Method and equipment for measuring optical parameters of functional metal glass

Abstract

The invention relates to the technical field of optical parameter analysis, in particular to a method and equipment for measuring optical parameters of functional metal glass, and solves the problems in the prior art that periodic texture diffraction and process flow deformation of a molded surface of the functional metal glass are caused; the measurement of the intrinsic optical parameters of the material is not accurate, and the process rheological state parameters cannot be obtained. The method comprises the following steps: acquiring a multi-angle reflectivity data set of the surface of a functional metal glass element to be detected; according to the multi-angle reflectivity data set, separating a strong diffraction spectrum and a weak diffraction spectrum, and determining a diffraction residue ratio; determining geometric deformation parameters of the surface of the functional metal glass element to be detected according to the strong diffraction spectrum, and constructing a simulated diffraction waveform according to the geometric deformation parameters; and determining the intrinsic optical parameters of the functional metal glass according to the weak diffraction spectrum, the diffraction residual ratio and the simulated diffraction waveform.

Description

Technical Field

[0001] This invention relates to the field of optical parameter analysis technology, specifically to a method and apparatus for measuring the optical parameters of functional metallic glasses. Background Technology

[0002] Functional metallic glasses possess excellent thermoplastic molding capabilities in the supercooled liquid phase region, making them ideal materials for manufacturing precision optical components with subwavelength periodic microstructures. The high-temperature molding and rapid cooling manufacturing process can accurately replicate the grating texture on the mold surface onto the metallic glass surface, making them widely used in the field of precision optics.

[0003] In actual manufacturing, the periodic microstructure on the surface of metal-glass components induces a strong optical anisotropic diffraction effect, making the measured reflection spectrum a mixed response of intrinsic absorption and structural geometric scattering. At the same time, the significant volume shrinkage and non-uniform rheological shearing effect during cooling will cause the surface texture period to be nonlinearly reduced relative to the mold design value, and the micro waveform will also show asymmetric distortion.

[0004] Existing measurement techniques struggle to effectively distinguish between absorption loss caused by material crystallization and diffraction loss caused by surface texture without damaging the sample. Furthermore, traditional measurement models cannot accurately describe the special optical artifacts induced by the process, which not only easily leads to misjudging good products as material defects but also fails to obtain rheological state parameters reflecting the quality of the molding process from spectral data. Summary of the Invention

[0005] To address the technical problems in existing technologies where the periodic texture diffraction of the molded surface of functional metallic glasses leads to inaccurate measurement of intrinsic optical parameters and the inability to obtain process rheological state parameters, the present invention aims to provide a method and apparatus for measuring the optical parameters of functional metallic glasses. The specific technical solution adopted is as follows: In a first aspect, a method for determining the optical parameters of a functional metallic glass is provided, comprising: acquiring a multi-angle reflectance dataset of the surface of the functional metallic glass element to be tested; separating strong diffraction spectra and weak diffraction spectra from the multi-angle reflectance dataset and determining the diffraction residue ratio; the strong diffraction spectrum is the reflectance spectrum with the largest standard deviation of spectral intensity in the multi-angle reflectance dataset, and the weak diffraction spectrum is the reflectance spectrum with the smallest standard deviation of spectral intensity in the multi-angle reflectance dataset; the diffraction residue ratio is used to characterize the amplitude proportion of the residual geometric scattering signal in the weak diffraction spectrum; determining the geometric deformation parameters of the surface of the functional metallic glass element to be tested based on the strong diffraction spectrum, and constructing a simulated diffraction waveform based on the geometric deformation parameters; the geometric deformation parameters are used to quantify the size and morphological changes of the surface periodic structure caused by the molding process; the simulated diffraction waveform is used to characterize the diffraction energy distribution caused only by the surface periodic structure; and determining the intrinsic optical parameters of the functional metallic glass based on the weak diffraction spectrum, the diffraction residue ratio, and the simulated diffraction waveform.

