LED Ring Light Source Testing Method, Device, Equipment and Storage Medium

Abstract

The present invention provides a method, device, equipment and storage medium for testing an LED ring light source. The method includes: performing calibration initialization processing on the LED ring light source mounted on an angle-adjustable bracket and a standard silicon wafer simulation board to obtain a reference test sequence; controlling the LED ring light source to irradiate the standard silicon wafer simulation board according to the reference test sequence, and performing multi-region light field acquisition at different speed gears to obtain multi-dimensional test data; performing characteristic analysis on the multi-dimensional test data to obtain light field spatial characteristic data, light intensity fluctuation frequency characteristic data and defect detection rate evaluation results, and further constructing a light field distortion model; calculating the light intensity uniformity deviation value and the defect detection ability score according to the light field distortion model, and generating a test evaluation report. By multi-region acquisition and data analysis, the method constructs a light field distortion model, which can accurately evaluate the uniformity of the light intensity distribution and the defect detection ability, and improve the effect of the LED ring light source in the high-precision detection of silicon wafers.

Description

Technical Field

[0001] The present invention relates to the field of semiconductor technology, and particularly to a method, device, equipment and storage medium for testing an LED ring light source. Background Art

[0002] In the field of semiconductor manufacturing, as a detection light source device, the LED ring light source is widely used in the defect detection and quality assessment of silicon wafers. Due to its advantages such as high efficiency, long life, energy conservation and environmental protection, the LED ring light source gradually replaces the traditional light source device. However, in practical applications, the light intensity distribution uniformity and defect detection ability of the LED ring light source are the key factors affecting the detection accuracy and reliability.

[0003] In the prior art, simple light intensity measurement methods and empirical analysis means are usually used to evaluate the performance of the LED ring light source. These methods usually only focus on the light intensity data in a single dimension. At the same time, when evaluating the light intensity distribution and defect detection ability in the prior art, there is often a lack of a systematic multi-region acquisition and analysis mechanism, resulting in an inaccurate evaluation of the distortion degree of the light intensity distribution. Especially in the semiconductor field, the high-precision detection of silicon wafers has higher requirements for the uniformity and stability of the light source. The problems of uneven light intensity and insufficient defect detection ability in the prior art are particularly prominent. Such defects limit the effect and reliability of the LED ring light source in high-precision detection applications in the semiconductor field. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing test of the LED ring light source lacks a systematic multi-region acquisition and analysis mechanism, resulting in an inaccurate evaluation of the distortion degree of the light intensity distribution;

[0005] The first aspect of the present invention provides a method for testing an LED ring light source, and the method for testing the LED ring light source includes:

[0006] Performing calibration initialization processing on the LED ring light source and the standard silicon wafer simulation board installed on the angle-adjustable bracket, and obtaining a reference test sequence of the LED ring light source;

[0007] Controlling the LED ring light source to irradiate the standard silicon wafer simulation board according to the reference test sequence, and performing multi-region acquisition on the light field of the LED ring light source irradiated on the standard silicon wafer simulation board at different speed gears to obtain multi-dimensional test data;

[0008] Perform characteristic analysis and processing on the multi-dimensional test data to obtain light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results, and construct a light field distortion model representing the degree of light intensity distribution distortion based on the light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results;

[0009] Calculate the light intensity uniformity deviation value and defect detection ability score of the LED ring light source according to the light field distortion model, and generate a test evaluation report for the LED ring light source based on the light intensity uniformity deviation value and defect detection ability score.

[0010] Optionally, in the first implementation manner of the first aspect of the present invention, the calibrating and initializing the LED ring light source mounted on the angle-adjustable bracket and the standard silicon wafer simulation board, and obtaining the reference test sequence of the LED ring light source includes:

[0011] Measure the positional relationship between the LED ring light source and the standard silicon wafer simulation board to obtain position parameter data, and set the motion parameters of the standard silicon wafer simulation board according to the position parameter data;

[0012] Configure the illumination parameters of the LED ring light source according to the motion parameters to obtain a parameter combination of illumination angle, working distance, and light intensity level;

[0013] Perform timing matching processing on the motion parameters and the parameter combination to obtain a motion-illumination linkage control scheme, and generate a reference test sequence according to the motion-illumination linkage control scheme.

[0014] Optionally, in the second implementation manner of the first aspect of the present invention, the irradiating the standard silicon wafer simulation board with the LED ring light source according to the reference test sequence, and multi-region collecting the light field of the LED ring light source irradiated on the standard silicon wafer simulation board at different speed gears to obtain multi-dimensional test data includes:

[0015] Determine the light source illumination control instruction of the LED ring light source according to the reference test sequence, and perform speed gear switching control on the standard silicon wafer simulation board;

[0016] Under the light source illumination control instruction and different speed gears of the standard silicon wafer simulation board, collect the light field of the inner ring, middle ring, and outer ring of the LED ring light source through a photodetector array to obtain light intensity distribution data;

[0017] An industrial camera is used to collect and process images of standard defects on the surface of the standard silicon wafer simulation board to obtain defect detection image data, and a spectral analyzer is used to perform spectral collection and processing on the three regions of the inner ring, middle ring, and outer ring according to the light intensity distribution data to obtain spectral characteristic data;

[0018] Data integration processing is performed on the light intensity distribution data, defect detection image data, and spectral characteristic data to obtain multi-dimensional test data.

[0019] Optionally, in the third implementation manner of the first aspect of the present invention, the characteristic analysis and processing of the multi-dimensional test data to obtain the light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation result include:

[0020] Perform Fourier transform processing on the light intensity distribution data to obtain a light intensity distribution spectrum feature vector, and discretely sample the light intensity fluctuation frequency domain distribution of the LED ring light source according to the light intensity distribution spectrum feature vector to obtain light intensity fluctuation frequency characteristic data;

[0021] Perform spatial mapping processing on the light intensity distribution of the three regions of the inner ring, middle ring, and outer ring according to the spectral characteristic data to obtain a light field spatial distribution characteristic matrix, and perform singular value decomposition operation on the light field spatial distribution characteristic matrix to obtain light field spatial characteristic data;

[0022] Perform edge detection and morphological processing on the defect detection image data to obtain a defect feature vector, and perform identification and classification processing on the standard defects according to the defect feature vector to obtain a defect detection rate evaluation result.

[0023] Optionally, in the fourth implementation manner of the first aspect of the present invention, the performing spatial mapping processing on the light intensity distribution of the three regions of the inner ring, middle ring, and outer ring according to the spectral characteristic data to obtain a light field spatial distribution characteristic matrix, and performing singular value decomposition operation on the light field spatial distribution characteristic matrix to obtain light field spatial characteristic data includes:

[0024] Perform polar coordinate transformation processing on the spectral characteristic data to obtain angular light intensity distribution data, and perform circular uniform division on the three regions of the inner ring, middle ring, and outer ring according to the angular light intensity distribution data to obtain annular region division parameters;

[0025] Perform interpolation fitting processing on the angular light intensity distribution data according to the annular region division parameters to obtain a spatial sampling point sequence, and perform radial normalization processing on the spatial sampling point sequence to obtain normalized light intensity distribution data;

[0026] Perform matrix reconstruction operation on the standardized light intensity distribution data to obtain a light field spatial distribution feature matrix, and perform covariance calculation processing on the light field spatial distribution feature matrix to obtain a spatial correlation matrix;

[0027] Perform eigenvalue calculation processing according to the spatial correlation matrix to obtain a singular value sequence, and perform descending order arrangement operation on the singular value sequence to obtain main eigencomponents;

[0028] Perform spatial reprojection processing on the main eigencomponents to obtain reconstructed light field distribution data, and perform eigenvector orthogonalization processing according to the reconstructed light field distribution data to obtain light field spatial characteristic data.

