Photoanalysis and detection system for magnesium oxide single crystal target material

Abstract

The invention relates to the technical field of material analysis and detection, in particular to a magnesium oxide single crystal target material light analysis and detection system which comprises a spectrum acquisition module, a data processing module and a data analysis and calculation module. The spectrum acquisition module is used for irradiating the magnesium oxide single crystal sample through ultraviolet light and visible light respectively to obtain a sample ultraviolet light spectrum and a sample visible light spectrum, and synthesizing the sample ultraviolet light spectrum and the sample visible light spectrum into a first spectrum; the method comprises the following steps: extracting absorption peak spectral characteristics of vacancy defects of iron impurities in a magnesium oxide single crystal sample and absorption peak spectral characteristics of magnesium vacancies and oxygen vacancies in a magnesium oxide single crystal target material, and comparing the absorption peak spectral characteristics to find out the vacancy defects of the iron impurities which are mistakenly identified as the magnesium vacancies and the oxygen vacancies in the magnesium oxide single crystal target material; and the calculation error of the iron impurity concentration of the magnesium oxide single crystal target material caused by neglect of the iron impurities due to misjudgment is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of material analysis and detection, and in particular to a magnesium oxide single crystal target optical analysis and detection system. Background Art

[0002] As market demand for magnesium oxide single crystal targets continues to grow, industry requirements for their purity, size, and performance are becoming increasingly stringent, driving manufacturers to continuously improve their preparation processes to enhance product quality. Magnesium oxide targets are used to form the isolation layer in magnetic tunnel junctions (MTJs), a key component of magnetic random access memory (MRAM). Magnesium oxide single crystal targets contain a small amount of impurities. While impurities such as aluminum and silicon have no impact on their use, iron impurities pose a significant threat, introducing additional magnetic impurities that interfere with the magnetoresistance effect of the magnetic tunnel junction. However, there are a large number of intrinsic defects such as oxygen vacancies and magnesium vacancies in magnesium oxide single crystal targets. When oxygen vacancies and magnesium vacancies combine and interact with iron impurities, they will significantly affect the optical properties of iron impurities in the magnesium oxide single crystal target, especially the optical properties in the ultraviolet-visible light range. Due to technical limitations of existing ultraviolet and visible light analysis and detection systems, it is difficult to accurately detect the complex defect combination of iron impurities with oxygen vacancies and magnesium vacancies, and it is easy to miss or misjudge iron impurities, causing errors in the instrument's detection of the iron impurity content of the magnesium oxide single crystal target, resulting in the use of magnesium oxide single crystal targets that do not meet the standards for iron impurities, which reduces the read and write speed of the magnetic memory, increases energy consumption, and even affects the reliability of the memory. Summary of the Invention

[0003] (1) Technical problems to be solved The purpose of the present invention is to provide a magnesium oxide single crystal target optical analysis and detection system to solve the problem that the existing ultraviolet and visible light optical analysis and detection systems are subject to technical limitations and are difficult to accurately detect the complex defect combination of iron impurities and oxygen vacancies and magnesium vacancies, and are prone to missed or misjudgment of iron impurities, resulting in errors in the instrument's detection of the iron impurity content in magnesium oxide single crystal targets.

[0004] (2) Technical solution To achieve the above objectives, on the one hand, the present invention provides a magnesium oxide single crystal target optical analysis and detection system, including: a spectrum acquisition module, a data processing module and a data analysis and calculation module.

[0005] A spectrum acquisition module is used to irradiate a magnesium oxide single crystal sample with ultraviolet light and visible light respectively to obtain a sample ultraviolet light spectrum and a sample visible light spectrum, and synthesize the sample ultraviolet light spectrum and the sample visible light spectrum into a first spectrum; irradiate the magnesium oxide single crystal target to be tested with ultraviolet light to obtain a first defect spectrum, and irradiate the magnesium oxide single crystal target to be tested with two preset irradiation bands of visible light to obtain a second defect spectrum; the first spectrum is an absorption spectrum including vacancy defect absorption peaks of iron impurities in both ultraviolet and visible light bands; the first defect spectrum is an absorption spectrum of magnesium vacancy and oxygen vacancy absorption peaks in the ultraviolet band, and the second defect spectrum is an absorption spectrum of magnesium vacancy and oxygen vacancy absorption peaks in the two preset irradiation bands of visible light.

[0006] The data processing module is used to extract the characteristics of the first spectrum, the first defect spectrum and the second defect spectrum to obtain the first characteristic spectrum, the first defect characteristic spectrum and the second defect characteristic spectrum respectively.

[0007] A data analysis and calculation module is used to obtain first iron impurity position information through a first defect characteristic spectrum and a second defect characteristic spectrum; obtain first comparison data by comparing the first characteristic spectrum and the first defect characteristic spectrum; obtain second comparison data by comparing the first characteristic spectrum and the second defect characteristic spectrum; obtain second iron impurity position information through the overlap information of vacancy defects of iron impurities in the first comparison data and the second comparison data; obtain third iron impurity position information by superimposing the first iron impurity position information on the second iron impurity position information; obtain iron impurity concentration information in a magnesium oxide single crystal target material through the third iron impurity position information; and calculate the iron impurity content in the magnesium oxide single crystal target material through the iron impurity concentration information.

[0008] Furthermore, the data processing module is used to extract the characteristics of the first spectrum, the first defect spectrum, and the second defect spectrum to obtain the first characteristic spectrum, the first defect characteristic spectrum, and the second defect characteristic spectrum respectively, including: A first characteristic spectrum is obtained by performing feature extraction on the first spectrum using a first feature extraction method.

[0009] The first feature extraction method includes: The first spectrum is preprocessed by using Savitzky-Golay filter and Asymmetric Least Squares algorithm; and the preprocessed first spectrum is normalized.

[0010] A first spectrum matrix is ​​generated by taking fractional derivatives of the normalized first spectrum; and a sensitive band of vacancy defects of iron impurities is screened out by using spectrum coefficients in the first spectrum matrix.

