A testing device and method for testing crystal uniformity using photoelectric properties

Abstract

This invention discloses a testing device and method for testing crystal uniformity using photoelectric properties. The method includes: pre-setting multiple different target detection bands; scanning the target detection area of ​​the crystal under test using excitation light in each preset target detection band to obtain a fluorescence signal matrix; preprocessing the fluorescence signal matrix in each target detection band to obtain a fluorescence intensity distribution matrix; extracting single-band uniformity parameters from the fluorescence intensity distribution matrix; and fusing the single-band uniformity parameters from all target detection bands to obtain crystal uniformity parameters as the test result. This invention achieves quantitative output of both global and local uniformity results for crystal uniformity.

Description

Technical Field

[0001] This invention belongs to the field of precision optical measurement and testing technology, and particularly relates to a testing device and testing method for evaluating the uniformity of optical crystals. Background Technology

[0002] In precision optical systems, the uniformity of core crystal components is crucial. The uniformity of crystal components is influenced by two main factors: their inherent microstructure and the manufacturing process. The manufacturing process refers to externally introduced surface roughness, which can cause light scattering. In high-power laser systems, this not only reduces light transmission efficiency but can also lead to safety issues. Material growth defects, such as point defects and impurities, arise from static structural defects caused by inconsistent environmental parameters during crystal growth, primarily affecting the crystal's light transmission and luminescence efficiency. Currently, thanks to the maturity of surface processing technologies such as magnetorheological polishing, the surface shape issues of component processing have been well controlled. However, with the advancement of crystal growth processes and the upgrading of application requirements, existing uniformity evaluation methods are insufficient to characterize the nanoscale microstructural features of crystal materials, and the test results are insufficient to support application iteration and process optimization.

[0003] Currently, the assessment of internal homogeneity of crystals mainly involves two types of detection methods:

[0004] 1. Material chemical elemental analysis, such as secondary ion mass spectrometry or plasma mass spectrometry, uses ion beams to exfoliate the material and detect secondary ion information at different depths; this is a destructive analysis. By probing elemental composition, doping concentration, and the spatial distribution of impurities in the matrix, it traces the layered arrangement (growth striations) and elemental ratios (compositional segregation) from a chemical composition perspective. These methods can provide semi-quantitative chemical composition information, but they cannot reflect the geometrical dislocations, structural distortions, and uniformity of the crystal lattice within a region. Furthermore, they are typically localized and destructive, making it difficult to achieve comprehensive non-destructive evaluation of large-size crystals.

[0005] 2. Uniformity can be non-destructively reflected through crystal optical parameters. Currently, the industry commonly uses laser interferometry and stress birefringence polarization to reflect the overall changes in crystal structure by measuring optical path difference and polarization state changes. However, refractive index is affected by many factors such as crystal facet shape, chemical composition, and lattice structure. The measurement results are superimposed effects, making it difficult to trace and separate processing errors and material growth causes, and thus unable to truly evaluate the uniformity of the material's internal structure.

[0006] Furthermore, current methods primarily rely on rear-mounted photodetectors (PDs) or PD arrays to collect the excitation fluorescence of crystals. However, this method still has the following drawbacks:

[0007] 1. In precision optical measurement, the PD (Power Producer) and amplification circuits are the main sources of intrinsic noise. The electronic noise they generate can couple with weak target signals, reducing the output signal-to-noise ratio and making it difficult to obtain effective information in subsequent signal processing, thus making it difficult to extract high-fidelity target signals.

[0008] 2. At present, the uniformity of crystal materials is mainly characterized by refractive index uniformity. This type of method represents the uniformity index of crystal by evaluating the optical path difference over a large area. However, this type of method has limited reflection of microscopic defects inside the crystal and cannot fully meet the requirements in some high-precision applications and advanced processes.

[0009] 3. Currently, most PDs output the spatial integral of the luminescence intensity within the detection area when collecting crystal fluorescence signals, which weakens the local luminescence difference characteristics of the crystal and cannot simultaneously achieve a comprehensive evaluation of the global fluorescence signal of the crystal and a fine characterization of the signal in the local area. Summary of the Invention

[0010] To address the problems existing in the background art, the present invention provides a testing device and method for testing the uniformity of crystals using photoelectric properties.