[0006] Based on the above technical solution, in the method for measuring the optical parameters of functional metallic glass provided by this invention, by acquiring a multi-angle reflectance dataset and separating strong diffraction spectra, weak diffraction spectra, and diffraction residual ratios, the surface geometric deformation parameters induced by the molding process are determined by combining the strong diffraction spectra and a simulated diffraction waveform is constructed. Finally, the intrinsic optical parameters are determined based on the weak diffraction spectra, diffraction residual ratios, and simulated diffraction waveforms. This effectively decouples the interference of surface periodic texture diffraction and process flow deformation on the measurement of the intrinsic optical parameters of the material, and realizes the accurate measurement of intrinsic optical parameters without destroying the sample. At the same time, it quantifies the process-related surface structure size and morphological changes, providing a key foundation for subsequent process quality assessment.

[0007] In conjunction with the first aspect above, in one possible implementation, the method for separating strong diffraction spectra and weak diffraction spectra and determining the diffraction residue ratio specifically includes: determining the spectral intensity standard deviation of the reflectance spectrum corresponding to each azimuth angle in the multi-angle reflectance dataset; determining the reflectance spectrum corresponding to the maximum spectral intensity standard deviation in the multi-angle reflectance dataset as the strong diffraction spectrum; determining the reflectance spectrum corresponding to the minimum spectral intensity standard deviation in the multi-angle reflectance dataset as the weak diffraction spectrum; and determining the diffraction residue ratio based on the ratio of the minimum spectral intensity standard deviation to the maximum spectral intensity standard deviation.

[0008] In conjunction with the first aspect above, in one possible implementation, the aforementioned geometric deformation parameters include a period reduction rate and a waveform skew factor; the period reduction rate is used to characterize the overall dimensional shrinkage of the surface periodic structure; the waveform skew factor is used to characterize the shape asymmetry of a single periodic unit of the surface periodic structure; the method for determining the geometric deformation parameters of the surface of the functional metal glass element under test based on the strong diffraction spectrum specifically includes: determining a theoretical valley position set based on a preset mold period value or period information extracted from the strong diffraction spectrum; extracting a measured valley position set from the strong diffraction spectrum; establishing a mapping relationship between the measured valley positions and the theoretical valley positions by minimizing the overall matching deviation between the measured valley position set and the theoretical valley position set; determining the period reduction rate based on the ratio of the measured valley positions matched in the mapping relationship to the theoretical valley positions; and determining the waveform skew factor based on the difference in the left and right half widths of the measured valleys corresponding to the mapping relationship in the strong diffraction spectrum.

[0009] In conjunction with the first aspect above, in one possible implementation, the method for determining the set of theoretical valley positions based on the preset value of the mold cycle specifically includes: determining the theoretical wavelength of the multi-order diffraction valleys appearing within the preset wavelength range based on the preset value of the mold cycle and the fixed incident angle used during measurement, and forming the set of theoretical valley positions based on the grating diffraction relationship.

[0010] In conjunction with the first aspect above, in one possible implementation, the method for determining the waveform skew factor based on the difference in the left and right half-widths of the measured valley corresponding to the mapping relationship in the strong diffraction spectrum specifically includes: for each measured valley in the mapping relationship, determining the wavelengths corresponding to the intensity rising to half the valley depth on both sides of the center position of the measured valley in the strong diffraction spectrum; determining the waveform skew factor corresponding to the measured valley based on the ratio of the difference between the right wavelength and the center wavelength, and the difference between the center wavelength and the left wavelength, to the total left and right widths of the measured valley.

[0011] In conjunction with the first aspect above, in one possible implementation, the method for constructing a simulated diffraction waveform based on geometric deformation parameters specifically includes: constructing multiple single-peak profile functions based on the measured position and depth of the valley and the corresponding waveform skew factor in the mapping relationship; the linewidth of each single-peak profile function is modulated by the full width of the corresponding measured valley and the waveform skew factor; and superimposing the constructed multiple single-peak profile functions to generate a simulated diffraction waveform.