[0011] The spectral characteristic parameters of the vacancy defect of the iron impurity in the first spectrum are extracted by a first feature extraction formula; the first feature extraction formula is: ; in, is the spectrum characteristic parameter of the vacancy defect of iron impurities; is the weight parameter; is the bias term; wavelength The absorption peak of the vacancy of the iron impurity at and The wavelength interval between them is set to 5-10nm; is the number of characteristic bands.

[0012] Furthermore, the data processing module is used to extract the characteristics of the first spectrum, the first defect spectrum, and the second defect spectrum to obtain the first characteristic spectrum, the first defect characteristic spectrum, and the second defect characteristic spectrum respectively, and the method further includes: The first defect characteristic spectrum and the second defect characteristic spectrum are respectively obtained by performing feature extraction on the first defect spectrum and the second defect spectrum using a second feature extraction method.

[0013] The second feature extraction method includes: The first defect information is obtained by using the first defect spectrum as the absorption spectrum of the magnesium vacancy and oxygen vacancy absorption peaks in the ultraviolet light band, and the second defect information is obtained by using the second defect spectrum as the absorption spectrum of the magnesium vacancy and oxygen vacancy absorption peaks in two preset irradiation bands of visible light.

[0014] The first defect information and the second defect information are respectively processed by polynomial fitting to obtain preprocessed first defect information and second defect information respectively; and the preprocessed first defect information and second defect information are normalized.

[0015] The first defect information and the second defect information after normalization are decomposed by J-layer wavelet using Symlet wavelet symN to obtain low-frequency approximation coefficients and high-frequency detail coefficients.

[0016] The wavelength range of the sensitive band of vacancy defects of iron impurities is selected, corresponding to a specific high-frequency sub-band in the wavelet decomposition; the energy distribution of each sub-band is calculated, and the sub-bands with abnormal energy shift are screened out, and the screened sub-bands with abnormal energy shift are recorded as characteristic sub-bands.

[0017] The characteristic subbands are subjected to threshold processing, and the noise components are removed by using a first threshold function or a second threshold function; the weights of the enhanced characteristic subbands are adjusted to highlight the characteristic components of the first defect information and the second defect information; the high-frequency detail coefficients in the original decomposition are replaced by the enhanced characteristic subbands, the low-frequency approximation coefficients are retained, and an inverse wavelet transform is performed to obtain the magnesium vacancy and oxygen vacancy information of the first defect information and the magnesium vacancy and oxygen vacancy information of the second defect information, respectively.

[0018] The first threshold function formula is: ; in, is the threshold parameter. When the absolute value of the coefficient is greater than When , it indicates that the oxygen vacancy or magnesium vacancy information in the first defect information and the second defect information is valid, and the original value is retained; otherwise, it is regarded as noise and set to 0; is the high-frequency detail coefficient after original wavelet decomposition; is the wavelet coefficient after threshold processing; is an indicator function, which takes the value 1 when , and 0 otherwise.

[0019] The second threshold function formula is: ; in, is a symbolic function that returns Symbols; is the maximum value function, that is, when When , otherwise output 0.

[0020] The first defect characteristic spectrum is obtained by extracting information of magnesium vacancies and oxygen vacancies in the first defect spectrum, and the second defect characteristic spectrum is obtained by extracting information of magnesium vacancies and oxygen vacancies in the second defect spectrum; the information of magnesium vacancies and oxygen vacancies includes spectral characteristic information and position information of magnesium vacancies, and spectral characteristic information and position information of oxygen vacancies.

[0021] Further, the method of obtaining first comparative data by comparing the first characteristic spectrum with the first defect characteristic spectrum; obtaining second comparative data by comparing the first characteristic spectrum with the second defect characteristic spectrum; and obtaining second iron impurity position information by overlapping information of vacancy defects of iron impurities in the first comparative data and the second comparative data includes: The absorption spectrum data of the absorption peak of the vacancy defect of the iron impurity in the first characteristic spectrum is collected and recorded as the reference spectrum.

[0022] The absorption spectrum data of the absorption peaks of magnesium vacancies and oxygen vacancies in the first defect characteristic spectrum are collected and recorded as the first defect spectrum; the absorption spectrum data of the absorption peaks of magnesium vacancies and oxygen vacancies in the second defect characteristic spectrum are collected and recorded as the second defect spectrum.

[0023] The peak value of the first preset wavelength band in the first defect spectrum is Gaussian fitted by the first differential spectrum. If the absolute value of the absorption peak of the magnesium vacancy or oxygen vacancy in the first defect spectrum is S times the absorption peak of the vacancy defect of the iron impurity in the reference spectrum, the magnesium vacancy or oxygen vacancy in the first defect spectrum is marked as a potential vacancy defect of the iron impurity, and the first comparative data is obtained; suppose the first differential spectrum is at wavelength The absorption peak at ,in The wavelength of the magnesium vacancy or oxygen vacancy in the first defect spectrum is The absorption peak at minus the wavelength of the vacancy defect at the iron impurity in the reference spectrum The absorption peak at .

[0024] Gaussian fitting is performed on the peak of the second preset wavelength band in the second defect spectrum through the second differential spectrum. If the absolute value of the absorption peak of the magnesium vacancy or oxygen vacancy in the second defect spectrum is T times the absorption peak of the vacancy defect of the iron impurity in the reference spectrum, the magnesium vacancy or oxygen vacancy in the second defect spectrum is marked as a potential vacancy defect of the iron impurity, and the second comparative data is obtained; suppose the second differential spectrum is at wavelength The absorption peak at ,in The wavelength of the magnesium vacancy or oxygen vacancy in the first defect spectrum is The absorption peak at minus the wavelength of the vacancy defect at the iron impurity in the reference spectrum The absorption peak at .

[0025] If the magnesium vacancy or oxygen vacancy at the same position in the first comparison data and the second comparison data is marked as a vacancy defect of potential iron impurities, the magnesium vacancy or oxygen vacancy is determined to be a vacancy defect of iron impurities; otherwise, it is determined to be a vacancy defect of non-iron impurities; the second iron impurity position information is obtained through the position information of the vacancy defect of iron impurities.