[0029] Optionally, in the fifth implementation manner of the first aspect of the present invention, the construction of a light field distortion model for characterizing the degree of light intensity distribution distortion based on the light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation result includes:

[0030] Perform standardization processing on the light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation result to obtain a normalized eigenvector;

[0031] Construct a three-dimensional feature space according to the normalized eigenvector, and perform principal component analysis operation on the three-dimensional feature space to obtain main eigencomponents;

[0032] Perform feature weight calculation processing on the main eigencomponents to obtain contribution coefficients of each dimension feature, and perform linear combination operation on the main eigencomponents according to the contribution coefficients to obtain light field distortion characteristic parameters;

[0033] Establish a nonlinear regression equation according to the light field distortion characteristic parameters, and perform iterative solution processing on the coefficients of the nonlinear regression equation by the least squares method to obtain a light field distortion model for characterizing the degree of light intensity distribution distortion.

[0034] Optionally, in the sixth implementation manner of the first aspect of the present invention, the calculation of the light intensity uniformity deviation value and the defect detection ability score of the LED ring light source according to the light field distortion model, and the generation of a test evaluation report for the LED ring light source according to the light intensity uniformity deviation value and the defect detection ability score include:

[0035] Perform numerical calculation processing on the light field distortion model to obtain light intensity distortion coefficients of each region, and perform statistical distribution operation according to the light intensity distortion coefficients to obtain a light intensity uniformity deviation curve;

[0036] Perform weighted accumulation processing on the light intensity distortion coefficients of the respective regions according to the light intensity uniformity deviation curve to obtain a comprehensive distortion index, and perform normalization operation on the comprehensive distortion index to obtain the light intensity uniformity deviation value;

[0037] Perform classification and statistical processing on the defect detection rate evaluation results to obtain the detection probability distribution of various types of defects, and perform error analysis operations according to the detection probability distribution to obtain the detection confidence data;

[0038] Perform weight allocation processing on the detection probability distribution of various types of defects according to the detection confidence data to obtain a detection ability weight matrix, and perform feature fusion operations on the detection ability weight matrix to obtain a defect detection ability score;

[0039] Perform multi-dimensional index integration processing on the light intensity uniformity deviation value and the defect detection ability score to obtain a comprehensive performance evaluation index, and generate a test evaluation report according to the comprehensive performance evaluation index.

[0040] The second aspect of the present invention provides an LED ring light source test device, and the LED ring light source test device includes:

[0041] A preprocessing module for performing calibration initialization processing on an LED ring light source mounted on an angle-adjustable bracket and a standard silicon wafer simulation board, and obtaining a reference test sequence of the LED ring light source;

[0042] A test module for controlling the LED ring light source to irradiate the standard silicon wafer simulation board according to the reference test sequence, and performing multi-region acquisition on the light field irradiated by the LED ring light source on the standard silicon wafer simulation board at different speed gears to obtain multi-dimensional test data;

[0043] An analysis module for performing characteristic analysis processing on the multi-dimensional test data to obtain light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results, and constructing a light field distortion model characterizing the degree of light intensity distribution distortion based on the light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results;

[0044] An evaluation module for calculating the light intensity uniformity deviation value and the defect detection ability score of the LED ring light source according to the light field distortion model, and generating a test evaluation report of the LED ring light source according to the light intensity uniformity deviation value and the defect detection ability score.

[0045] In a third aspect of the present invention, a testing device for an LED ring light source is provided, including: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected through a circuit; the at least one processor calls the instructions in the memory to cause the LED ring light source testing device to execute the steps of the above-mentioned LED ring light source testing method.

[0046] In a fourth aspect of the present invention, a computer-readable storage medium is provided, in which instructions are stored, and when it runs on a computer, it causes the computer to execute the steps of the above-mentioned LED ring light source testing method.

[0047] For the above-mentioned LED ring light source testing method, device, equipment and storage medium, through the calibration and initialization process of the LED ring light source installed on the angle-adjustable bracket and the standard silicon wafer simulation board, a reference test sequence is obtained; according to the reference test sequence, the LED ring light source is controlled to irradiate the standard silicon wafer simulation board, and multi-region light field acquisition is carried out at different speed gears to obtain multi-dimensional test data; the multi-dimensional test data is analyzed for characteristics to obtain light field spatial characteristic data, light intensity fluctuation frequency characteristic data and defect detection rate evaluation results, and then a light field distortion model is constructed; according to the light field distortion model, the light intensity uniformity deviation value and the defect detection ability score are calculated, and a test evaluation report is generated. This method can accurately evaluate the uniformity of light intensity distribution and the defect detection ability by multi-region acquisition and data analysis, and construct a light field distortion model, improving the effect of the LED ring light source in the high-precision detection of silicon wafers.

[0048] Other features and advantages of the present invention will be described in the subsequent description, and part of them will become obvious from the description, or be understood by implementing the present invention. The objectives and other advantages of the present invention are realized and obtained by the structures specifically pointed out in the description, claims and drawings.

[0049] To make the above-mentioned objectives, features and advantages of the present invention more obvious and understandable, the following specific preferred embodiments are given, and in conjunction with the accompanying drawings, the detailed description is as follows. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a schematic diagram of the first embodiment of the LED ring light source testing method in the embodiment of the present invention;

[0051] Figure 2 It is a schematic diagram of an embodiment of the LED ring light source testing device in the embodiment of the present invention;

[0052] Figure 3 It is a schematic diagram of an embodiment of the LED ring light source testing equipment in the embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Apparently, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0054] As used in the embodiments of the present invention, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes other steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products or devices.

[0055] For ease of understanding of this embodiment, a method for testing an LED ring light source disclosed in the embodiments of the present invention will be introduced in detail first. As Figure 1 shown, this method includes the following steps:

[0056] 101. Perform calibration initialization processing on the LED ring light source mounted on the angle-adjustable bracket and the standard silicon wafer simulation board, and obtain the reference test sequence of the LED ring light source;

[0057] In an embodiment of the present invention, the performing calibration initialization processing on the LED ring light source mounted on the angle-adjustable bracket and the standard silicon wafer simulation board, and obtaining the reference test sequence of the LED ring light source includes: measuring the positional relationship between the LED ring light source and the standard silicon wafer simulation board to obtain position parameter data, and setting the motion parameters of the standard silicon wafer simulation board according to the position parameter data; configuring the illumination parameters of the LED ring light source according to the motion parameters to obtain a parameter combination of illumination angle, working distance and light intensity level; performing timing matching processing on the motion parameters and the parameter combination to obtain a motion-illumination linkage control scheme, and generating a reference test sequence according to the motion-illumination linkage control scheme.

[0058] Specifically, due to the special annular structural characteristics of the LED annular light source, a laser rangefinder is required to perform three-dimensional coordinate positioning of the characteristic points on the inner ring, middle ring, and outer ring of the annular light source during the measurement process. The laser rangefinder is installed on a three-axis sliding table, and the rangefinder is driven by a stepping motor to move in the X, Y, and Z directions to perform scanning measurements on multiple characteristic points of the LED annular light source. The measurement data is collected in real time through a data acquisition module and transmitted to the control system. After coordinate transformation processing, the spatial position data of the LED annular light source is obtained. At the same time, multiple reference marking points are arranged on the surface of the standard silicon wafer simulation board, and an industrial camera is used to collect images of these marking points. The position coordinates of the marking points are extracted through an image processing algorithm to establish a spatial coordinate system on the surface of the simulation board. Through spatial coordinate transformation, the coordinate systems of the LED annular light source and the standard silicon wafer simulation board are unified to obtain the relative position relationship parameters between the two, including position parameter data such as relative distance, tilt angle, and horizontal offset. Based on the position parameter data, a kinematic inverse solution algorithm is used to calculate the translation speed, acceleration of the standard silicon wafer simulation board in the XY plane, and the lifting parameters in the Z direction, so as to set the motion parameters of the standard silicon wafer simulation board.