[0011] The technical solution adopted in this invention is:

[0012] I. A test method for testing crystal uniformity using photoelectric properties

[0013] The testing method includes the following steps:

[0014] Step S1) Pre-set multiple different target detection bands. Under each preset target detection band, use excitation light to scan the target detection area of ​​the crystal under test to obtain a fluorescence signal matrix.

[0015] The target detection band is the emission band corresponding to the secondary fluorescence of the crystal under test.

[0016] In step S1, the target detection band can be switched by switching the filters on the filter wheel: when the excitation light irradiates each scanning sampling point of the crystal under test, the secondary fluorescence passes through the filter and generates fluorescence under the corresponding target detection band, which is collected by the detection module to obtain the fluorescence signal at the scanning sampling point; the fluorescence signals at all scanning sampling points in the target detection area form a fluorescence signal matrix.

[0017] In step S1, multiple different target detection bands can be selected based on the characteristic fluorescence emission peaks of the crystal to be tested.

[0018] Step S2) Preprocess the fluorescence signal matrix under each target detection band to obtain the fluorescence intensity distribution matrix, and extract the single-band uniformity parameter from the fluorescence intensity distribution matrix.

[0019] In step S2, the preprocessing includes signal correction processing and surface domain fitting processing.

[0020] In step S2, the process of extracting the single-band uniformity parameter includes: processing the fluorescence intensity distribution matrix using the sliding window method to obtain the local dispersion coefficients at each sampling window; combining the local dispersion coefficients at all sampling windows to obtain the uniformity distribution map matrix; and performing a global average on the uniformity distribution map matrix to obtain the single-band uniformity parameter.

[0021] Step S3) The single-band uniformity parameters under all target detection bands are fused to obtain the crystal uniformity parameters as the test results.

[0022] In step S3, the fusion process can be: weighted calculation of the single-band uniformity parameters of all target detection bands according to the spectral weights of each target detection band.

[0023] The process of obtaining the spectral weights for each target detection band includes: obtaining the absorption spectrum of the crystal under test in each target detection band based on the absorption coefficient curve of the crystal under test, and calculating the effective energy based on the absorption spectrum; normalizing the effective energy in all target detection bands to obtain the spectral weights for each target detection band.

[0024] The crystal homogeneity parameter is the comprehensive homogeneity index U. final U final U is used to characterize the degree of dispersion of the surface-domain signal of the crystal under test in a selected spectral band or after the fusion of multiple spectral bands. final The numerical value is negatively correlated with uniformity, U final A smaller value indicates less surface area fluctuation and better uniformity. final The larger the value, the greater the fluctuation of the area and the worse the uniformity.

[0025] Furthermore, in specific implementations, the crystal uniformity parameter U can be... final Compared with a preset threshold T, when U final When U ≤ T, the uniformity of the crystal under test is deemed to meet the requirements; when U final When the threshold value is greater than T, the uniformity of the crystal under test is deemed to be unsatisfactory. The threshold value T is determined based on the application requirements of the crystal under test, or on the calibration results of reference samples and historical batch statistics.

[0026] II. A testing device for testing crystal uniformity using photoelectric properties

[0027] The testing apparatus includes:

[0028] The light source module is used to emit excitation light;

[0029] A focusing lens group is used to focus the excitation light onto the scanning sampling point of the crystal under test;

[0030] The sample carrying and motion module is used to carry the crystal under test and move the crystal under test.

[0031] A filter wheel is arranged on the fluorescence receiving side of the crystal under test and is equipped with at least one filter for selecting the target detection band, which is used to separate the fluorescence signal of the target detection band corresponding to the filter.

[0032] The detection module is used to collect fluorescence in the target detection band corresponding to the filter in the filter wheel;

[0033] The data processing unit is used to receive fluorescence signals in the target detection band and process them according to the above test method to obtain test results.