[0026] Furthermore, the 1.1-order fractional derivative is calculated by the first formula to highlight the change in the absorption peak of the vacancy defect of the iron impurity; the calculation formula of the first formula is: ; in, Represents a function Calculate the 1.1-order derivative; is the gamma function; is the wavelength interval; The length of the calculation window; To express minus wavelength interval The function value at .

[0027] Furthermore, the system also includes a light source module, which divides the light beam into two beams through a beam splitter prism or a reflector, and irradiates the magnesium oxide single crystal target at a first incident angle and a second incident angle respectively, wherein the first incident angle is perpendicular to the surface of the magnesium oxide single crystal target, and the second incident angle is at an angle to the surface of the magnesium oxide single crystal target; the spectrum acquisition module includes a first spectrum collector and a second spectrum collector, wherein the first spectrum collector is directly facing the surface of the magnesium oxide single crystal target, and the first spectrum collector captures the spectral characteristics of the combination of iron impurities and vacancies in the vertical direction; the second spectrum collector is arranged at a first angle to the surface of the magnesium oxide single crystal target, and the first angle is horizontally reversed to the second incident angle, and the second spectrum collector captures the spectral characteristics of the combination of iron impurities and vacancies in the non-vertical direction.

[0028] Furthermore, the light source module also emits a light beam of a specific wavelength.

[0029] Furthermore, the system also includes an environmental control module, which keeps the detection temperature constant.

[0030] Furthermore, the system also includes a sample processing module, which includes an ultraviolet ozone cleaning device, and the ultraviolet ozone cleaning device is used to remove organic matter from the surface of the magnesium oxide single crystal target.

[0031] Furthermore, the system also includes a calibration and database module, which is used for data acquisition and storage of magnesium oxide single crystal targets; the calibration and database module has a built-in spectral database of vacancy defects of iron impurities, and the spectral database includes multiple groups of spectral sample data of vacancy defect types of known iron impurities.

[0032] (3) Beneficial effects Compared with the prior art, the present invention has the following beneficial effects: 1. By extracting the absorption peak spectral characteristics of the vacancy defects of iron impurities in the magnesium oxide single crystal sample and the absorption peak spectral characteristics of magnesium vacancies and oxygen vacancies in the magnesium oxide single crystal target, and comparing them, the vacancy defects of iron impurities in the magnesium oxide single crystal target that were mistakenly identified as magnesium vacancies and oxygen vacancies can be found, so as to avoid the iron impurities being ignored due to misjudgment, which leads to errors in the calculation of the iron impurity concentration of the magnesium oxide single crystal target.

[0033] 2. The present invention identifies vacancy defects of iron impurities in magnesium oxide single crystal targets by irradiating them with ultraviolet and visible light, effectively reducing the cost of using the system and providing beneficial value for enterprises to reduce costs and increase efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without making any creative efforts.

[0035] Figure 1 This is a module block diagram of a magnesium oxide single crystal target optical analysis and detection system according to an embodiment of the present invention; Figure 2 This is a flow chart of a magnesium oxide single crystal target optical analysis and detection system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] Throughout this specification, references to "one embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment of the present invention. Therefore, appearances of the phrases "one embodiment," "an embodiment," "an example," or "an example" in various places throughout this specification are not necessarily all referring to the same embodiment or example. Furthermore, the particular features, structures, or characteristics may be combined in one or more embodiments or examples in any suitable combinations and / or subcombinations. Furthermore, it will be understood by those of ordinary skill in the art that the figures provided herein are for illustrative purposes only and are not necessarily drawn to scale. As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0038] Before giving examples, it is necessary to explain the application scenarios of the present invention. The magnesium oxide target used in magnetic random access memory (MRAM) may contain a variety of impurities, mainly iron, silicon, and aluminum. Among them, impurities such as aluminum and silicon have no effect on the use of magnesium oxide single crystal targets, while iron impurities have a serious impact on the use of magnesium oxide single crystal targets. The presence of iron impurities will introduce additional magnetic impurities, interfering with the magnetoresistance effect of the magnetic tunnel junction, resulting in reduced read and write speeds of the magnetic memory, increased energy consumption, and even affecting the reliability of the memory. Due to the presence of intrinsic defects such as oxygen vacancies and magnesium vacancies in magnesium oxide single crystal targets, when oxygen vacancies and magnesium vacancies combine and interact with iron impurities, they will significantly affect the optical properties of iron impurities in magnesium oxide single crystal targets, especially the optical properties under ultraviolet and visible light. For example, in ultraviolet and visible light spectrum detection, due to the presence of oxygen vacancies or magnesium vacancies around iron impurities, the absorption spectrum characteristics of the iron impurities combined with the intrinsic defects may overlap with the absorption spectrum characteristics of magnesium vacancies or oxygen vacancies. This absorption spectrum overlap may cause the iron impurities to be mistaken for oxygen vacancies or magnesium vacancies under optical identification, making it impossible to accurately identify the presence of iron impurities.

[0039] Example 1: Figure 1 As shown, this embodiment provides a magnesium oxide single crystal target optical analysis and detection system, including: a spectrum acquisition module, a data processing module and a data analysis and calculation module.

[0040] A spectrum acquisition module is used to irradiate a magnesium oxide single crystal sample with ultraviolet light and visible light respectively to obtain a sample ultraviolet light spectrum and a sample visible light spectrum, and synthesize the sample ultraviolet light spectrum and the sample visible light spectrum into a first spectrum; irradiate the magnesium oxide single crystal target to be tested with ultraviolet light to obtain a first defect spectrum, and irradiate the magnesium oxide single crystal target to be tested with two preset irradiation bands of visible light to obtain a second defect spectrum; the first spectrum is an absorption spectrum including vacancy defect absorption peaks of iron impurities in both ultraviolet and visible light bands; the first defect spectrum is an absorption spectrum of magnesium vacancy and oxygen vacancy absorption peaks in the ultraviolet band, and the second defect spectrum is an absorption spectrum of magnesium vacancy and oxygen vacancy absorption peaks in the two preset irradiation bands of visible light.