[0012] In conjunction with the first aspect above, in one possible implementation, the method for determining the intrinsic optical parameters of a functional metallic glass based on a weak diffraction spectrum, a diffraction residual ratio, and a simulated diffraction waveform specifically includes: determining a corrected reflection spectrum based on a weighted sum of the weak diffraction spectrum, the diffraction residual ratio, and the simulated diffraction waveform; and determining the refractive index and extinction coefficient of the functional metallic glass as intrinsic optical parameters by fitting the corrected reflection spectrum using an amorphous dispersion model.

[0013] In conjunction with the first aspect above, in one possible implementation, the method further includes: generating molding quality evaluation information for the functional metallic glass element under test based on at least one of the period reduction rate, waveform skew factor, and intrinsic optical parameters.

[0014] In conjunction with the first aspect above, in one possible implementation, the method for obtaining the multi-angle reflectance dataset of the surface of the functional metal glass element under test specifically includes: rotating the functional metal glass element under test around the surface normal direction at a fixed incident angle, and collecting the reflectance spectrum of the surface within a preset wavelength range at multiple continuous or discrete azimuth angle positions to form a multi-angle reflectance dataset.

[0015] Secondly, an optical parameter measuring device for functional metallic glasses is provided, comprising: a data acquisition unit for acquiring a multi-angle reflectance dataset of the surface of the functional metallic glass element under test; a signal separation unit for separating strong diffraction spectra and weak diffraction spectra from the multi-angle reflectance dataset and determining the diffraction residue ratio; the strong diffraction spectrum is the reflectance spectrum with the largest standard deviation of spectral intensity in the multi-angle reflectance dataset, and the weak diffraction spectrum is the reflectance spectrum with the smallest standard deviation of spectral intensity in the multi-angle reflectance dataset; the diffraction residue ratio is used to characterize the amplitude proportion of the geometric scattering signal remaining in the weak diffraction spectrum; a geometric modeling unit for determining the geometric deformation parameters of the surface of the functional metallic glass element under test based on the strong diffraction spectrum and constructing a simulated diffraction waveform based on the geometric deformation parameters; the geometric deformation parameters are used to quantify the size and morphological changes of the surface periodic structure caused by the molding process; the simulated diffraction waveform is used to characterize the diffraction energy distribution caused only by the surface periodic structure; and a parameter inversion unit for determining the intrinsic optical parameters of the functional metallic glass based on the weak diffraction spectrum, the diffraction residue ratio, and the simulated diffraction waveform.

[0016] The present invention has the following beneficial effects: By acquiring multi-angle reflectance datasets and separating strong diffraction spectra, weak diffraction spectra, and diffraction residual ratios, the surface geometric deformation parameters induced by the molding process were determined by combining strong diffraction spectra, and simulated diffraction waveforms were constructed. Finally, the intrinsic optical parameters were determined based on weak diffraction spectra, diffraction residual ratios, and simulated diffraction waveforms. This effectively decoupled the interference of surface periodic texture diffraction and process flow deformation on the measurement of intrinsic optical parameters of the material, achieving accurate determination of intrinsic optical parameters without destroying the sample. At the same time, the process-related surface structure size and morphological changes were quantified, providing a key foundation for subsequent process quality assessment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A structural diagram of an optical parameter measuring device for functional metallic glass provided in one embodiment of the present invention; Figure 2 A flowchart illustrating a method for measuring the optical parameters of a functional metallic glass, as provided in one embodiment of the present invention; Figure 3 This is a schematic diagram of the hardware structure of an optical parameter measuring device for functional metallic glass, provided in one embodiment of the present invention. Detailed Implementation

[0019] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method and apparatus for measuring the optical parameters of functional metallic glass according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0021] The following description, in conjunction with the accompanying drawings, details the specific scheme of the optical parameter measurement method and equipment for functional metallic glass provided by the present invention.

[0022] Please see Figure 1The diagram shows a structural diagram of an optical parameter measuring device for functional metallic glass according to an embodiment of the present invention. The optical parameter measuring device for functional metallic glass includes: a data acquisition unit 1, a signal separation unit 2, a geometric modeling unit 3, and a parameter inversion unit 4.