[0034] The detection module includes a photodetector array, which is a rectangular array composed of multiple photodiode (PD) elements. The photodiode elements are silicon-based photodiodes or indium gallium arsenide-based photodiodes: when the secondary fluorescence of the crystal under test is mainly distributed in the range of 400nm to 1100nm, the photodiode element is a silicon-based photodiode; when the secondary fluorescence of the crystal under test is mainly distributed in the range of 900nm to 1700nm, the photodiode element is an indium gallium arsenide-based photodiode.

[0035] The crystals to be tested applicable to this invention include optical crystals and laser windows.

[0036] The beneficial effects of this invention are as follows:

[0037] 1. This invention improves the quality of target fluorescence signals across the entire signal acquisition and data processing chain. On the acquisition side, preliminary spectral screening blocks most of the excitation light residue, enhancing the visibility and signal-to-noise ratio of narrow-linewidth target characteristic spectra. On the front-end readout side, a low-noise transimpedance amplifier and band-limited filter are used to match the target bandwidth, suppressing intrinsic noise and stabilizing readout. On the data side, correction algorithms such as dark field subtraction, flat field correction, and gain calibration are implemented to further purify the signal and improve the signal-to-noise ratio and measurement repeatability, thereby ensuring high-fidelity detection.

[0038] 2. This invention proposes a method for quantifying crystal homogeneity to characterize microscopic defects. By assigning physically interpretable weights to each gated spectral band based on first-principles calculations, and by weighted fusion of the acquired multi-band secondary fluorescence signals, a quantitative index for crystal homogeneity that can characterize microscopic defects is formed.

[0039] 3. This invention uses PD array imaging and interpolation algorithm to fit the surface region, realizing the acquisition of signals in the entire sample area. Then, through the crystal uniformity index evaluation system, the quantitative results of global and local uniformity are output. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of a crystal uniformity detection system;

[0041] Figure 2 This is a schematic diagram of the optical path for crystal uniformity detection.

[0042] Figure 3 This is a schematic diagram of a PD array.

[0043] In the figure, 1 is the light source module, 11 is the laser; 2 is the focusing lens group, 21 is the beam expander lens, 22 is the collimating lens; 3 is the sample carrying and motion module, 31 is the displacement stage, 32 is the sample stage; 4 is the filter wheel, 41 is the filter; 5 is the detection module, 51 is the photodiode element, 52 is the photodetector array; 6 is the data processing module, and 7 is the crystal under test. Detailed Implementation

[0044] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0045] This invention provides a testing device and method for testing the uniformity of crystals using photoelectric properties. The invention proposes a uniformity testing method based on photoluminescence. Internal defects or doping in crystals introduce intermediate energy levels into the band gap. Therefore, by using light excitation (photoluminescence), secondary fluorescence information can be obtained, which can then provide feedback on the location and type of defects, thereby evaluating the consistency and uniformity of the material. For example, lattice defects and oxygen vacancies form color centers after excitation, and their characteristic absorption can be used to identify specific defects or doping types; specific band gap transitions caused by metal ions exhibit sharp peaks at specific light wavelengths, and their relative intensity can serve as a direct indicator of the doping valence state.

[0046] The apparatus and method of this invention can be used for non-destructive testing of the uniformity of crystalline materials. Primarily, after excitation light passes through the crystal, a photodetector (PD) or PD array acquires the changes in the properties of the transmitted light, and the uniformity of the crystal is tested using this change as a measurement. The apparatus and method of this invention are applicable to the uniformity testing of materials such as oxide crystals, fluoride crystals, and wide-bandgap semiconductor crystals.

[0047] The method of the present invention specifically includes the following steps:

[0048] Step S1) Pre-set multiple different target detection bands. Under each preset target detection band, use excitation light (excitation light) to scan the target detection area of ​​the crystal under test to obtain a fluorescence signal matrix.

[0049] The crystal under test includes an optical crystal and a laser window.

[0050] Among them, the target detection band is the emission band corresponding to the secondary fluorescence of the crystal under test.

[0051] In step S1, the target detection band is switched by switching the filters on the filter wheel: when the excitation light shines on each scanning sampling point of the crystal under test, the secondary fluorescence passes through the filter and generates fluorescence under the corresponding target detection band, which is collected by the detection module to obtain the fluorescence signal at the scanning sampling point; the fluorescence signals at all scanning sampling points in the target detection area form a fluorescence signal matrix.