[0041] The data processing module is used to extract the characteristics of the first spectrum, the first defect spectrum and the second defect spectrum to obtain the first characteristic spectrum, the first defect characteristic spectrum and the second defect characteristic spectrum respectively.

[0042] A data analysis and calculation module is used to obtain first iron impurity position information through a first defect characteristic spectrum and a second defect characteristic spectrum; obtain first comparison data by comparing the first characteristic spectrum and the first defect characteristic spectrum; obtain second comparison data by comparing the first characteristic spectrum and the second defect characteristic spectrum; obtain second iron impurity position information through the overlap information of vacancy defects of iron impurities in the first comparison data and the second comparison data; obtain third iron impurity position information by superimposing the first iron impurity position information on the second iron impurity position information; obtain iron impurity concentration information in a magnesium oxide single crystal target material through the third iron impurity position information; and calculate the iron impurity content in the magnesium oxide single crystal target material through the iron impurity concentration information.

[0043] Specifically, the magnesium oxide single crystal sample and magnesium oxide single crystal target in the test are both circular thin slices with a thickness of 3 mm and a diameter of 15 cm. There are several known vacancy defects of iron impurities in the magnesium oxide single crystal sample. The absorption spectrum characteristics of the absorption peaks of the magnesium oxide single crystal sample under ultraviolet light and visible light can be obtained through the known vacancy defects of iron impurities. Since the visible light band is long, in order to ensure the accuracy of feature selection, it is preferred to divide the visible light band into at least 2 preset bands for feature extraction.

[0044] Furthermore, the data processing module is used to extract the characteristics of the first spectrum, the first defect spectrum, and the second defect spectrum to obtain the first characteristic spectrum, the first defect characteristic spectrum, and the second defect characteristic spectrum respectively, including: A first characteristic spectrum is obtained by performing feature extraction on the first spectrum using a first feature extraction method.

[0045] The first feature extraction method includes: The first spectrum is preprocessed using a Savitzky-Golay filter and an Asymmetric Least Squares algorithm; the preprocessed first spectrum is normalized. The Savitzky-Golay filter is a smoothing filtering method based on polynomial least squares in the time domain. It can effectively reduce the impact of random noise while preserving the main characteristics of the spectral signal. In the present invention, it is used to remove high-frequency random fluctuations caused by factors such as instrument noise and environmental interference during the spectrum acquisition process, making the spectral curve smoother. This avoids the excessive blurring of spectral details caused by traditional smoothing filtering methods (such as the moving average method) and ensures the shape and position accuracy of the absorption peak of the iron impurity vacancy defect. The ALS algorithm is specifically used for spectral baseline correction and can effectively solve the problem of baseline drift in spectral data caused by factors such as sample scattering and light source fluctuations. In the present invention, the ALS algorithm is used to accurately separate the true absorption signal from the baseline noise in the spectrum, making the absorption peak of the iron impurity vacancy defect stand out from the complex background signal. This eliminates the interference of baseline offset on the calculation of absorption peak intensity and position, providing a more reliable data foundation for subsequent feature extraction. Normalization is used to unify the data scale and map the preprocessed first spectral data to a uniform numerical range. This avoids feature extraction bias caused by significant intensity differences between spectral signals in different bands. It also ensures that the numerical range of the spectral features is consistent when subsequently comparing and analyzing the first and second defect characteristic spectra, thus avoiding calculation errors caused by different dimensions. Furthermore, during the subsequent fractional derivative calculation and feature parameter extraction processes, normalized data reduces the algorithm's sensitivity to extreme values, improving the model's robustness.

[0046] A first spectral matrix is ​​generated by taking fractional derivatives of the normalized first spectrum. The spectral coefficients in the first spectral matrix are used to identify sensitive wavelengths for vacancy defects in iron impurities. Further spectral features are extracted using methods such as fractional derivatives to highlight the characteristics of vacancy defects in iron impurities. Compared to integer derivatives, fractional derivatives can more precisely capture the slope changes of the spectral curve. The technical advantage of this method lies in enhancing the edge characteristics of the absorption peak of the iron impurity vacancy defect, making the start and end positions of the absorption peak more clearly distinguishable. This method amplifies the difference between the defect signature and the background signal, especially for iron impurity signals that overlap with the absorption peaks of magnesium and oxygen vacancies, which can be effectively distinguished by differences in slope changes. By analyzing the spectral coefficients of the first spectral matrix, the wavelength range most sensitive to iron impurity vacancy defects (e.g., a characteristic wavelength range between 5 and 10 nm) is identified. This allows for focusing on key spectral regions, reducing data interference from irrelevant bands, improving feature extraction efficiency, and enabling targeted analysis of absorption peaks unique to iron impurities (e.g., specific wavelengths in the ultraviolet and visible light bands), enhancing the relevance and accuracy of feature extraction.

[0047] The spectral characteristic parameters of the vacancy defect of the iron impurity in the first spectrum are extracted by a first feature extraction formula; the first feature extraction formula is: ; in, is the spectrum characteristic parameter of the vacancy defect of iron impurities; is the weight parameter; is the bias term; wavelength The absorption peak of the vacancy of the iron impurity at and The wavelength interval between them is set to 5-10nm; The number of characteristic bands is obtained by dividing the spectral characteristic parameters of vacancy defects of iron impurities into a training set and a validation set, thereby achieving iterative optimization of the feature extraction model. In the present invention, the training set is used to fit the weight parameter in the first feature extraction formula. and bias , so that the model can adaptively learn the spectral characteristic pattern of iron impurity vacancy defects. The validation set is used to evaluate the generalization ability of the model, avoid overfitting, and ensure the reliability of the model in actual detection. The first feature extraction formula integrates the absorption peak information of multiple characteristic bands by weighted summation, comprehensively considers the contribution of defect characteristics at different wavelengths, and avoids the one-sidedness of single band analysis. At the same time, the importance of each band is adjusted by the weight parameter, so that the spectral characteristic parameters output by the formula are It can accurately reflect the distribution characteristics of vacancy defects of iron impurities and provide a quantitative basis for subsequent calculation of iron impurity position information and concentration.