[0023] The data acquisition unit 1, serving as the signal input terminal of the device, acquires complete optical response data of the component under test (DUT). It primarily comprises a sample carrying and rotation submodule 11, a light source emission submodule 12, and a spectral detection submodule 13. The sample carrying and rotation submodule 11 utilizes a high-precision motorized rotating sample stage, enabling the DUT to rotate smoothly within a 0-360 degree range around the surface normal, with adjustable rotation step angles. The light source emission submodule 12 employs a broadband light source, outputting a stable beam covering a preset wavelength band and allowing for a fixed incident angle to enhance surface texture detection sensitivity. The spectral detection submodule 13 is equipped with a high-resolution spectrometer, simultaneously acquiring reflectance spectral data at each azimuth angle during component rotation. Through the collaborative work of these three submodules, this unit stacks multiple sets of spectral data according to wavelength and azimuth angle dimensions, forming a multi-angle reflectance dataset. This dataset is transmitted in real-time to the signal separation unit 2 as the foundational data source for subsequent signal processing.

[0024] Signal separation unit 2 receives the multi-angle reflectance dataset output by data acquisition unit 1. Its core function is to separate spectral signals representing different characteristics and quantify the proportion of residual interference. It consists of a standard deviation calculation submodule 21, a spectral classification submodule 22, and a residual ratio calculation submodule 23, which can be implemented through an embedded data processing chip or an industrial server. The standard deviation calculation submodule 21 calculates the dispersion (i.e., standard deviation) of the spectral intensity of the reflectance spectrum corresponding to each azimuth angle in the multi-angle reflectance dataset, thereby quantifying the diffraction interference intensity at different azimuth angles. The spectral classification submodule 22 sorts the standard deviations of all azimuth angles, selects the reflectance spectrum corresponding to the largest standard deviation as the strong diffraction spectrum (containing rich surface structure information), and selects the reflectance spectrum corresponding to the smallest standard deviation as the weak diffraction spectrum (minimally affected by diffraction interference). The residual ratio calculation submodule 23 calculates the ratio of the smallest standard deviation to the largest standard deviation to obtain the diffraction residual ratio, which is used to characterize the proportion of the residual geometric scattering signal amplitude in the weak diffraction spectrum. The strong diffraction spectrum, weak diffraction spectrum, and diffraction residue ratio output by the signal separation unit 2 are sent to the geometric modeling unit 3 and the parameter inversion unit 4, respectively. The strong diffraction spectrum provides the core analysis object for geometric modeling, while the weak diffraction spectrum and diffraction residue ratio provide the basic data for spectral correction.

[0025] The geometric modeling unit 3 is the core computing module of the equipment. It is responsible for analyzing the process distortion characteristics of the surface structure and building a correction model. It consists of a theoretical valley generation submodule 31, a measured valley extraction submodule 32, a mapping matching submodule 33, a deformation parameter calculation submodule 34, and a simulated waveform reconstruction submodule 35. It relies on a high-performance processor to perform complex data operations. The theoretical valley generation submodule 31 can receive a known preset value of the mold period, or when the mold period is unknown or worn, it performs frequency domain analysis on the strong diffraction spectrum to extract period information, and then combines it with a fixed incident angle to generate a set of theoretical valley positions based on the grating diffraction relationship; the measured valley extraction submodule 32 performs feature recognition on the strong diffraction spectrum, extracts all significant local minima, and forms a set of measured valley positions including the valley center position and depth; the mapping matching submodule 33 uses a global optimization algorithm to establish a mapping relationship between the measured valley positions and the theoretical valley positions, ensuring accurate matching even if there is a large valley drift; the deformation parameter calculation submodule 34 calculates the period reduction rate (characterizing the degree of overall size shrinkage of the surface periodic structure) based on the matching relationship, and calculates the waveform skew factor (characterizing the degree of shape asymmetry of a single periodic unit) by analyzing the difference in the width of the left and right halves of the measured valley; the simulated waveform reconstruction submodule 35 constructs multiple single-peak profile functions according to the measured valley position, depth and corresponding waveform skew factor, and then linearly superimposes these functions to generate a simulated diffraction waveform that only characterizes the diffraction energy distribution of the surface periodic structure. The period reduction rate, waveform skew factor, and simulated diffraction waveform output by geometric modeling unit 3 are all transmitted to parameter inversion unit 4, providing key information for optical parameter inversion and quality evaluation.