[0052] In step S1, multiple different target detection bands are selected based on the characteristic fluorescence emission peaks of the crystal to be tested.

[0053] Step S2) Preprocess the fluorescence signal matrix under each target detection band to obtain the fluorescence intensity distribution matrix, and extract the single-band uniformity parameter from the fluorescence intensity distribution matrix.

[0054] In step S2, the preprocessing includes signal correction processing and surface domain fitting processing.

[0055] The signal correction process includes performing dark field subtraction, flat field correction, and reference ratio normalization on the fluorescence signal matrix to obtain the initial fluorescence intensity distribution matrix.

[0056] The surface region fitting process includes: fitting and reconstructing the quadratic fluorescence intensity matrix to obtain the fluorescence intensity distribution matrix. Specifically, this involves calculating continuous signal data between sampling points using spatial interpolation. Spatial interpolation algorithms include, but are not limited to, bilinear interpolation or kriging interpolation. The purpose of this step is to eliminate sampling gaps between PD elements, providing a continuous data foundation for subsequent uniformity analysis.

[0057] In step S2, the process of extracting the single-band uniformity parameter includes: processing the fluorescence intensity distribution matrix using the sliding window method to obtain the local dispersion coefficients at each sampling window; combining the local dispersion coefficients at all sampling windows to obtain the uniformity distribution map matrix; and performing a global average on the uniformity distribution map matrix to obtain the single-band uniformity parameter.

[0058] Step S3) The single-band uniformity parameters under all target detection bands are fused to obtain the crystal uniformity parameters as the test results.

[0059] In step S3, the fusion process is as follows: weighted calculation is performed based on the spectral weights of each target detection band.

[0060] The process of obtaining the spectral weights for each target detection band includes: obtaining the absorption spectrum of the crystal under test in each target detection band based on the absorption coefficient curve of the crystal under test; calculating the effective energy in each target detection band based on the absorption spectrum of each target detection band; and normalizing the effective energy in all target detection bands to obtain the spectral weights for each target detection band.

[0061] Furthermore, the method of the present invention also includes a calibration process:

[0062] a. Dark signal calibration: Acquire and store the noise substrate under no-light conditions;

[0063] b. Flat-field response calibration: When the crystal to be tested is unavailable, place a standard fluorescent plate and collect the response correction coefficients. This step is only performed when the excitation spectrum is within the effective spectral response range of the post-PD. If the spectrum is outside this range, this step can be omitted.

[0064] Furthermore, the present invention also provides a testing apparatus for testing crystal uniformity using photoelectric properties, comprising:

[0065] Light source module 1 is used to emit excitation light;

[0066] Focusing lens group 2 is used to focus the excitation light onto the scanning sampling point of the crystal under test 7;

[0067] Sample carrying and motion module 3 is used to carry the crystal under test 7 and move the crystal under test 7.

[0068] A filter wheel 4 is arranged on the fluorescence receiving side of the crystal under test 7 and is equipped with at least one filter 41 for selecting the target detection band, which is used to separate the fluorescence signal of the target detection band corresponding to the filter 41 from the mixed signal light containing excitation light and secondary fluorescence.

[0069] The detection module 5 is used to collect fluorescence under the target detection band corresponding to the filter 41 in the filter wheel 4 and convert it into a digital signal;

[0070] The data processing unit 6 is used to receive the digital signal converted from fluorescence in the target detection band, and process it according to the above test method to obtain the test result.

[0071] Specifically, the light source module 1 includes a laser 11, the excitation light emitted by the laser 11 has a center wavelength that can be selected in the range of visible light to near-infrared (e.g., 400nm~1600nm), and the output power can be adjusted in the range of 100mW to 10W.

[0072] Specifically, the focusing lens group 2 is a modulation lens group facing the excitation light outlet, used to modulate the excitation beam from the light source module 1 into a more uniform light spot with an effective illumination size, thereby incident on the effective area of ​​the crystal surface test.