[0048] Furthermore, the data processing module is used to extract the characteristics of the first spectrum, the first defect spectrum, and the second defect spectrum to obtain the first characteristic spectrum, the first defect characteristic spectrum, and the second defect characteristic spectrum respectively, and the method further includes: The first defect characteristic spectrum and the second defect characteristic spectrum are respectively obtained by performing feature extraction on the first defect spectrum and the second defect spectrum using a second feature extraction method.

[0049] The second feature extraction method includes: The first defect information is obtained by using the first defect spectrum as the absorption spectrum of the magnesium vacancy and oxygen vacancy absorption peaks in the ultraviolet light band, and the second defect information is obtained by using the second defect spectrum as the absorption spectrum of the magnesium vacancy and oxygen vacancy absorption peaks in two preset irradiation bands of visible light.

[0050] The first defect information and the second defect information are processed separately by polynomial fitting to obtain the pre-processed first defect information and the second defect information respectively; the pre-processed first defect information and the second defect information are normalized. The fitting process in the present invention can smooth the random fluctuations of the spectral curve, and is particularly suitable for complex noise generated by lattice vibration, electron transition, etc. in the ultraviolet and visible light bands, making the absorption peak profiles of magnesium vacancies and oxygen vacancies clearer. The normalization process ensures that the characteristic parameters of the first spectrum and the first defect spectrum and the second defect spectrum are within the same numerical range, which is convenient for subsequent comparative analysis, and at the same time makes the spectral characteristics of magnesium vacancies and oxygen vacancies comparable when quantified, providing a standardized data basis for subsequent energy distribution calculations and characteristic sub-band screening.

[0051] The normalized first and second defect information is decomposed using the Symlet wavelet symN using J layers of wavelet decomposition to obtain low-frequency approximation coefficients and high-frequency detail coefficients. This preserves the overall trend and main energy distribution of the spectrum and reflects the macroscopic characteristics of the magnesium vacancy and oxygen vacancy absorption peaks (such as peak position and broadening). During testing, it was found that the wavelet basis Symlet 8 (sym8) was the best for denoising oxygen vacancy and magnesium vacancy defect spectra. The decomposition layer number J = 2 layers corresponds to the high-frequency subband range:

[0052] Energy anomaly judgment standard: When the sub-band energy exceeds 1.5 times the standard deviation (σ) of the average energy of the full spectrum, it is judged as a characteristic sub-band, that is, E>μ+1.5σ, where μ is the average energy of the full spectrum.

[0053] The wavelength range sensitive to vacancy defects in iron impurities was selected, corresponding to specific high-frequency subbands in the wavelet decomposition. The energy distribution of each subband was calculated, and subbands with abnormal energy shifts were screened. These subbands were recorded as characteristic subbands. The wavelength ranges sensitive to vacancy defects in iron impurities were selected as 240nm-280nm and 300nm-500nm. By selecting the wavelength range sensitive to vacancy defects in iron impurities, the overlapping absorption spectra of magnesium vacancies and oxygen vacancies were mapped to specific high-frequency subbands. The specific high-frequency subbands were then combined with the wavelength ranges sensitive to vacancy defects in iron impurities (240nm-280nm and 480nm-500nm) after J-layer wavelet decomposition of the defect spectrum using the Symlet wavelet (symN) method. The energy distribution analysis was then used to locate abnormal signals. The multi-resolution properties of wavelet decomposition were then used to separate the high-frequency signatures caused by iron impurity vacancy defects from the low-frequency signatures of magnesium and oxygen vacancies, providing a basis for subsequent energy screening. The sub-bands with abnormal energy shifts were directly associated with the absorption peak positions of iron impurity vacancy defects, providing precise wavelength positioning for subsequent identification of potential iron impurities.

[0054] The characteristic subbands are subjected to threshold processing, and the noise components are removed by using a first threshold function or a second threshold function; the weights of the enhanced characteristic subbands are adjusted to highlight the characteristic components of the first defect information and the second defect information; the high-frequency detail coefficients in the original decomposition are replaced by the enhanced characteristic subbands, the low-frequency approximation coefficients are retained, and an inverse wavelet transform is performed to obtain the magnesium vacancy and oxygen vacancy information of the first defect information and the magnesium vacancy and oxygen vacancy information of the second defect information, respectively.

[0055] The first threshold function formula is: ; in, is the threshold parameter. When the absolute value of the coefficient is greater than When , it indicates that the oxygen vacancy or magnesium vacancy information in the first defect information and the second defect information is valid, and the original value is retained; otherwise, it is regarded as noise and set to 0; is the high-frequency detail coefficient after original wavelet decomposition; is the wavelet coefficient after threshold processing; is an indicator function, which takes the value 1 when , and 0 otherwise.

[0056] The second threshold function formula is: ; in, is a symbolic function that returns Symbols; is the maximum value function, that is, when When the output , otherwise the output is 0. The first threshold function can strictly eliminate noise coefficients below the threshold, retaining strong defect signals. It is suitable for scenarios with high signal-to-noise ratios and preventing weak iron impurity signals from being overwhelmed by noise. The second threshold function can smoothly shrink coefficients above the threshold, reducing feature distortion while removing noise. It is suitable for spectral data with complex noise.

[0057] The first defect characteristic spectrum is obtained by extracting information of magnesium vacancies and oxygen vacancies in the first defect spectrum, and the second defect characteristic spectrum is obtained by extracting information of magnesium vacancies and oxygen vacancies in the second defect spectrum; the information of magnesium vacancies and oxygen vacancies includes spectral characteristic information and position information of magnesium vacancies, and spectral characteristic information and position information of oxygen vacancies.

[0058] Further, the method of obtaining first comparative data by comparing the first characteristic spectrum with the first defect characteristic spectrum; obtaining second comparative data by comparing the first characteristic spectrum with the second defect characteristic spectrum; and obtaining second iron impurity position information by overlapping information of vacancy defects of iron impurities in the first comparative data and the second comparative data includes: Collect absorption spectrum data for the absorption peak of the iron impurity vacancy defect in the first characteristic spectrum and record it as the reference spectrum. Determine the position, intensity, and shape (e.g., absorption peak wavelength, half-width, etc.) of the characteristic absorption peak of the iron impurity vacancy defect in the ultraviolet and visible light bands to use as a benchmark for determining whether the magnesium and oxygen vacancies are iron impurities.

