the cooperative excitation signal include: Obtain the structural parameter data and electromagnetic characteristic data of the silicon carbide metasurface grating to be measured to determine the initial excitation parameters of the optical wave excitation signal and the microwave excitation signal; Set the light source and the microwave source according to the determined initial excitation parameters to generate a cooperative excitation signal; Apply the cooperative excitation signal to the silicon carbide metasurface grating to be measured, collect the formed interference pattern data and perform preprocessing; Extract defect features from the preprocessed interference pattern data, and perform preliminary quality evaluation based on the extracted defect features to determine whether there are defects in the silicon carbide metasurface grating to be measured; If it is determined that there are defects, divide the potential defect region of the silicon carbide metasurface grating to be measured, record the position and range information of the potential defect region, and trigger the regulation of the cooperative excitation signal.

3. The combined test system according to claim 2, characterized in that, The specific steps of updating and adjusting the cooperative excitation signal further include: Take the ratio of the edge gradient to the noise standard deviation of the interference pattern data of the potential defect region as the objective function to regulate the excitation parameters of the optical wave excitation signal and the microwave excitation signal; Within the excitation parameter adjustment range, cooperatively regulate the excitation parameters through the gradient descent algorithm, specifically including: Based on the initial excitation parameters of the optical wave excitation signal and the microwave excitation signal, determine the initial frequency difference and phase difference, and obtain the objective function value of the potential defect region; Calculate the partial derivatives of the objective function with respect to the frequency difference and the phase difference, and update the frequency difference and the phase difference according to the gradient direction; Recalculate the frequency and phase of the optical wave excitation signal and the microwave excitation signal according to the updated frequency difference and phase difference, synthesize the updated optical wave excitation signal and the microwave excitation signal into a new cooperative excitation signal, apply it to the silicon carbide metasurface grating to be tested to form an updated interference pattern, and calculate the objective function value of the potential defect area; Configure the convergence threshold, calculate the change in the objective function value between two adjacent times, and if the change is less than the convergence threshold, stop updating and output the optimized excitation parameters; otherwise, continue iterative updating.

4. The combined test system according to claim 1, characterized in that, The specific steps of constructing the multidimensional data matrix include: Using the timestamp of the synchronous clock signal, the light intensity distribution data collected by the photoelectric detector and the microwave field intensity data collected by the microwave detector are aligned in the time dimension; After removing the noise of the light intensity distribution data and enhancing the contrast, a scale space is constructed to detect the extreme points in the enhanced light intensity distribution data, determine the position of the feature points of the silicon carbide metasurface grating marker to be tested, calculate the scale and direction information of each feature point and generate a feature descriptor; The threshold segmentation method is used for microwave field intensity data. The segmentation threshold is set to separate the marker area. The outline of the marker area is optimized through morphological processing. The geometric center of the marker area is calculated as the feature point, and the coordinate position of the feature point in the microwave field intensity data is recorded. Using a geometric calibration algorithm, according to the feature descriptors of the feature points in the light intensity distribution data and the coordinate positions of the feature points in the microwave field intensity data, the coordinates of the light intensity distribution data are mapped to the physical coordinate positions of the silicon carbide metasurface grating to be measured, and the coordinates of the microwave field intensity data are matched with the coordinates of the light intensity distribution data; Selecting a fixed position on the silicon carbide metasurface grating to be measured as the origin of the space coordinate system and determining the direction of the coordinate axis, and converting the coordinates of the light intensity distribution data and the microwave field intensity data into coordinates under the space coordinate system; The light intensity distribution data and the microwave field intensity data are integrated according to the spatial coordinate system to construct a multidimensional data matrix, wherein the elements of the multidimensional data matrix correspond to the light intensity distribution data and the microwave field intensity data at corresponding positions in the silicon carbide metasurface grating to be measured.

5. The combined test system according to claim 1, wherein The specific steps of extracting defect features include: Obtaining a preprocessed multidimensional data matrix, and extracting corresponding defect features for a local area where each coordinate position in the multidimensional data matrix is ​​located, wherein the defect features include light intensity distribution features, microwave field features, and cross-modal correlation features; The light intensity distribution feature is extracted in the following manner: adjacent areas are demarcated with the coordinate position of the multidimensional data matrix as the center to calculate the light intensity contrast index; the actual fringe period corresponding to the coordinate position of the multidimensional data matrix is ​​compared with the configured theoretical period, and the deviation rate is calculated as the fringe spacing uniformity index; the gradient amplitude of the edge position of the multidimensional data matrix is ​​calculated, and the standard deviation of the gradient amplitude of the adjacent edge position is calculated as the edge gradient consistency index; The extraction method of the microwave field characteristics is as follows: Divide the local area centered on the coordinate position in the multi-dimensional data matrix into analysis units, and calculate the variance of the microwave phase values within the local area as the microwave phase stability index; Compare the microwave field strength in the local area with the standard microwave field strength, and calculate the ratio of the microwave field strength to the standard microwave field strength as the field strength attenuation rate characteristic index. The extraction method of the cross-modal correlation characteristics is as follows: For the light intensity distribution data and microwave field strength data corresponding to the coordinate position in the multi-dimensional data matrix, calculate the light intensity gradient and microwave phase gradient respectively, and calculate the correlation index of the light intensity gradient and microwave phase gradient through the correlation coefficient. Perform dimensionless processing on the extracted defect characteristics, so that each coordinate position in the multi-dimensional data matrix generates a set of defect feature vectors to construct a multi-dimensional feature matrix.