[0059] After obtaining the motion parameters, it is necessary to configure the illumination parameters of the LED annular light source. First, based on the relative position information in the motion parameters, combined with the light intensity attenuation model, calculate the actual irradiation distance from each light-emitting unit of the LED annular light source to the surface of the standard silicon wafer simulation board, and determine the working distance parameter according to the irradiation distance. Then, through the angle adjustment mechanism, control the pitch angle and horizontal rotation angle of the LED annular light source so that the illumination area can accurately cover the detection area on the surface of the simulation board, thereby obtaining the optimal illumination angle. At the same time, considering the requirements for light intensity uniformity in different regions, the drive currents of the inner ring, middle ring, and outer ring LED beads are respectively controlled through a current modulation circuit to adjust the light intensity of each region, establish a light intensity hierarchical control strategy, and finally obtain an illumination parameter combination including illumination angle, working distance, and light intensity level. These parameter combinations are uniformly managed through the parameter configuration module of the control system to ensure that the LED annular light source can maintain stable illumination conditions during the detection process.

[0060] When performing timing matching processing on the combination of motion parameters and lighting parameters, first establish a parameter synchronization mechanism based on the time axis. Discretize the motion trajectory of the standard silicon wafer simulation board into key points in the time series, and each key point corresponds to a set of position coordinates and speed parameters. Then divide the lighting parameters of the LED ring light source according to the same time series to ensure that the motion state and lighting state can be accurately corresponding at each time point. Generate a unified clock signal through the timing controller to drive the motion control module and the lighting control module to perform cooperative operations according to the predetermined timing, forming a complete motion-lighting linkage control scheme. Based on this control scheme, further convert the change processes of the motion parameters and lighting parameters into an instruction sequence executable by the device, including motion control instructions, light intensity adjustment instructions, angle adjustment instructions, etc. These instructions are arranged in chronological order to form a benchmark test sequence for guiding the subsequent test process. Through this timing-based parameter matching method, it is ensured that the lighting characteristics of the LED ring light source and the motion characteristics of the standard silicon wafer simulation board can achieve precise synchronization.

[0061] 102. Control the LED ring light source to irradiate the standard silicon wafer simulation board according to the benchmark test sequence, and perform multi-region acquisition of the light field of the LED ring light source irradiated on the standard silicon wafer simulation board at different speed gears to obtain multi-dimensional test data;

[0062] In an embodiment of the present invention, the controlling the LED ring light source to irradiate the standard silicon wafer simulation board according to the benchmark test sequence, and performing multi-region acquisition of the light field of the LED ring light source irradiated on the standard silicon wafer simulation board at different speed gears to obtain multi-dimensional test data includes: determining the light source lighting control instruction of the LED ring light source according to the benchmark test sequence, and performing speed gear switching control on the standard silicon wafer simulation board; under the light source lighting control instruction and different speed gears of the standard silicon wafer simulation board, perform light field acquisition on three regions, namely the inner ring, middle ring, and outer ring of the LED ring light source through a photodetector array to obtain light intensity distribution data; perform image acquisition and processing on the standard defects on the surface of the standard silicon wafer simulation board through an industrial camera to obtain defect detection image data, and perform spectral acquisition and processing on the three regions of the inner ring, middle ring, and outer ring according to the light intensity distribution data through a spectral analyzer to obtain spectral characteristic data; perform data integration processing on the light intensity distribution data, defect detection image data, and spectral characteristic data to obtain multi-dimensional test data.

[0063] Specifically, the benchmark test sequence contains the complete timing information of the lighting parameters of the LED ring light source and the motion parameters of the standard silicon wafer simulation board. The control system first parses the benchmark test sequence to extract the lighting control instructions corresponding to each time point. These instructions include the drive current values, lighting angle adjustment values, and working distance setting values of the LED beads in the inner, middle, and outer rings. The control system converts these parameters into specific PWM waveforms and digital control signals through a digital signal processor, and precisely controls the luminous intensity and irradiation angle of the LED ring light source through a drive circuit. At the same time, the motion control parameters are extracted from the benchmark test sequence, and the motion speed of the standard silicon wafer simulation board is hierarchically set through a motion controller to establish a speed gear system including low speed, medium speed, and high speed. Each gear corresponds to specific linear speed and acceleration parameters. The motor speed is controlled through a servo drive system to achieve the smooth motion of the standard silicon wafer simulation board at different speed gears, ensuring that the light field characteristic data under different motion states can be obtained during the test.

[0064] Specifically, after determining the light source lighting control instructions and the motion speed gears, a photodetector array is used to collect the light field generated by the LED ring light source in multiple regions. The photodetector array consists of multiple high-sensitivity photodiodes, which are evenly distributed along the ring and cover the three regions of the inner, middle, and outer rings. Each detector converts the optical signal into an electrical signal through a preamplifier circuit, performs amplification and filtering processing through a signal conditioning circuit, and then realizes digital sampling through a high-speed AD converter. At each speed gear, the control system synchronously collects the light intensity signals of each region according to the preset sampling frequency to obtain the complete data of the light intensity changing with time and spatial position. These data are processed through normalization and spatial reconstruction to form the light intensity distribution data representing the spatial light intensity distribution characteristics of the LED ring light source, including the time-domain fluctuation characteristics and spatial distribution characteristics of the light intensity.

[0065] Specifically, while collecting the light field, an industrial camera is used to capture images of standard defects on the surface of a standard silicon wafer simulation board. The industrial camera uses a high-resolution CMOS sensor and is equipped with a telecentric lens optical system, and is controlled by a trigger signal to synchronize with the movement of the standard silicon wafer simulation board. The images captured by the camera undergo digital image processing, including preprocessing operations such as image enhancement, noise suppression, and geometric correction, and then an edge detection algorithm is used to extract the contour features of the defect area. For different types of standard defects, morphological analysis methods are used to extract characteristic parameters such as the area, perimeter, and gray-scale distribution of the defects, forming a complete description of the defect characteristics. At the same time, a spectral analyzer is used to measure the spectral characteristics of the LED ring light source. The spectral analyzer collects spectral information from three regions, namely the inner ring, the middle ring, and the outer ring, through an optical fiber probe to obtain spectral parameters such as the wavelength distribution, color temperature, and color rendering index of each region. After wavelength calibration and intensity calibration of the spectral data, combined with the previously obtained light intensity distribution data, spectral characteristic data representing the light-emitting characteristics of the LED ring light source is formed.

[0066] Specifically, in order to comprehensively evaluate the performance of the LED ring light source, it is necessary to perform systematic data integration processing on the obtained light intensity distribution data, defect detection image data, and spectral characteristic data. First, a unified data format standard is established to convert different types of data into a standardized data structure. Through a data synchronization mechanism, it is ensured that data from different sources are aligned in the time dimension and the correlation relationship between the data is established. A data fusion algorithm is used to perform feature-level fusion on multi-source data to extract key indicators reflecting the performance characteristics of the LED ring light source. For the light intensity distribution data, indicators of light intensity uniformity and stability are extracted; for the defect detection image data, indicators of defect detection sensitivity and accuracy are extracted; for the spectral characteristic data, indicators of spectral consistency and color reducibility are extracted. These indicators are organized in the form of feature vectors to form multi-dimensional test data describing the overall performance of the LED ring light source, providing comprehensive data support for subsequent performance evaluation.