[0059] The absorption spectrum data of the absorption peaks of magnesium vacancies and oxygen vacancies in the first defect characteristic spectrum are collected and recorded as the first defect spectrum; the absorption spectrum data of the absorption peaks of magnesium vacancies and oxygen vacancies in the second defect characteristic spectrum are collected and recorded as the second defect spectrum.

[0060] The peak value of the first preset wavelength band in the first defect spectrum is Gaussian fitted by the first differential spectrum. If the absolute value of the absorption peak of the magnesium vacancy or oxygen vacancy in the first defect spectrum is S times the absorption peak of the vacancy defect of the iron impurity in the reference spectrum, the magnesium vacancy or oxygen vacancy in the first defect spectrum is marked as a potential vacancy defect of the iron impurity, and the first comparative data is obtained; suppose the first differential spectrum is at wavelength The absorption peak at ,in The wavelength of the magnesium vacancy or oxygen vacancy in the first defect spectrum is The absorption peak at minus the wavelength of the vacancy defect at the iron impurity in the reference spectrum The absorption peak at .

[0061] Gaussian fitting is performed on the peak of the second preset wavelength band in the second defect spectrum through the second differential spectrum. If the absolute value of the absorption peak of the magnesium vacancy or oxygen vacancy in the second defect spectrum is T times the absorption peak of the vacancy defect of the iron impurity in the reference spectrum, the magnesium vacancy or oxygen vacancy in the second defect spectrum is marked as a potential vacancy defect of the iron impurity, and the second comparative data is obtained; suppose the second differential spectrum is at wavelength The absorption peak at ,in The wavelength of the magnesium vacancy or oxygen vacancy in the first defect spectrum is The absorption peak at minus the wavelength of the vacancy defect at the iron impurity in the reference spectrum The absorption peak at .

[0062] Specifically, Gaussian fitting smooths noise in the differential spectrum. Parametric fitting is also performed on the peaks in the first preset band (240-280nm) and the second preset band (300-500nm), precisely determining the center position and intensity of the absorption peaks and improving the accuracy of potential iron impurity identification. By setting multiples (S and T) of the absorption peak's absolute value, the screening criteria can be dynamically adjusted based on the degree of spectral overlap between iron impurities and intrinsic defects. When S = 0.8 in the first preset band (240-280nm), or when T = 1.2 in the second preset band (300-500nm), magnesium or oxygen vacancies are flagged as potential defects only when their intensity is highly similar to that of the vacancy defect absorption peak of the iron impurity, thus avoiding mislabeling due to slight overlap.

[0063] If the magnesium vacancy or oxygen vacancy at the same position in both the first and second comparison data is marked as a potential vacancy defect of an iron impurity, the magnesium vacancy or oxygen vacancy is determined to be a vacancy defect of an iron impurity; otherwise, it is determined to be a vacancy defect of a non-iron impurity; the second iron impurity position information is obtained through the position information of the vacancy defect of the iron impurity. If a magnesium vacancy or oxygen vacancy is marked as a potential iron impurity only in a single band of ultraviolet or visible light, it may be due to accidental spectral overlap in that band; and when the comparison data of the two bands both mark the defect at the same position, it indicates that the spectral characteristics of the defect are consistent with those of iron impurities in multiple bands, greatly improving the reliability of the judgment. Overlap verification can accurately locate the position of the composite defect formed by iron impurities, magnesium vacancies, and oxygen vacancies, providing accurate spatial distribution data for subsequent calculation of iron impurity concentration.

[0064] Furthermore, the 1.1-order fractional derivative is calculated by the first formula to highlight the change in the absorption peak of the vacancy defect of the iron impurity; the calculation formula of the first formula is: ; in, Represents a function Calculate the 1.1-order derivative; is the gamma function; is the wavelength interval; The length of the calculation window; To express minus wavelength interval The function value at .

[0065] Specifically, compared with integer-order derivatives (such as first-order and second-order derivatives), the 1.1-order fractional derivative can capture the nonlinear changes of the spectral curve more accurately. The specific effects include that the iron impurity vacancy defect will cause the spectrum to have a sudden slope mutation at a specific wavelength (such as the rising / falling edge of the absorption peak). The fractional derivative can amplify the mutation signal, making the defect characteristics significantly different from the smooth spectrum of intrinsic defects (magnesium vacancies and oxygen vacancies). At the same time, when the absorption peaks of iron impurities partially overlap with those of magnesium vacancies and oxygen vacancies, the fractional derivative can effectively separate the mixed peaks through the difference in slope change rate (such as the slope of the iron impurity peak changes steeper).

[0066] Furthermore, the system also includes a light source module, which divides the light beam into two beams through a beam splitter prism or a reflector, and irradiates the magnesium oxide single crystal target at a first incident angle and a second incident angle respectively, wherein the first incident angle is perpendicular to the surface of the magnesium oxide single crystal target, and the second incident angle is at an angle to the surface of the magnesium oxide single crystal target; the spectrum acquisition module includes a first spectrum collector and a second spectrum collector, wherein the first spectrum collector is directly facing the surface of the magnesium oxide single crystal target, and the first spectrum collector captures the spectral characteristics of the combination of iron impurities and vacancies in the vertical direction; the second spectrum collector is arranged at a first angle to the surface of the magnesium oxide single crystal target, and the first angle is horizontally reversed to the second incident angle, and the second spectrum collector captures the spectral characteristics of the combination of iron impurities and vacancies in the non-vertical direction.

[0067] Furthermore, the light source module also emits a light beam of a specific wavelength.

[0068] Furthermore, the system also includes an environmental control module that maintains a constant detection temperature. Temperature fluctuations during magnesium oxide single crystal target testing can exacerbate target lattice vibrations, causing spectral broadening or peak shifts. Maintaining a constant temperature (e.g., 25°C ± 0.5°C) ensures repeatability and reliability of the spectral signal.