[0026] The parameter inversion unit 4 is the core of the equipment's output, responsible for restoring the intrinsic optical parameters of the raw material and evaluating the molding quality. It consists of a spectral correction submodule 41, an optical parameter inversion submodule 42, and a quality evaluation submodule 43, and can be implemented through a computer system integrating data processing and analysis software. The spectral correction submodule 41 receives the weak diffraction spectrum, diffraction residual ratio, and simulated diffraction waveform. It compensates and corrects the weak diffraction spectrum through weighted superposition operations to fill the energy loss caused by structural diffraction, obtaining a corrected reflection spectrum that only contains the intrinsic properties of the material. The optical parameter inversion submodule 42 uses an amorphous dispersion model to fit the corrected reflection spectrum. Through iterative optimization of the model parameters, it brings the deviation between the theoretical reflectivity and the corrected reflection spectrum to below a preset threshold, thereby calculating the material's refractive index and extinction coefficient (i.e., intrinsic optical parameters) across the entire wavelength range. The quality evaluation submodule 43, in conjunction with the period reduction rate and waveform skew factor output by the geometric modeling unit 3 and the inverted intrinsic optical parameters, comprehensively evaluates the material properties, geometric molding, and rheological filling from three dimensions, generating molding quality evaluation information. The intrinsic optical parameters and molding quality evaluation information finally output by parameter inversion unit 4 provide users with complete test results of material properties and process quality.

[0027] Please see Figure 2 The diagram illustrates a flowchart of a method for measuring the optical parameters of a functional metallic glass according to an embodiment of the present invention. The method for measuring the optical parameters of a functional metallic glass includes: S1. Obtain the multi-angle reflectivity dataset of the surface of the functional metal glass component to be tested.

[0028] In some implementations, the functional metal glass element under test is rotated around the surface normal direction at a fixed incident angle, and the reflectance spectrum of the surface within a preset wavelength range is collected at multiple continuous or discrete azimuth angle positions to form a multi-angle reflectance dataset.

[0029] Specifically, since the periodic microstructures on the surface of functional metal glass molded components have significant optical anisotropy, a single-angle measurement cannot fully capture the mixed information of material absorption and structural diffraction, and it is also difficult to predict the specific orientation of the texture. Therefore, it is necessary to construct a spectral dataset covering the entire rotation period as the basis for subsequent signal separation and parameter decoupling.

[0030] First, place the functional metallic glass component to be tested stably on the motorized rotating sample stage of the spectral measurement equipment, ensuring that the surface of the component is perpendicular to the rotation axis of the sample stage. This prevents the component from tilting, which could cause deviations in the azimuth angle and actual texture direction of the subsequently acquired optical response data, thus ensuring the correspondence between the data and the spatial features of the component surface.

[0031] Next, the incident angle of the broadband light source is set to a fixed value, for example, 70 degrees. The choice of angle can enhance the diffraction effect of the surface periodic microstructure on the incident light, improve the subsequent detection sensitivity of texture features, and facilitate the capture of weak structural scattering signals.

[0032] Subsequently, the control module of the device sends control commands to the electric rotating sample stage, which rotates within the azimuth angle range of 0 to 360 degrees with a preset step angle (e.g., a step angle of 1 degree; if azimuth resolution needs to be improved, a step angle of 0.5 degrees can also be selected). This covers the optical response of all planar directions on the surface of the component, avoiding the omission of strong or weak diffraction features at specific azimuth angles.

[0033] When the electric rotating sample stage reaches a preset azimuth angle position, it triggers the spectrometer to start the acquisition process and collects the reflectivity intensity data of the surface of the component under test in a preset wavelength range (e.g., visible light and near-infrared bands from 300nm to 1000nm). The selection of the wavelength range is suitable for the optical property detection needs of most functional metallic glasses.