[0073] In a specific implementation, the focusing lens group 2 may include a beam expander lens 21 and a collimating lens 22 arranged sequentially along the optical path. The beam expander lens 21 is used to expand the excitation light, and the collimating lens 22 is used to collimate the expanded excitation light to generate an excitation beam.

[0074] Specifically, the sample carrying and motion module 3 includes a displacement stage 31 and a sample stage 32. The sample stage 32 on the displacement stage 31 carries the crystal to be tested. The displacement stage 31 can move in two dimensions in a plane perpendicular to the excitation optical axis to achieve scanning of the crystal surface.

[0075] Specifically, filter 41 is a narrowband bandpass filter. The bandwidth of the narrowband bandpass filter is measured in full width at half maximum (FWHM) and characterized by a relative bandwidth Δλ / λ0, where Δλ is the FWHM and λ0 is the center wavelength. The relative bandwidth is 0.1% to 10%, preferably 0.2% to 5%, to meet the spectral band selection requirements of different test crystals in terms of fluorescence bandwidth and signal-to-noise ratio. The center wavelength can be selected based on the characteristic fluorescence emission peaks of the test crystal. For example, if the test crystal emits fluorescence in bands λ1 and λ2, filters with center wavelengths close to λ1 and λ2 are selected to separate and selectively detect fluorescence signals in different bands.

[0076] Specifically, the detection module 5 includes a photodetector array 52 and a signal processing circuit board.

[0077] The signal processing circuit board is used to convert the optical signal into a digital signal and then transmit it to the data processing module 6.

[0078] like Figure 3 As shown, the photodetector array 52 adopts a rectangular array composed of multiple photodiode elements 51, that is, the photodiode elements 51 are arranged in a two-dimensional matrix of n rows × m columns.

[0079] In practical implementation, the center-to-center spacing of adjacent photodiode elements 51 in the row or column direction of the photodetector array 52 can be determined according to the size and number of elements of the crystal under test, thereby ensuring precise matching between the overall photosensitive area of ​​the array and the crystal surface. For example, the center-to-center spacing of adjacent photodiode elements 51 in the row direction is calculated using the following formula:

[0080] d=L / n

[0081] In the formula, d represents the center-to-center distance between adjacent photodiode elements 51 in the row direction, L represents the size of the crystal under test in the row direction, and n represents the number of rows of the photodetector array 52.

[0082] Preferably, the photodiode element 51 is a silicon-based photodiode or an indium gallium arsenide-based photodiode: when the secondary fluorescence of the crystal under test is mainly distributed in the range of 400nm to 1100nm, the photodiode element is a silicon-based photodiode; when the secondary fluorescence of the crystal under test is mainly distributed in the range of 900nm to 1700nm, the photodiode element is an indium gallium arsenide-based photodiode.

[0083] Specifically, the crystal under test 7 includes an optical crystal and a laser window.

[0084] The process of acquiring a fluorescence signal matrix using the device of the present invention includes:

[0085] a. Place the crystal to be tested 7 on the sample stage 32 and align it;

[0086] b. Drive the filter wheel 4 to select the target detection band in sequence, and then collect and store the original signal of the target detection band by scanning each sampling point in the entire target detection area of ​​the crystal under test 7.

[0087] Specific embodiments of the present invention are as follows:

[0088] Example

[0089] In this embodiment, a square Nd:YAG (neodymium-doped yttrium aluminum garnet) crystal with a size of 10mm×10mm (L=10mm) is selected. This type of crystal is one of the most widely used laser gain media in industrial and scientific research fields and is representative.

[0090] like Figure 1 and Figure 2 As shown, the testing device used in this embodiment includes a light source module 1, a focusing lens group 2, a sample carrying and motion module 3, a filter wheel 4, a detection module 5, and a data processing unit 6.

[0091] In this embodiment, the light source module 1 is a solid-state laser. Depending on the absorption characteristics of different crystals, its center wavelength can be selected within the visible to near-infrared band (e.g., 400nm~1600nm), and the output power can be adjusted from 100mW to 10W. This module is equipped with a precision temperature control and power stabilization unit to ensure the stability of the excitation light parameters.