[0069] Furthermore, the system also includes a sample processing module, which includes an ultraviolet ozone cleaning device for removing organic matter from the surface of the magnesium oxide single crystal target. Ultraviolet ozone can decompose organic residues on the surface of the target (such as grease and polymers introduced during cutting and grinding) into and , to avoid the coverage of the iron impurity spectrum by the organic absorption peak (such as the organic absorption at 200-300nm in the ultraviolet band). At the same time, the strong oxidizing property of ozone is used to remove the iron impurities (Fe +2 ) is oxidized to (Fe +3 ), solve the problem of different absorption peak characteristics of iron impurities at different price levels.

[0070] Furthermore, the system also includes a calibration and database module, which is used for data acquisition and storage of magnesium oxide single crystal targets; the calibration and database module has a built-in spectral database of vacancy defects of iron impurities, and the spectral database includes multiple groups of spectral sample data of vacancy defect types of known iron impurities.

[0071] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0072] Finally, it should be noted that although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A magnesium oxide single crystal target optical analysis detection system, characterized in that: The system includes: a spectrum acquisition module, a data processing module and a data analysis and calculation module; A spectrum acquisition module is configured to irradiate a magnesium oxide single crystal sample with ultraviolet light and visible light respectively to obtain a sample ultraviolet light spectrum and a sample visible light spectrum, and synthesize the sample ultraviolet light spectrum and the sample visible light spectrum into a first spectrum; irradiate a magnesium oxide single crystal target to be tested with ultraviolet light to obtain a first defect spectrum, and irradiate the magnesium oxide single crystal target to be tested with two preset irradiation bands of visible light to obtain a second defect spectrum; the first spectrum is an absorption spectrum comprising vacancy defect absorption peaks of iron impurities in both ultraviolet and visible light bands; the first defect spectrum is an absorption spectrum of magnesium vacancies and oxygen vacancies absorption peaks in the ultraviolet light band, and the second defect spectrum is an absorption spectrum of magnesium vacancies and oxygen vacancies absorption peaks in the two preset irradiation bands of visible light; A data processing module, configured to extract features of the first spectrum, the first defect spectrum, and the second defect spectrum to obtain a first characteristic spectrum, a first defect characteristic spectrum, and a second defect characteristic spectrum, respectively; A data analysis and calculation module is used to obtain first iron impurity position information through a first defect characteristic spectrum and a second defect characteristic spectrum; obtain first comparison data by comparing the first characteristic spectrum and the first defect characteristic spectrum; obtain second comparison data by comparing the first characteristic spectrum and the second defect characteristic spectrum; obtain second iron impurity position information through the overlap information of vacancy defects of iron impurities in the first comparison data and the second comparison data; obtain third iron impurity position information by superimposing the first iron impurity position information on the second iron impurity position information; obtain iron impurity concentration information in a magnesium oxide single crystal target material through the third iron impurity position information; and calculate the iron impurity content in the magnesium oxide single crystal target material through the iron impurity concentration information.

2. The optical analysis and detection system for magnesium oxide single crystal target according to claim 1, characterized in that: The data processing module is used to extract the characteristics of the first spectrum, the first defect spectrum, and the second defect spectrum to obtain the first characteristic spectrum, the first defect characteristic spectrum, and the second defect characteristic spectrum respectively, including: Performing feature extraction on the first spectrum using a first feature extraction method to obtain a first feature spectrum; The first feature extraction method includes: preprocessing the first spectrum using a Savitzky-Golay filter and an Asymmetric Least Squares algorithm; and performing normalization processing on the preprocessed first spectrum; The first spectrum after normalization is generated by fractional derivatives; the sensitive band of vacancy defects of iron impurities is screened out by spectral coefficients in the first spectrum matrix; The spectral characteristic parameters of the vacancy defect of the iron impurity in the first spectrum are extracted by a first feature extraction formula; the first feature extraction formula is: ； in, is the spectrum characteristic parameter of the vacancy defect of iron impurities; is the weight parameter; is the bias term; is the wavelength The absorption peak of the iron impurity vacancy at and The wavelength interval between them is set to 5-10nm; is the number of characteristic bands.

3. The optical analysis and detection system for magnesium oxide single crystal target according to claim 2, characterized in that: The method for extracting the features of the first spectrum, the first defect spectrum, and the second defect spectrum by the data processing module to obtain the first characteristic spectrum, the first defect characteristic spectrum, and the second defect characteristic spectrum respectively further includes: Performing feature extraction on the first defect spectrum and the second defect spectrum using a second feature extraction method to obtain a first defect characteristic spectrum and a second defect characteristic spectrum respectively; The second feature extraction method includes: The first defect information is obtained by using the first defect spectrum as an absorption spectrum of the magnesium vacancy and oxygen vacancy absorption peaks in the ultraviolet light band, and the second defect information is obtained by using the second defect spectrum as an absorption spectrum of the magnesium vacancy and oxygen vacancy absorption peaks in two preset irradiation bands of visible light; The first defect information and the second defect information are respectively processed by polynomial fitting to obtain preprocessed first defect information and second defect information respectively; and the preprocessed first defect information and second defect information are normalized; Performing J-layer wavelet decomposition on the normalized first defect information and the second defect information using Symlet wavelet symN to obtain low-frequency approximation coefficients and high-frequency detail coefficients; Select the wavelength range of the sensitive band of vacancy defects of iron impurities, and the corresponding specific high-frequency sub-band in the wavelet decomposition; calculate the energy distribution of each sub-band, screen the sub-bands with abnormal energy shift, and record the screened sub-bands with abnormal energy shift as characteristic sub-bands; Threshold processing is performed on the characteristic subband to remove noise components using a first threshold function or a second threshold function; weight adjustment is performed on the enhanced characteristic subband to highlight the characteristic components of the first defect information and the second defect information; high-frequency detail coefficients in the original decomposition are replaced by the enhanced characteristic subband, low-frequency approximate coefficients are retained, and an inverse wavelet transform is performed to obtain magnesium vacancy and oxygen vacancy information in the first defect information and magnesium vacancy and oxygen vacancy information in the second defect information, respectively; The first threshold function formula is: ； in, is the threshold parameter. When the absolute value of the coefficient is greater than When , it indicates that the oxygen vacancy or magnesium vacancy information in the first defect information and the second defect information is valid, and the original value is retained; otherwise, it is regarded as noise and set to 0; is the high-frequency detail coefficient after original wavelet decomposition; is the wavelet coefficient after threshold processing; is an indicator function, which takes the value 1 when , otherwise 0; The second threshold function formula is: ； in, is a symbolic function that returns Symbols; is the maximum value function, that is, when When , otherwise output 0; The first defect characteristic spectrum is obtained by extracting information of magnesium vacancies and oxygen vacancies in the first defect spectrum, and the second defect characteristic spectrum is obtained by extracting information of magnesium vacancies and oxygen vacancies in the second defect spectrum; the information of magnesium vacancies and oxygen vacancies includes spectral characteristic information and position information of magnesium vacancies, and spectral characteristic information and position information of oxygen vacancies.