[0067] 103. Perform characteristic analysis processing on the multi-dimensional test data to obtain light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results, and construct a light field distortion model representing the degree of light intensity distribution distortion based on the light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results;

[0068] In an embodiment of the present invention, the characteristic analysis and processing of the multi-dimensional test data to obtain the light field spatial characteristic data, the light intensity fluctuation frequency characteristic data, and the defect detection rate evaluation result include: performing Fourier transform processing on the light intensity distribution data to obtain a light intensity distribution spectrum feature vector, and discretely sampling the light intensity fluctuation frequency domain distribution of the LED ring light source according to the light intensity distribution spectrum feature vector to obtain the light intensity fluctuation frequency characteristic data; performing spatial mapping processing on the light intensity distributions of the inner ring, the middle ring, and the outer ring three regions according to the spectral characteristic data to obtain a light field spatial distribution feature matrix, and performing singular value decomposition operation on the light field spatial distribution feature matrix to obtain the light field spatial characteristic data; performing edge detection and morphological processing on the defect detection image data to obtain a defect feature vector, and performing identification and classification processing on the standard defects according to the defect feature vector to obtain the defect detection rate evaluation result.

[0069] Specifically, performing Fourier transform processing on the light intensity distribution data is an important means for analyzing the light intensity fluctuation characteristics of the LED ring light source. First, the collected time-domain light intensity data is reconstructed into a discrete time series according to the sampling time series, and the data is preprocessed by a window function to reduce the spectrum leakage phenomenon. The fast Fourier transform algorithm is used to transform the processed time-domain data, converting the time-domain information to the frequency-domain space to obtain the spectrum distribution of the light intensity fluctuation. The amplitude normalization and phase correction are performed on the spectrum data to extract the amplitude and phase information of the main frequency components, forming a vector describing the light intensity distribution spectrum characteristics. Based on this spectrum feature vector, the sampling interval is determined by the frequency resolution criterion, and the frequency domain distribution is discretely sampled to obtain the spectrum characteristics at discrete frequency points. By statistically analyzing the sampled data, calculating the spectrum energy distribution, the main frequency component and its modulation depth, the frequency characteristic data characterizing the light intensity fluctuation characteristics of the LED ring light source is finally obtained.

[0070] Specifically, the spectral characteristic data contains the spectral information of each region of the LED ring light source. It is necessary to associate the spectral characteristics with the light intensity distribution through spatial mapping. First, a cylindrical coordinate system is established, and the spatial position parameters of the inner ring, middle ring, and outer ring regions are converted into normalized coordinates. The spectral data of each region is integrated over wavelength to calculate the energy distribution in each wavelength range, and a corresponding relationship is established with the spatial coordinates. Through an interpolation algorithm, the discrete sampling points are spatially reconstructed to obtain a continuous light intensity distribution function. The reconstructed light intensity distribution data is organized in matrix form, where the rows of the matrix represent spatial positions and the columns represent the light intensity values at different wavelengths, forming a spatial distribution characteristic matrix of the light field. The singular value decomposition is performed on this characteristic matrix, which is decomposed into three components representing the spatial basis vectors, singular values, and eigenvectors. By analyzing the distribution characteristics of the singular values, the main spatial characteristic modes are extracted, and the light field distribution is reconstructed in combination with the eigenvectors. Finally, the spatial characteristic data of the light field describing the spatial distribution characteristics of the LED ring light source is obtained.

[0071] Specifically, the processing of the defect detection image data first requires edge detection and morphological analysis. The gradient of the image is calculated through the Sobel operator or Canny operator to extract the edge information in the image and obtain the contour characteristics of the defect area. The adaptive threshold segmentation method is used to binarize the edge image to separate the defect area from the background. Then, morphological operations, including basic operations such as dilation, erosion, opening, and closing, are used to process the binary image to eliminate noise and improve the defect contour. Based on the processed image, morphological characteristic parameters such as the area, perimeter, roundness, and rectangularity of the defect area are calculated, and at the same time, the gray-scale statistical characteristics of the defect area are extracted, including texture characteristics such as the average gray scale, gray-scale variance, and gray-level co-occurrence matrix. These characteristic parameters form a vector describing the defect characteristics.

[0072] Specifically, based on the obtained defect characteristic vector, it is necessary to identify and classify the standard defects. First, a standard defect sample library is established, which contains characteristic templates of different types of defects, such as typical defect types like scratches, stains, and bubbles. Pattern recognition algorithms, such as support vector machines or neural network classifiers, are used to classify and discriminate the characteristic vectors of the defects to be measured to determine the type of the defects. By calculating the similarity between the characteristic vector and the standard template, the credibility of the defect recognition is evaluated. For each type of defect, the ratio of the detected number to the actual number of defects is statistically calculated, and the detection rate index under different working conditions is calculated. The detection rate data is analyzed in association with the working parameters to establish a quantitative evaluation system for the defect detection ability. Finally, the evaluation results of the defect detection rate characterizing the detection performance of the LED ring light source are obtained. These evaluation results include statistical indicators such as the detection probability, missed detection rate, and false detection rate of different types of defects, providing an important basis for the performance evaluation of the LED ring light source.

[0073] Further, the light intensity distribution in the three regions of the inner ring, middle ring, and outer ring is subjected to spatial mapping processing according to the spectral characteristic data to obtain a light field spatial distribution characteristic matrix, and the singular value decomposition operation is performed on the light field spatial distribution characteristic matrix to obtain light field spatial characteristic data, including: performing polar coordinate transformation processing on the spectral characteristic data to obtain angular light intensity distribution data, and uniformly dividing the three regions of the inner ring, middle ring, and outer ring according to the angular light intensity distribution data to obtain annular region division parameters; performing interpolation fitting processing on the angular light intensity distribution data according to the annular region division parameters to obtain a spatial sampling point sequence, and performing radial normalization processing on the spatial sampling point sequence to obtain normalized light intensity distribution data; performing matrix reconstruction operation on the normalized light intensity distribution data to obtain a light field spatial distribution characteristic matrix, and performing covariance calculation processing on the light field spatial distribution characteristic matrix to obtain a spatial correlation matrix; performing eigenvalue calculation processing according to the spatial correlation matrix to obtain a singular value sequence, and performing descending order arrangement operation on the singular value sequence to obtain main eigencomponents; performing spatial reprojection processing on the main eigencomponents to obtain reconstructed light field distribution data, and performing eigenvector orthogonalization processing according to the reconstructed light field distribution data to obtain light field spatial characteristic data.

[0074] Specifically, after the spectral characteristic data is collected in the rectangular coordinate system, precise coordinate transformation processing is required to meet the geometric characteristic analysis requirements of the annular light source. First, establish a coordinate transformation equation system, and convert the position point (x, y) in the rectangular coordinate system to (r, θ) in the polar coordinate system through the arctangent function and square root operation of the sum of squares, where r represents the distance value from the spatial sampling point to the center of the LED annular light source, and θ represents the angle value between the sampling point position vector and the horizontal reference axis. In the polar coordinate system, extract the spectral energy value at each angular position according to the principle of uniform angular sampling, and integrate it to obtain the light intensity value at the corresponding position, thereby forming a complete angular light intensity distribution data set. At the same time, according to the physical size parameters of the inner ring, middle ring, and outer ring of the LED annular light source, calculate the radius range of each region to determine the radial stratification boundary. Adopting the principle of equal angle division, each annular region is divided into multiple angular units along the circumferential direction, and the number of divided units is determined according to the spatial resolution requirement, generally taking values ranging from 120 to 360. By accurately calculating the geometric parameters such as the start angle, end angle, inner diameter, and outer diameter of each angular unit, a complete annular region division parameter system is formed.