[0092] In this embodiment, to address common issues such as crystal size, spatial sampling density, and multi-channel parallel processing at the back end, an 8×8 (n×n) photodetector array 52 is used. Each independent PD element in the array has an effective photosensitive area of ​​1mm×1mm, and the center-to-center spacing between adjacent elements is designed to be 1.25mm (d=1.25mm). There is a physical gap of 0.25mm between the PD elements, and the physical photosensitive area of ​​the entire 8×8 array is 10mm×10mm, which can completely cover crystal samples of mainstream sizes.

[0093] In this embodiment, the signal processing circuit board uses a microcontroller and integrates 64 parallel analog front-ends. Each channel uses a transimpedance amplifier to convert photocurrent into voltage, which is then digitally processed by an analog-to-digital converter.

[0094] In this embodiment, the data processing unit 6 is an industrial computer with a built-in multi-channel synchronous data acquisition system, used to perform subsequent data processing and index quantification.

[0095] In this embodiment, the test method for testing crystal uniformity using the above-described device is as follows:

[0096] Step 1: System Calibration. This step is performed before the actual measurement to eliminate errors introduced by the system itself.

[0097] Step 1.1: Perform dark signal calibration. Under no-light conditions, acquire and calculate the average noise floor for each PD element, generating an n×n dark signal matrix S. dark :

[0098]

[0099] In the formula, S dark S is the dark signal matrix. dark (i,j) represents the average noise floor of the PD element located in the i-th row and j-th column, i=1,2,…,n, j=1,2,…,n.

[0100] Step 1.2: Under uniform illumination, detect the wavelength band for each target. Select a standard fluorescent plate, collect the response signal, and generate a flat-field response matrix. After normalization, the target detection bands are obtained. n×n flat field correction coefficient matrix :

[0101]

[0102] In the formula, Indicates target detection band The n×n flat field correction coefficient matrix, where k is the index of the target detection band, k=1,2,…,m; Indicates target detection band The flat field response matrix; mean() represents calculating the average value.

[0103] Step 2: Acquire multi-band data.

[0104] Step 2.1: Place the crystal 7 to be tested on the sample support and motion module 3, and plan a scanning sampling matrix of p rows × q columns (p and q are both integers greater than 1) covering the entire target detection area. The matrix coordinate set is as follows:

[0105]

[0106] In the formula, x p ,y q Represents a set of point coordinates.

[0107] Step 2.2: Drive filter wheel 4 to switch to the first target detection band. The corresponding filter. The sample carrier and motion module 3 is moved sequentially to each point (x) in the scanning array. p ,y q At each point, the signal from the PD array is acquired synchronously and stored as an n×n fluorescence signal matrix. .

[0108] Step 2.3: For all preset target detection bands (such as...) , ,..., ..., Repeat the scanning process in step 2.2 to obtain the fluorescence signal matrix of all target detection bands at all scanning sampling points.

[0109] Step 3: Data processing and indicator quantification.

[0110] Step 3.1, Signal Correction: For each scanning sampling point, the corrected fluorescence intensity matrix I (λ,k) Fluorescence signal matrix Dark signal matrix S dark Flat field correction coefficient matrix Both are n×n matrices. For any scanning sampling point in any target detection band, the corrected fluorescence intensity matrix I... (λ,k) The following matrix operations are used to obtain the result, where ⊘ represents element-wise division:

[0111]

[0112] In the formula, I (λ,k)This is the corrected fluorescence intensity matrix. S is the fluorescence signal matrix. dark For dark signal matrix, This is the flat field correction coefficient matrix.

[0113] Step 3.2, Surface Domain Fitting: For each target detection band, the set of fluorescence intensity data collected and corrected from all p×q scanning sampling points is represented as:

[0114]

[0115] Using a spatial interpolation algorithm, the discrete sampling points are reconstructed into a high-resolution fluorescence intensity distribution matrix within the same evaluation region on the crystal surface. The grid size of the high-resolution fluorescence intensity distribution matrix is ​​increased from p×q to N×N, with 1≤p,q≤N, to obtain intensity estimates for the gap region between measurement points and form a continuous surface distribution.