[0034] After completing the reflectance spectrum acquisition at all azimuth positions, a preset data integration algorithm (such as a spectral stacking algorithm based on a two-dimensional array) is invoked to stack all the acquired spectral data in an orderly manner according to the two dimensions of wavelength and azimuth, forming a multi-angle reflectance dataset, thus integrating the discrete single-azimuth spectral data into a structured dataset.

[0035] S2. Based on the multi-angle reflectance dataset, separate the strong diffraction spectrum and the weak diffraction spectrum, and determine the diffraction residue ratio.

[0036] Strong diffraction spectra are reflectance spectra with the largest standard deviation of spectral intensity in a multi-angle reflectance dataset, while weak diffraction spectra are reflectance spectra with the smallest standard deviation of spectral intensity in a multi-angle reflectance dataset. The diffraction residue ratio is used to characterize the amplitude proportion of the residual geometric scattering signal in a weak diffraction spectrum.

[0037] In some implementations, the method for separating strong and weak diffraction spectra and determining the diffraction residue ratio can be specifically implemented through the following steps S21 to S24, which are explained in detail below: S21. Determine the standard deviation of the spectral intensity of the reflectance spectrum corresponding to each azimuth angle in the multi-angle reflectance dataset.

[0038] Specifically, due to the significant optical anisotropy of the periodic microstructure on the surface of functional metal glass molded elements, the diffraction efficiency will vary significantly when the incident light plane is at different angles to the extension direction of the texture grooves, which in turn leads to different fluctuation amplitudes of the reflection spectrum. Therefore, it is necessary to calculate the fluctuation amplitude of the spectral intensity in the wavelength dimension to quantify the diffraction interference intensity at each azimuth angle.

[0039] First, a preset traversal algorithm (such as a loop traversal algorithm) is called to process the reflectance spectrum corresponding to each azimuth angle in the constructed multi-angle reflectance dataset one by one. For the current azimuth angle, the reflectance intensity data of the full wavelength point covering the preset band under this angle is extracted to form a spectral intensity vector, so as to fully obtain the optical response characteristics under this azimuth angle and avoid misjudgment of fluctuation amplitude caused by missing data.

[0040] Next, the arithmetic mean of the intensity values ​​at all wavelengths in the spectral intensity vector is calculated. This mean serves as the baseline level for the spectral intensity at that azimuth angle, eliminating the interference of fluctuations at a single wavelength point on the overall characteristics. Subsequently, based on this mean, the standard deviation (Std) of the spectral intensity is calculated using the following formula: In the formula, Represents the current azimuth angle The corresponding standard deviation of spectral intensity; Represents the current azimuth angle The reflectivity intensity data corresponding to the i-th wavelength point; Represents the current azimuth angle Below, N is the arithmetic mean of the intensity values ​​at all wavelength points in the reflectance spectrum; N represents the total number of wavelength sampling points in the reflectance spectrum. The standard deviation of spectral intensity is the fluctuation range of the reflectance spectrum in the wavelength dimension at the current azimuth angle. The larger the value, the stronger the diffraction interference of the surface texture of the component at that azimuth angle. It can intuitively reflect the change law of the surface texture scattering intensity with the observation angle.

[0041] S22. The reflectance spectrum corresponding to the maximum standard deviation of spectral intensity in the multi-angle reflectance data is determined as the strong diffraction spectrum.

[0042] Specifically, after calculating the standard deviation of spectral intensity for all azimuth angles, a preset extreme value search algorithm (such as a traversal search algorithm, or a divide-and-conquer search algorithm when the dataset is large) is invoked to perform a global scan on the generated standard deviation sequence that varies with azimuth angle, and to find the global maximum value in the sequence; the azimuth angle corresponding to the global maximum value is locked, at which the incident light plane is perpendicular to the direction of the texture groove extension, the grating diffraction efficiency on the surface of the component reaches its highest level, and the trough fluctuations of the reflection spectrum are the most intense.

[0043] Subsequently, the reflectance spectrum corresponding to the azimuth angle was extracted from the multi-angle reflectance dataset and identified as a strong diffraction spectrum. This yielded spectral data containing the most significant trough position and waveform distortion information, providing sufficient geometric feature basis for subsequent calculations of period reduction rate and waveform skew factor.