[0075] Specifically, based on the annular region division parameters, the cubic spline interpolation algorithm is used to reconstruct and optimize the angular light intensity distribution data. First, a cubic spline function is constructed within each angular unit. The function coefficients are calculated by analyzing the positions and light intensity values of adjacent data points to ensure the continuity of the zero-order, first-order, and second-order derivatives of the interpolation curve at the nodes. Interpolation calculations are performed for each angular unit to generate denser sampling points in both the angular and radial directions. The sampling interval is usually taken as 1 / 10 of the original data interval to obtain a high-precision spatial sampling point sequence. Then, a radial normalization function is constructed. Considering the distance attenuation characteristics of LED light emission, the inverse square relationship is used to compensate the light intensity values at different radial positions. By dividing the measured light intensity value of each sampling point by its corresponding normalization coefficient, the light intensity difference caused by distance changes is eliminated, and finally, the spatially uniform standard light intensity distribution data is obtained. These data truly reflect the light emission characteristics of the LED annular light source in different regions.

[0076] Specifically, after the system reconstruction of the standardized light intensity distribution data, a two-dimensional matrix representation needs to be established. First, the number of rows and columns of the matrix is determined. The number of rows corresponds to the number of angular sampling points, generally taking 360 to ensure an angular resolution of 1 degree. The number of columns corresponds to the number of radial layers, which varies from 12 to 24 according to the layering requirements of the inner ring, middle ring, and outer ring. The sampling data is positioned and mapped according to the two dimensions of angle and radius, and the standardized light intensity value corresponding to each spatial position is filled into the corresponding position of the matrix to form a complete light field spatial distribution characteristic matrix. Statistical methods are used to calculate the spatial correlation of this characteristic matrix. By calculating the correlation coefficient of the light intensity values of any two position points in the matrix, the correlation coefficient value measuring the correlation degree of the spatial point pairs is obtained. The correlation coefficients of all position pairs are rearranged according to the spatial correspondence relationship to construct an N×N-dimensional spatial correlation matrix, where N is the total number of sampling points of the characteristic matrix, and the matrix element values represent the correlation degree of the light intensity distribution between the corresponding spatial positions.

[0077] Specifically, eigenvalue analysis is performed based on the spatial correlation matrix, and numerical calculation methods are used to solve the characteristic equation. All the eigenvalues of this N×N matrix are calculated through an iterative algorithm. Each eigenvalue corresponds to a basic mode reflecting the spatial distribution characteristics of the light field. The calculated eigenvalues are sorted in descending order of numerical magnitude to obtain a singular value sequence representing the importance degree of different spatial modes. By analyzing the attenuation law of the singular values, the number of main characteristic components is determined. Generally, the eigenvectors corresponding to the eigenvalues with a cumulative contribution rate of more than 95% are selected as the main characteristic components. These main characteristic components contain the key characteristic information of the light field spatial distribution, and their numerical magnitudes directly reflect the weight of this characteristic mode in the overall light field distribution.

[0078] Specifically, spatial domain reprojection is performed on the main feature components to map the information in the feature space back to the physical space. First, the eigenvectors corresponding to each feature component are calculated, and these eigenvectors form an orthogonal basis for describing the spatial distribution of the light field. By linearly combining the main feature components with the corresponding eigenvectors, the spatial distribution of the light field is reconstructed, and the reconstructed light field distribution data reflecting the main spatial features of the LED ring light source are obtained. Then, Schmidt orthogonalization is performed on the eigenvectors in the reconstructed data to eliminate the possible linear correlation between the vectors and obtain mutually orthogonal eigenbasis vectors. Based on these orthogonalized eigenvectors, the spatial structure features of the light field distribution are extracted, including the light intensity uniformity index, the spatial symmetry index, the edge sharpness index, etc. These indices constitute the complete light field spatial characteristic data and comprehensively characterize the spatial light emission characteristics of the LED ring light source.

[0079] Furthermore, the construction of the light field distortion model for characterizing the degree of light intensity distribution distortion based on the light field spatial characteristic data, the light intensity fluctuation frequency characteristic data, and the defect detection rate evaluation result includes: performing standardization processing on the light field spatial characteristic data, the light intensity fluctuation frequency characteristic data, and the defect detection rate evaluation result to obtain normalized eigenvectors; constructing a three-dimensional feature space according to the normalized eigenvectors, and performing principal component analysis on the three-dimensional feature space to obtain main feature components; performing feature weight calculation processing on the main feature components to obtain the contribution coefficients of the features in each dimension, and performing a linear combination operation on the main feature components according to the contribution coefficients to obtain the light field distortion characteristic parameters; establishing a nonlinear regression equation according to the light field distortion characteristic parameters, and performing iterative solution processing on the coefficients of the nonlinear regression equation by the least squares method to obtain the light field distortion model for characterizing the degree of light intensity distribution distortion.

[0080] Specifically, when standardizing the light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results, it is first necessary to solve the problem of inconsistent dimensions of different data types. By calculating the mean and standard deviation of each type of data, the original data is centered and normalized by the standard deviation. Specifically, for the light field spatial characteristic data, which contains the light intensity distribution values of 360×24 spatial sampling points, each data point is subtracted by the average value of all sampling points and then divided by the standard deviation; for the light intensity fluctuation frequency characteristic data, which contains the amplitudes of 128 discrete sampling points in the frequency domain, the mean is also eliminated and the variance is normalized; for the defect detection rate evaluation results, which contain the detection probability values of different types of defects, these probability values are logarithmically transformed and then standardized. After standardization, the three types of data are all mapped into the distribution interval with a mean of 0 and a variance of 1, forming an equal-weight standardized feature space. These standardized data are combined into a high-dimensional feature vector in the order of spatial characteristics, frequency characteristics, and detection rate characteristics. The vector dimension is the sum of all feature dimensions, that is, 8640 + 128 + 10 (assuming there are 10 types of defect types), thus obtaining the normalized feature vector.

[0081] Specifically, when constructing a three-dimensional feature space based on the normalized feature vector, it is necessary to first perform dimensionality reduction on the high-dimensional feature data. Using the principal component analysis (PCA) method, first calculate the covariance matrix of the normalized feature vector, and the matrix dimension is 8778×8778 (i.e., the square of 8640 + 128 + 10). Perform eigenvalue decomposition on this covariance matrix, solve the eigenvalues and eigenvectors through the Jacobi iterative method, set the convergence threshold in the iterative process to 1e-6, and the maximum number of iterations to 1000 times. Sort the obtained eigenvalues from largest to smallest, and select the eigenvectors corresponding to the first three largest eigenvalues as the principal component basis vectors. These three eigenvectors form the basis of the new three-dimensional feature space, where the first eigenvector mainly reflects the spatial distribution characteristics, the second eigenvector mainly reflects the frequency characteristics, and the third eigenvector mainly reflects the detection rate characteristics. Project the original normalized feature vector onto these three basis vectors to obtain the coordinate representation in the new three-dimensional feature space, that is, the principal feature components. Each principal feature component is the projection value of the original feature in the corresponding direction and contains the main change information in that direction.