6. The combined test system according to claim 2, wherein The specific steps for the preliminary quality assessment include: Establish a standard feature set, and the standard feature set includes the multi-dimensional feature matrix of the defect-free standard silicon carbide metasurface grating sample. Statistically calculate the mean vector and covariance matrix of each defect feature in the multi-dimensional feature matrix in the standard feature set, and construct a distribution model of the multi-dimensional feature matrix in the standard feature set as the feature reference model. Obtain the interference pattern data of the silicon carbide metasurface grating to be tested, construct a multi-dimensional data matrix and a multi-dimensional feature matrix, and calculate the distance between the defect feature vector at each coordinate position in the multi-dimensional feature matrix and the feature reference model to generate a deviation matrix. Perform global averaging on the deviation matrix to obtain the deviation index of each interference pattern data; Configure a distance threshold, compare the deviation index with the distance threshold. If there is a deviation index greater than the distance threshold, it is determined that the silicon carbide metasurface grating to be tested has defects, otherwise it is determined to be qualified.

7. The combined test system according to claim 6, wherein The specific steps for the preliminary quality assessment also include: If it is determined that the silicon carbide metasurface grating to be tested has defects, then based on the deviation matrix and the distance threshold, for the multi-dimensional data matrix of the interference pattern data collected multiple times, mark the coordinate points with deviation indexes greater than the distance threshold as suspected defect points to form a binary abnormal point matrix, and statistically calculate the abnormal occurrence frequency of the same coordinate point, and screen out the stable abnormal points with abnormal occurrence frequency greater than the preset frequency threshold. Perform edge detection on the stable abnormal points to extract continuous edge contours, and form candidate defect connected regions through morphological dilation operations. Select seed points in the candidate defect connected regions according to the deviation index ranking. Centered on the seed points, perform region growing according to the light intensity distribution characteristics, microwave field characteristics and cross-modal correlation characteristics until the preset growth termination condition is met. Configure an effective threshold, calculate the coordinate proportion of the light intensity distribution anomaly and microwave field anomaly existing simultaneously in each potential defect region. If the proportion is greater than the effective threshold, it is confirmed as an effective potential defect region, otherwise it is eliminated.

8. The combined test system according to claim 1, characterized in that, The specific steps for the in-depth quality assessment include: Obtain the multi-dimensional data matrix and its multi-dimensional feature matrix of each updated interference pattern data, map the coordinates of the potential defect regions to the updated multi-dimensional data matrix, and generate a potential defect region mask. Based on the potential defect area mask, calculate the mean and standard deviation of the defect feature vectors in the potential defect area and the non-potential defect area respectively, and calculate the difference degree of each defect feature vector; According to the mean and standard deviation of the defect feature vectors in the potential defect area and the non-potential defect area obtained by calculation, calculate the defect degree of the defect feature vectors in each potential defect area; According to the defect degree of each defect feature vector in the potential defect area and the area of the potential defect area, divide the defect level into mild defect level, moderate defect level, and severe defect level.

9. A method for detecting a silicon carbide metasurface grating in a joint test, which is implemented based on the joint test system described in any one of claims 1-8, and is characterized in that, It includes the following steps: Step S1: Obtain the initial parameters of the optical wave excitation signal and the microwave excitation signal, regulate the light source and the microwave source to generate a collaborative excitation signal, and apply the collaborative excitation signal to the silicon carbide metasurface grating to be measured to form an interference pattern including the light intensity distribution and the microwave field intensity distribution; Step S2: Multichannel synchronously collect the interference pattern multiple times through a detector to obtain multiple interference pattern data, and the interference pattern data includes light intensity distribution data and microwave field intensity data; Align the interference pattern data in the time dimension, and by extracting the feature points of the markers on the silicon carbide metasurface grating to be measured, map and match the coordinates of the interference pattern data to construct a multi-dimensional data matrix; Step S3: Extract defect features from the multi-dimensional data matrix to construct a multi-dimensional feature matrix, establish a feature reference model based on the multi-dimensional feature matrix of the defect-free standard silicon carbide metasurface grating sample, and calculate the deviation index of the silicon carbide metasurface grating to be measured for quality determination; If it is determined that there are defects, then screen stable abnormal points in multiple rounds, perform edge detection to construct candidate defect connected regions, perform region growing based on the similarity conditions of multi-modal features, and combine the coupling verification of the light intensity and microwave field anomalies to divide the potential defect area; Step S4: If the potential defect area is identified, dynamically adjust the parameters of the optical wave excitation signal and the microwave excitation signal according to the information of the potential defect area, update the collaborative excitation signal and obtain the updated interference pattern data; based on the defect degrees of the defect area and the non-defect area in the updated multi-dimensional feature matrix and the area of the potential defect area, divide the defect level.

10. A test device for implementing the combined test system according to any one of claims 1-8, characterized in that, It includes: An optical wave signal source for generating optical wave excitation signals with different wavelengths, powers, and polarization states; A microwave signal source for generating microwave excitation signals with different frequencies, phases, and powers; A trigger controller for generating a synchronous clock signal to achieve time synchronization between the optical wave signal source and the microwave signal source; A signal synthesizer for synthesizing the optical wave excitation signal and the microwave excitation signal into a collaborative excitation signal; An optoelectronic detection array for collecting light intensity distribution data and identifying marker feature points; A microwave scanning probe system for collecting microwave field intensity data and recording scanning coordinates; A data processing unit for executing marker feature extraction and coordinate mapping algorithms, constructing a multi-dimensional data matrix, extracting light intensity distribution features, microwave field features, and cross-modal correlation features, generating defect feature vectors, and constructing a multi-dimensional feature matrix; A defect detection unit for performing potential defect area division and dividing the defect level based on the characteristic defect degree, area ratio, and cross-modal coincidence degree; A parameter control actuator is used to dynamically adjust excitation parameters according to potential defect area information and optimize the excitation parameters through the gradient descent algorithm.

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