[0116]

[0117] In the formula, interpolate() represents the spatial interpolation process.

[0118] Step 3.3, Weighted Calculation. This includes:

[0119] Step 3.3.1: Calculate the absorption coefficient of the crystal under test using first-principles calculations. The absorption spectra of each target detection band were obtained. , representing the relative contribution of each wavelength to the overall absorption within the selected integration wavelength range Λ:

[0120]

[0121] In the formula, α represents the relative absorption contribution function obtained by normalizing the absorption coefficient in the k-th target detection band, which is used to characterize the relative weight of each wavelength in the target detection band to the total absorption; α(·) represents the absorption coefficient at wavelength "·"; Λ represents the preset integration wavelength range, which is the wavelength window of the target detection band.

[0122] Step 3.3.2: Calculate the effective energy Φ for each target detection band using the following formula. k :

[0123]

[0124] In the formula, Bk represents the set of discrete wavelength sampling points corresponding to the k-th target detection band; ∑ represents the summation operation; This represents the discrete wavelength sampling point within the k-th target detection band. The subscript f indicates the identifier of the corresponding emission spectrum or emission-side wavelength variable, used to distinguish it from other wavelength variables; H(·) represents the system transfer function of the test system at wavelength "·", used to characterize the transmission and collection capability of the optical link for that wavelength signal; R pd (·) indicates the spectral responsivity of the photodetector at wavelength "·"; △ This represents the wavelength interval in the set of discrete wavelength sampling points, and is used to weight and accumulate the contributions of each discrete wavelength sampling point.

[0125] The effective energy Φ of each target detection band k Normalization is performed to obtain the corresponding weight coefficients ω. k :

[0126]

[0127] In the formula, w k Φ represents the weighting coefficient corresponding to the k-th target detection band; k This represents the effective energy flux corresponding to the k-th target detection band; This represents the summation of the effective energy flux over all m target detection bands. This indicates that the sum of the weight coefficients for all m target detection bands is 1, where j is the summation index variable and m represents the total number of target detection bands participating in the weight allocation.

[0128] Step 3.4, Indicator Calculation. This includes:

[0129] Step 3.4.1: First, in the high-resolution intensity matrix... Above, define a sliding window submatrix W of size w × w. This window iterates through the high-resolution intensity matrix. Given all possible positions (p, q), at each position, extract the submatrix W. (p,q) And calculate its local discretization coefficient CV local (p,q):

[0130]

[0131] In the formula, std() represents the standard deviation of the sliding window submatrix W centered at (p,q), mean() represents the mean of the sliding window submatrix W centered at (p,q); W(p,q) represents the high-resolution intensity matrix obtained from the surface reconstruction. The value at coordinates (p, q).

[0132] Step 3.4.2: For each target detection band, calculate the local discretization coefficients (CVs) for all locations. local(p,q) are combined to form a new N cv ×N cv Matrix, 1≤p,q≤N cv The uniformity distribution matrix is ​​obtained. .

[0133] This matrix can intuitively reflect the local homogeneity of different regions of the crystal, and is represented as:

[0134]

[0135] Calculate the uniformity distribution matrix The arithmetic mean of all elements in the target detection band is used as the single-band uniformity index. , represented as:

[0136]

[0137] Step 3.4.3: Calculate the importance of each band based on first principles and set the weighting coefficient ω. k ,in This indicates that the weighted average of the single-band uniformity indices for all target detection bands is used to output the final comprehensive uniformity index U. final :

[0138]

[0139] Where m represents the total number of target detection bands.

[0140] The above specific embodiments are used to explain and illustrate the present invention, but not to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

[0141] The above description is only a preferred embodiment of the present invention. Therefore, all equivalent changes or modifications made to the structure, features and principles described in the claims of this patent application are included in the scope of this patent application.