[0082] Specifically, when calculating the feature weights of the main feature components, it is necessary to comprehensively consider the physical meaning and statistical significance of the features in each dimension. First, calculate the eigenvalue contribution rates in the directions of the three main feature components. Divide each eigenvalue by the sum of all eigenvalues to obtain the relative contribution ratio. Then, introduce a weight modulation factor based on the Fisher discriminant criterion. By calculating the ratio of the between-class scatter to the within-class scatter of the features in samples with different degrees of optical field distortion, obtain the Fisher score reflecting the discriminant ability of the features. Perform an exponential mapping transformation on the Fisher score to convert it into a weight coefficient within the range of [0, 1]. At the same time, considering the physical meaning of different features, introduce a prior weight matrix. The element values in this matrix are determined based on expert knowledge and historical data statistics. Perform a weighted average of the contribution rate, Fisher weight, and prior weight to obtain the final contribution coefficient of the features in each dimension. Based on these contribution coefficients, perform a linear combination of the three main feature components. The combination process uses weighted summation. Multiply each feature component by the corresponding contribution coefficient and then add them together to finally obtain a one-dimensional optical field distortion feature parameter, which comprehensively reflects the distortion degree of the optical field in terms of spatial distribution, frequency characteristics, and defect detection.

[0083] Specifically, based on the calculated optical field distortion feature parameter, it is necessary to establish an accurate mathematical model to describe the degree of optical field distortion. First, construct a nonlinear regression equation. The independent variable of the equation is the optical field distortion feature parameter, and the dependent variable is the measured optical field distortion metric value. Considering the nonlinear relationship between the optical field distortion and the feature parameter, select a polynomial function as the basic model, including quadratic terms, cubic terms, and cross terms, with a total of 10 undetermined coefficients. Then, solve these coefficients by the least squares method, establish an objective function of the sum of squared errors, and use the Levenberg-Marquardt algorithm for iterative optimization. In each iteration, first calculate the error between the model prediction value and the measured value under the current coefficients, then construct the Jacobian matrix, and solve the normal equation to obtain the correction amount of the coefficients. To prevent overfitting, introduce a Tikhonov regularization term, and the regularization parameter is determined by the L-curve method. When the change amount of the coefficients in two consecutive iterations is less than the preset threshold (usually taken as 1e-8) or reaches the maximum number of iterations (set to 1000 times), stop the iteration. The finally obtained nonlinear regression equation is the optical field distortion model characterizing the distortion degree of the light intensity distribution, and this model can accurately predict the distortion degree under the given optical field feature parameters.

[0084] 104. Calculate the light intensity uniformity deviation value and the defect detection ability score of the LED ring light source according to the optical field distortion model, and generate a test evaluation report of the LED ring light source based on the light intensity uniformity deviation value and the defect detection ability score.

[0085] In an embodiment of the present invention, calculating the light intensity uniformity deviation value and the defect detection ability score of the LED ring light source according to the light field distortion model, and generating a test evaluation report of the LED ring light source according to the light intensity uniformity deviation value and the defect detection ability score includes: performing numerical calculation processing on the light field distortion model to obtain the light intensity distortion coefficients of each region, and performing statistical distribution operation according to the light intensity distortion coefficients to obtain a light intensity uniformity deviation curve; performing weighted accumulation processing on the light intensity distortion coefficients of each region according to the light intensity uniformity deviation curve to obtain a comprehensive distortion index, and performing normalization operation on the comprehensive distortion index to obtain a light intensity uniformity deviation value; performing classification statistical processing on the defect detection rate evaluation results to obtain the detection probability distribution of various defects, and performing error analysis operation according to the detection probability distribution to obtain detection confidence data; performing weight distribution processing on the detection probability distribution of various defects according to the detection confidence data to obtain a detection ability weight matrix, and performing feature fusion operation on the detection ability weight matrix to obtain a defect detection ability score; performing multi-dimensional index integration processing on the light intensity uniformity deviation value and the defect detection ability score to obtain a comprehensive performance evaluation index, and generating a test evaluation report according to the comprehensive performance evaluation index.

[0086] Specifically, when performing numerical calculation on the light field distortion model, it is first necessary to apply the model to three regions of the inner ring, middle ring, and outer ring of the LED ring light source. By substituting the light field characteristic parameters of each region into the nonlinear regression equation, the light intensity distortion coefficient of each region is calculated. In the calculation process, the Runge-Kutta method is used to numerically solve the nonlinear equation, the solution step size is set to 0.01, and the calculation accuracy is controlled within the range of 1e-6. For each region, 360 uniformly distributed sampling points are taken along the circumferential direction, and the local light intensity distortion coefficient is calculated at each sampling point. These coefficients reflect the degree to which the light intensity distribution of the LED ring light source deviates from the ideal uniform distribution at different positions. Then, probability statistical analysis is performed on the light intensity distortion coefficients of the three regions to calculate the probability density distribution function of the distortion coefficient of each region. The kernel density estimation method is used, and the Gaussian kernel function is used to smooth the discrete distortion coefficient data. The bandwidth of the kernel function is determined by the Silverman criterion. The probability density curves of the three regions are plotted in the same coordinate system to form a complete light intensity uniformity deviation curve, which reflects the distribution law and severity of light intensity distortion in different regions.

[0087] Specifically, based on the obtained light intensity uniformity deviation curve, quantitative weighted analysis needs to be performed on the light intensity distortion coefficients of each region. First, calculate the statistical moments of the deviation curve for each region, including the mean, variance, skewness, and kurtosis. These statistics reflect different statistical characteristics of the light intensity distortion in each region. Then, according to the importance of each region in actual applications, set the regional weight coefficients. The weight ratios of the inner ring, middle ring, and outer ring are assigned as 4:3:3. Multiply the light intensity distortion coefficient of each region by the corresponding weight coefficient, and accumulate the weighted results of all regions to obtain a comprehensive distortion index reflecting the overall distortion degree. Considering the comparability requirements between different batches of LED ring light sources, normalize the comprehensive distortion index. The normalization process uses the min-max normalization method to map the original comprehensive distortion index to the interval [0,1]. When mapping, refer to the benchmark range determined by historical test data to finally obtain the normalized light intensity uniformity deviation value. Through this deviation value, the light intensity distribution uniformity level of the LED ring light source can be quantitatively characterized.

[0088] Specifically, when classifying and statistically processing the defect detection rate evaluation results, a special statistical analysis system needs to be established for each type of defect. First, classify the defects into different categories such as scratches, pitting, bubbles, stains, etc. according to their morphological characteristics. Each category is further divided into three levels: large, medium, and small according to the defect size. For the defect samples of each category and level, calculate their detection probabilities under different working conditions (including different light intensity levels and detection speeds). Using the stratified sampling method, repeat 100 detection experiments under each working condition, and record the number of successfully detected defects in each experiment. Through the law of large numbers principle, calculate the average detection rate and standard deviation of each defect type under each working condition. Use the Bootstrap resampling technique to estimate the confidence interval of the detection probability through 1000 resamplings, and construct a complete detection probability distribution curve. Perform an analysis of variance on these distribution data, calculate the ratio of the between-group variance to the within-group variance, and evaluate the significance of the influence of different working conditions on the detection rate. The detection confidence data obtained through these statistical analyses include the expected value, fluctuation range, and confidence level of the detection rate of each defect type.