4. The optical analysis and detection system for magnesium oxide single crystal target according to claim 3, characterized in that: A method for obtaining first comparative data by comparing a first characteristic spectrum with a first defect characteristic spectrum; obtaining second comparative data by comparing the first characteristic spectrum with the second defect characteristic spectrum; and obtaining second iron impurity position information by using overlap information of vacancy defects of iron impurities in the first comparative data and the second comparative data includes: Collecting absorption spectrum data of the absorption peak of the vacancy defect of the iron impurity in the first characteristic spectrum and recording it as a reference spectrum; Collecting absorption spectrum data of the absorption peaks of magnesium vacancies and oxygen vacancies in the first defect characteristic spectrum, which is recorded as the first defect spectrum; collecting absorption spectrum data of the absorption peaks of magnesium vacancies and oxygen vacancies in the second defect characteristic spectrum, which is recorded as the second defect spectrum; The peak value of the first preset wavelength band in the first defect spectrum is Gaussian fitted by the first differential spectrum. If the absolute value of the absorption peak of the magnesium vacancy or oxygen vacancy in the first defect spectrum is S times the absorption peak of the vacancy defect of the iron impurity in the reference spectrum, the magnesium vacancy or oxygen vacancy in the first defect spectrum is marked as a potential vacancy defect of the iron impurity, and the first comparative data is obtained; suppose the first differential spectrum is at wavelength The absorption peak at ,in The wavelength of the magnesium vacancy or oxygen vacancy in the first defect spectrum is The absorption peak at minus the wavelength of the vacancy defect at the iron impurity in the reference spectrum The absorption peak at Gaussian fitting is performed on the peak of the second preset wavelength band in the second defect spectrum through the second differential spectrum. If the absolute value of the absorption peak of the magnesium vacancy or oxygen vacancy in the second defect spectrum is T times the absorption peak of the vacancy defect of the iron impurity in the reference spectrum, the magnesium vacancy or oxygen vacancy in the second defect spectrum is marked as a potential vacancy defect of the iron impurity, and the second comparative data is obtained; suppose the second differential spectrum is at wavelength The absorption peak at ,in The wavelength of the magnesium vacancy or oxygen vacancy in the first defect spectrum is The absorption peak at minus the wavelength of the vacancy defect at the iron impurity in the reference spectrum The absorption peak at If the magnesium vacancy or oxygen vacancy at the same position in the first comparison data and the second comparison data is marked as a vacancy defect of potential iron impurities, the magnesium vacancy or oxygen vacancy is determined to be a vacancy defect of iron impurities; otherwise, it is determined to be a vacancy defect of non-iron impurities; the second iron impurity position information is obtained through the position information of the vacancy defect of iron impurities.

5. The optical analysis and detection system for magnesium oxide single crystal target according to claim 2, characterized in that: The 1.1-order fractional derivative is calculated by the first formula to highlight the change in the absorption peak of the vacancy defect of the iron impurity; the calculation formula of the first formula is: ； in, Represents a function Calculate the 1.1-order derivative; is the gamma function; is the wavelength interval; The length of the calculation window; To express minus wavelength interval The function value at .

6. The optical analysis and detection system for magnesium oxide single crystal target according to claim 1, characterized in that: The system also includes a light source module, which splits a light beam into two beams through a beam splitter prism or a reflector, and irradiates the magnesium oxide single crystal target at a first incident angle and a second incident angle, respectively, wherein the first incident angle is perpendicular to the surface of the magnesium oxide single crystal target, and the second incident angle forms an angle with the surface of the magnesium oxide single crystal target; the spectrum acquisition module includes a first spectrum collector and a second spectrum collector, wherein the first spectrum collector faces the surface of the magnesium oxide single crystal target, and the first spectrum collector captures the spectral characteristics of the combination of iron impurities and vacancies in the vertical direction; The second spectrum collector is arranged at a first angle to the surface of the magnesium oxide single crystal target, the first angle is horizontally reversed to the second incident angle, and the second spectrum collector captures the spectral characteristics of the combination of iron impurities and vacancies in a non-vertical direction.

7. The optical analysis and detection system for magnesium oxide single crystal target according to claim 6, characterized in that: The light source module also emits a light beam with a specific wavelength.

8. The optical analysis and detection system for magnesium oxide single crystal target according to claim 7, characterized in that: The system further comprises an environment control module, which keeps the detected temperature constant.

9. The optical analysis and detection system for magnesium oxide single crystal target according to claim 8, characterized in that: The system further comprises a sample processing module, which comprises an ultraviolet ozone cleaning device, and the ultraviolet ozone cleaning device is used to remove organic matter from the surface of the magnesium oxide single crystal target.

10. The optical analysis and detection system for magnesium oxide single crystal target according to claim 9, characterized in that: The system also includes a calibration and database module, which is used for data acquisition and storage of magnesium oxide single crystal targets; the calibration and database module has a built-in spectral database of vacancy defects of iron impurities, and the spectral database includes multiple groups of spectral sample data of vacancy defect types of known iron impurities.

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