Claims

1. A method for testing the uniformity of crystals using photoelectric properties, characterized in that, Includes the following steps: Step S1) Pre-set multiple different target detection bands. Under each preset target detection band, use excitation light to scan the target detection area of ​​the crystal under test to obtain a fluorescence signal matrix. The target detection band is the emission band corresponding to the secondary fluorescence of the crystal under test; Step S2) Preprocess the fluorescence signal matrix under each target detection band to obtain the fluorescence intensity distribution matrix, and extract the single-band uniformity parameter from the fluorescence intensity distribution matrix; In step S2, the process of extracting the single-band uniformity parameter includes: processing the fluorescence intensity distribution matrix using the sliding window method to obtain the local dispersion coefficients at each sampling window; combining the local dispersion coefficients at all sampling windows to obtain the uniformity distribution map matrix; and performing a global average on the uniformity distribution map matrix to obtain the single-band uniformity parameter. Step S3) The single-band uniformity parameters under all target detection bands are fused to obtain the crystal uniformity parameters as the test results.

2. The method for testing crystal uniformity using photoelectric properties according to claim 1, characterized in that: In step S1, the target detection band is switched by switching the filters on the filter wheel: when the excitation light irradiates each scanning sampling point of the crystal under test, the secondary fluorescence passes through the filter and generates fluorescence under the corresponding target detection band, which is collected by the detection module to obtain the fluorescence signal at the scanning sampling point; the fluorescence signals at all scanning sampling points in the target detection area form a fluorescence signal matrix.

3. The method for testing crystal uniformity using photoelectric properties according to claim 1, characterized in that: In step S1, multiple different target detection bands are selected based on the characteristic fluorescence emission peaks of the crystal to be tested.

4. The method for testing crystal uniformity using photoelectric properties according to claim 1, characterized in that: In step S2, the preprocessing includes signal correction processing and surface domain fitting processing.

5. The method for testing crystal uniformity using photoelectric properties according to claim 1, characterized in that: In step S3, the fusion process is: weighted calculation based on spectral weights.

6. The method for testing crystal uniformity using photoelectric properties according to claim 5, characterized in that: The process of obtaining the spectral weights for each target detection band includes: obtaining the absorption spectrum of the crystal under test in each target detection band based on the absorption coefficient curve of the crystal under test, and calculating the effective energy based on the absorption spectrum; normalizing the effective energy in each target detection band to obtain the spectral weights for each target detection band.

7. A testing device for testing the uniformity of crystals using photoelectric properties, characterized in that, include: Light source module (1), used to emit excitation light; A focusing lens group (2) is used to focus the excitation light onto the scanning sampling point of the crystal under test (7); The sample carrying and motion module (3) is used to carry the crystal to be tested (7) and move the crystal to be tested (7); A filter wheel (4) is arranged on the fluorescence receiving side of the crystal to be tested (7) and is equipped with at least one filter (41) for selecting the target detection band, which is used to separate the fluorescence signal of the target detection band corresponding to the filter (41). The detection module (5) is used to collect fluorescence in the target detection band corresponding to the filter (41) in the filter wheel (4); The data processing unit (6) is used to receive the fluorescence signal under the target detection band and process it according to the test method described in any one of claims 1 to 6 to obtain the test result; wherein, the process of extracting the single band uniformity parameter includes: processing the fluorescence intensity distribution matrix by the sliding window method to obtain the local discrete coefficients at each sampling window; combining the local discrete coefficients at all sampling windows to obtain the uniformity distribution map matrix; and performing a global average on the uniformity distribution map matrix to obtain the single band uniformity parameter.

8. The testing apparatus for testing crystal uniformity using photoelectric properties according to claim 7, characterized in that: The detection module (5) includes a photodetector array (52), which is a rectangular array composed of multiple photodiode elements (51). The photodiode elements (51) are silicon-based photodiodes or indium gallium arsenide-based photodiodes: when the secondary fluorescence of the crystal under test is mainly distributed in the range of 400nm to 1100nm, the photodiode element is a silicon-based photodiode; when the secondary fluorescence of the crystal under test is mainly distributed in the range of 900nm to 1700nm, the photodiode element is an indium gallium arsenide-based photodiode.

9. The testing apparatus for testing crystal uniformity using photoelectric properties according to claim 7, characterized in that: The crystal to be tested (7) includes an optical crystal and a laser window.

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