[0089] Specifically, based on the detected confidence data, a reasonable weight allocation mechanism needs to be established. First, construct an analytic hierarchy process model, taking the defect type, size grade, and working conditions as evaluation factors at different levels. Establish a pairwise comparison matrix between factors through the expert scoring method. The matrix size is n×n, where n is the total number of all evaluation factors. Conduct a consistency test on the comparison matrix, calculate the consistency index CI and the consistency ratio CR. When CR is less than 0.1, the judgment matrix is considered to have satisfactory consistency. Then, use the eigenvalue method to calculate the weight vector, and normalize the eigenvector corresponding to the largest eigenvalue to obtain the weight coefficients of each factor. Multiply these weight coefficients by the corresponding detection probabilities to construct a detection ability weight matrix. Perform eigenvalue decomposition on this weight matrix, extract the eigenvector corresponding to the largest eigenvalue as the principal component, and through the principal component score calculation method, integrate all detection indicators into a unified defect detection ability score, which comprehensively reflects the detection ability of the LED ring light source for various types of defects.

[0090] Specifically, when integrating multi-dimensional indicators such as the light intensity uniformity deviation value and the defect detection ability score, a scientific comprehensive evaluation system needs to be established. First, construct a standardization model for evaluation indicators. Use the fuzzy mathematics method to convert the light intensity uniformity deviation value into a fuzzy evaluation value through a semi-trapezoidal membership function. The parameters of the membership function are optimized and determined through the genetic algorithm based on a large amount of historical test data. At the same time, establish a five-level evaluation standard for the defect detection ability score, and calculate the closeness between the score value and each level standard through the distance discrimination method. Then, establish a fuzzy comprehensive evaluation matrix. The row vectors of the matrix represent different evaluation indicators, and the column vectors represent different evaluation levels. Use the weighted average model to calculate the comprehensive membership degree, and the weight coefficients are determined by the entropy weight method, considering the dispersion degree of the indicators during the calculation. Calculate the eccentricity of the comprehensive membership degree to determine the final evaluation level. Based on the evaluation results, generate a test evaluation report in a standard format. The report content includes the recording of test environment parameters, the original test data of each indicator, the intermediate results of the data processing process, the final quantitative evaluation results, the recording of the equipment operation status, and the test conclusion. Through this multi-dimensional indicator integration method, the overall performance of the LED ring light source can be comprehensively and objectively evaluated.

[0091] In this embodiment, through the calibration and initialization process of the LED ring light source installed on the angle-adjustable bracket and the standard silicon wafer simulation board, a reference test sequence is obtained; according to the reference test sequence, the LED ring light source is controlled to irradiate the standard silicon wafer simulation board, and multi-region light field acquisition is performed at different speed gears to obtain multi-dimensional test data; the multi-dimensional test data is analyzed for characteristics to obtain light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results, and then a light field distortion model is constructed; according to the light field distortion model, the light intensity uniformity deviation value and the defect detection ability score are calculated, and a test evaluation report is generated. This method can accurately evaluate the uniformity of the light intensity distribution and the defect detection ability by multi-region acquisition and data analysis, and improve the effect of the LED ring light source in the high-precision detection of silicon wafers.

[0092] The above describes the LED ring light source test method in the embodiment of the present invention. Next, the LED ring light source test device in the embodiment of the present invention will be described. Please refer to Figure 2 , an embodiment of the LED ring light source test device in the embodiment of the present invention includes:

[0093] A preprocessing module 201, configured to perform calibration and initialization processing on the LED ring light source installed on the angle-adjustable bracket and the standard silicon wafer simulation board, and obtain the reference test sequence of the LED ring light source;

[0094] A test module 202, configured to control the LED ring light source to irradiate the standard silicon wafer simulation board according to the reference test sequence, and perform multi-region acquisition on the light field of the LED ring light source irradiated on the standard silicon wafer simulation board at different speed gears to obtain multi-dimensional test data;

[0095] An analysis module 203, configured to perform characteristic analysis processing on the multi-dimensional test data to obtain light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results, and construct a light field distortion model representing the distortion degree of the light intensity distribution based on the light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results;

[0096] An evaluation module 204, configured to calculate the light intensity uniformity deviation value and the defect detection ability score of the LED ring light source according to the light field distortion model, and generate a test evaluation report of the LED ring light source according to the light intensity uniformity deviation value and the defect detection ability score.

[0097] In an embodiment of the present invention, the LED ring light source testing device runs the above-mentioned LED ring light source testing method. The LED ring light source testing device performs calibration initialization processing on the LED ring light source installed on the angle-adjustable bracket and the standard silicon wafer simulation board to obtain a reference test sequence; controls the LED ring light source to irradiate the standard silicon wafer simulation board according to the reference test sequence, and performs multi-region light field acquisition at different speed gears to obtain multi-dimensional test data; performs characteristic analysis on the multi-dimensional test data to obtain light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results, and then constructs a light field distortion model; calculates the light intensity uniformity deviation value and the defect detection ability score according to the light field distortion model, and generates a test evaluation report. This method constructs a light field distortion model through multi-region acquisition and data analysis, and can accurately evaluate the uniformity of the light intensity distribution and the defect detection ability, improving the effect of the LED ring light source in the high-precision detection of silicon wafers.

[0098] Above Figure 2 The LED ring light source testing device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. Next, the LED ring light source testing equipment in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0099] Figure 3 FIG. is a schematic structural diagram of an LED ring light source testing equipment provided by an embodiment of the present invention. The LED ring light source testing equipment 300 may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPU) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 for storing application programs 333 or data 332 (for example, one or more mass storage device terminals). Among them, the memory 320 and the storage media 330 may be transient storage or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the LED ring light source testing equipment 300. Further, the processor 310 may be configured to communicate with the storage media 330 and execute a series of instruction operations in the storage media 330 on the LED ring light source testing equipment 300 to implement the steps of the above-mentioned LED ring light source testing method.

[0100] The LED ring light source testing device 300 may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, and so on. Those skilled in the art can understand that Figure 3 The structure of the LED ring light source testing device shown does not constitute a limitation on the LED ring light source testing device provided by the present invention. It may include more or fewer components than those shown, or combine certain components, or have different component arrangements.

[0101] The present invention also provides a computer-readable storage medium. The computer-readable storage medium may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the LED ring light source testing method.

[0102] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system or device and unit can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0103] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0104] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for testing an LED ring light source, characterized in that: The LED ring light source testing method comprises: The positional relationship between the LED ring light source mounted on the angle-adjustable bracket and the standard silicon wafer simulation board is measured to obtain position parameter data, and the motion parameters of the standard silicon wafer simulation board are set according to the position parameter data; the lighting parameters of the LED ring light source are configured according to the motion parameters to obtain a parameter combination of the lighting angle, the working distance and the light intensity level; the motion parameters and the parameter combination are subjected to timing matching processing to obtain a motion-lighting linkage control scheme, and a benchmark test sequence is generated according to the motion-lighting linkage control scheme; Controlling the LED ring light source to illuminate the standard silicon wafer simulation board according to the benchmark test sequence, and collecting the light field illuminated by the LED ring light source on the standard silicon wafer simulation board in multiple regions at different speed gears to obtain multi-dimensional test data; Performing characteristic analysis on the multi-dimensional test data to obtain light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results, and constructing a light field distortion model that characterizes the degree of light intensity distribution distortion based on the light field spatial characteristic data, light intensity fluctuation frequency characteristic data, and defect detection rate evaluation results; The light intensity uniformity deviation value and the defect detection capability score of the LED ring light source are calculated according to the light field distortion model, and a test evaluation report of the LED ring light source is generated according to the light intensity uniformity deviation value and the defect detection capability score.

2. The LED ring light source testing method according to claim 1, characterized in that: The LED ring light source is controlled to illuminate the standard silicon wafer simulation board according to the benchmark test sequence, and the light field illuminated by the LED ring light source on the standard silicon wafer simulation board is collected in multiple regions at different speed gears to obtain multi-dimensional test data including: According to the benchmark test sequence, determining the light source illumination control instruction of the LED ring light source, and performing speed gear switching control on the standard silicon wafer simulation board; Under the light source illumination control instruction and different speed gears of the standard silicon wafer simulation board, light field collection is performed on the inner ring, middle ring and outer ring of the LED ring light source through a photodetector array to obtain light intensity distribution data; The standard defects on the surface of the standard silicon wafer simulation board are imaged and processed by an industrial camera to obtain defect detection image data, and the spectrum of the inner ring, middle ring and outer ring regions is collected and processed by a spectrum analyzer according to the light intensity distribution data to obtain spectrum characteristic data; The light intensity distribution data, defect detection image data and spectral characteristic data are subjected to data integration processing to obtain multi-dimensional test data.

3. The LED ring light source testing method according to claim 2, characterized in that: The characteristic analysis and processing of the multi-dimensional test data to obtain light field space characteristic data, light intensity fluctuation frequency characteristic data and defect detection rate evaluation results includes: Performing Fourier transform processing on the light intensity distribution data to obtain a light intensity distribution spectrum feature vector, and discretizing and sampling the light intensity fluctuation frequency domain distribution of the LED ring light source according to the light intensity distribution spectrum feature vector to obtain light intensity fluctuation frequency characteristic data; Performing spatial mapping processing on the light intensity distribution of the inner ring, the middle ring and the outer ring according to the spectral characteristic data to obtain a light field spatial distribution characteristic matrix, and performing singular value decomposition operation on the light field spatial distribution characteristic matrix to obtain light field spatial characteristic data; The defect detection image data is subjected to edge detection and morphological processing to obtain a defect feature vector, and the standard defects are identified and classified according to the defect feature vector to obtain a defect detection rate evaluation result.

4. The LED ring light source testing method according to claim 3, characterized in that: The light intensity distribution of the inner ring, the middle ring and the outer ring is spatially mapped according to the spectral characteristic data to obtain a light field spatial distribution characteristic matrix, and a singular value decomposition operation is performed on the light field spatial distribution characteristic matrix to obtain light field spatial characteristic data, including: Performing polar coordinate transformation processing on the spectral characteristic data to obtain angular light intensity distribution data, and evenly dividing the inner ring, middle ring and outer ring regions into three regions according to the angular light intensity distribution data to obtain annular region division parameters; Performing interpolation fitting processing on the angular light intensity distribution data according to the annular area division parameters to obtain a spatial sampling point sequence, and performing radial normalization processing on the spatial sampling point sequence to obtain standardized light intensity distribution data; Performing a matrix reconstruction operation on the standardized light intensity distribution data to obtain a light field spatial distribution characteristic matrix, and performing a covariance calculation process on the light field spatial distribution characteristic matrix to obtain a spatial correlation matrix; Performing eigenvalue calculation processing according to the spatial correlation matrix to obtain a singular value sequence, and performing a descending order operation on the singular value sequence to obtain a main eigencomponent; The main characteristic components are spatially reprojected to obtain reconstructed light field distribution data, and characteristic vector orthogonalization is performed on the reconstructed light field distribution data to obtain light field spatial characteristic data.

5. The LED ring light source testing method according to claim 1, characterized in that: The light field distortion model characterizing the degree of light intensity distribution distortion is constructed based on the light field spatial characteristic data, the light intensity fluctuation frequency characteristic data and the defect detection rate evaluation result, including: Standardizing the light field spatial characteristic data, the light intensity fluctuation frequency characteristic data and the defect detection rate evaluation result to obtain a normalized feature vector; Constructing a three-dimensional feature space according to the normalized feature vector, and performing a principal component analysis operation on the three-dimensional feature space to obtain a principal feature component; Performing feature weight calculation processing on the main feature components to obtain contribution coefficients of features in each dimension, and performing linear combination operations on the main feature components according to the contribution coefficients to obtain light field distortion feature parameters; A nonlinear regression equation is established according to the light field distortion characteristic parameters, and the coefficients of the nonlinear regression equation are iteratively solved by the least square method to obtain a light field distortion model that characterizes the degree of light intensity distribution distortion.

6. The LED ring light source testing method according to claim 1, characterized in that: The step of calculating the light intensity uniformity deviation value and the defect detection capability score of the LED ring light source according to the light field distortion model, and generating a test evaluation report of the LED ring light source according to the light intensity uniformity deviation value and the defect detection capability score includes: Performing numerical calculation processing on the light field distortion model to obtain light intensity distortion coefficients of each region, and performing statistical distribution calculation based on the light intensity distortion coefficients to obtain a light intensity uniformity deviation curve; According to the light intensity uniformity deviation curve, a weighted accumulation process is performed on the light intensity distortion coefficient of each region to obtain a comprehensive distortion index, and the comprehensive distortion index is normalized to obtain a light intensity uniformity deviation value; Performing classified statistical processing on the defect detection rate evaluation results to obtain the detection probability distribution of each type of defect, and performing error analysis calculation based on the detection probability distribution to obtain detection confidence data; Performing weight distribution processing on the detection probability distribution of the various defects according to the detection confidence data to obtain a detection capability weight matrix, and performing feature fusion operation on the detection capability weight matrix to obtain a defect detection capability score; The light intensity uniformity deviation value and the defect detection capability score are subjected to multi-dimensional indicator integration processing to obtain a comprehensive performance evaluation indicator, and a test evaluation report is generated based on the comprehensive performance evaluation indicator.

7. A LED ring light source testing device, characterized in that: The LED annular light source testing device comprises: A preprocessing module is used to measure the positional relationship between the LED ring light source installed on the angle-adjustable bracket and the standard silicon wafer simulation board to obtain position parameter data, and set the motion parameters of the standard silicon wafer simulation board according to the position parameter data; configure the lighting parameters of the LED ring light source according to the motion parameters to obtain a parameter combination of the lighting angle, working distance and light intensity level; perform timing matching processing on the motion parameters and the parameter combination to obtain a motion-lighting linkage control scheme, and generate a benchmark test sequence according to the motion-lighting linkage control scheme; A test module, used to control the LED ring light source to irradiate the standard silicon wafer simulation board according to the benchmark test sequence, and to collect multi-area light fields irradiated by the LED ring light source on the standard silicon wafer simulation board at different speed gears to obtain multi-dimensional test data; An analysis module is used to perform characteristic analysis on the multi-dimensional test data to obtain light field spatial characteristic data, light intensity fluctuation frequency characteristic data and defect detection rate evaluation results, and to construct a light field distortion model that characterizes the degree of light intensity distribution distortion based on the light field spatial characteristic data, light intensity fluctuation frequency characteristic data and defect detection rate evaluation results; An evaluation module is used to calculate the light intensity uniformity deviation value and defect detection capability score of the LED ring light source according to the light field distortion model, and generate a test evaluation report of the LED ring light source according to the light intensity uniformity deviation value and defect detection capability score.

8. An LED ring light source testing device, characterized in that: The LED ring light source testing device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the LED ring light source testing device to perform the steps of the LED ring light source testing method according to any one of claims 1 to 6.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the steps of the LED ring light source testing method as described in any one of claims 1 to 6 are implemented.

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