[0044] S23. The reflectance spectrum corresponding to the minimum standard deviation of spectral intensity in the multi-angle reflectance data is determined as the weak diffraction spectrum.

[0045] Specifically, the preset extreme value search algorithm is used to find the global minimum value in the standard deviation sequence; the azimuth angle corresponding to the global minimum value is locked. At this azimuth angle, the incident light plane is parallel to the extension direction of the texture groove, the geometric shading effect makes the diffraction effect the weakest, and the reflection spectrum curve is the smoothest.

[0046] The reflectance spectrum corresponding to the azimuth angle is extracted from the multi-angle reflectance dataset and identified as a weak diffraction spectrum. This yields the spectral data with minimal interference from texture diffraction, which can be used as the spectral basis for subsequent parameter inversion, reducing the interference of geometric scattering on the extraction of the intrinsic properties of the material.

[0047] S24. Determine the diffraction residual ratio based on the ratio of the minimum standard deviation of spectral intensity to the maximum standard deviation of spectral intensity.

[0048] Specifically, the minimum and maximum standard deviations of spectral intensity obtained in the previous steps are used to calculate their ratio. This ratio is then determined as the diffraction residual ratio, quantifying the amplitude proportion of the unavoidable residual geometric scattering signal in weak diffraction spectra relative to the main scattering signal in strong diffraction spectra. This ratio can subsequently be used as a weighting factor to control the intensity of spectral correction, ensuring that the correction process can adapt to the degree of scattering residuals of different components and improve the accuracy of parameter inversion.

[0049] In some implementations, to avoid calculation anomalies caused by extremely weak surface texture or excessively strong material absorption in the functional metallic glass component under test, a threshold judgment is required for the maximum spectral intensity standard deviation. First, a preset threshold is set, which can be determined based on the noise level or dynamic range of the spectral measurement system; for example, 0.01 (corresponding to relative reflectance intensity) can be selected. If the maximum spectral intensity standard deviation is less than the preset threshold, it indicates that the periodic texture diffraction effect on the component surface is negligible. In this case, there is no need for subsequent diffraction residue ratio calculation and spectral correction; the weak diffraction spectrum can be directly determined as the intrinsic reflectance spectrum of the functional metallic glass, or equivalently, the diffraction residue ratio can be set to 0, making the compensation term in subsequent correction steps zero. If the maximum spectral intensity standard deviation is greater than or equal to the preset threshold, the diffraction residue ratio is determined according to the ratio of the minimum spectral intensity standard deviation to the maximum spectral intensity standard deviation, following the aforementioned method. By introducing this threshold judgment, invalid diffraction separation calculations can be effectively avoided on textureless or strongly absorbing samples, improving the applicability and reliability of the measurement method.

[0050] S3. Based on the strong diffraction spectrum, determine the geometric deformation parameters of the surface of the functional metal glass element to be tested, and construct a simulated diffraction waveform based on the geometric deformation parameters.

[0051] Geometric deformation parameters are used to quantify the dimensional and morphological changes of surface periodic structures caused by the molding process, including the period reduction rate and the waveform skew factor. The period reduction rate characterizes the overall dimensional shrinkage of the surface periodic structure. The waveform skew factor characterizes the degree of shape asymmetry of individual periodic units in the surface periodic structure.

[0052] In some implementations, the method for determining the geometric deformation parameters of the surface of the functional metallic glass element under test can be specifically implemented through the following steps S31 to S35, which are explained in detail below: S31. Determine the set of theoretical trough positions based on the preset mold period value or the period information extracted from the strong diffraction spectrum.

[0053] In some implementation methods, the specific method for determining the theoretical valley position set based on the mold cycle preset value includes: based on the mold cycle preset value and the fixed incident angle used during measurement, and based on the grating diffraction relationship, determining the theoretical wavelength of the multi-order diffraction valleys that appear within the preset wavelength range, and forming the theoretical valley position set.

[0054] Specifically, in order to quantify the thermal shrinkage effect during the molding process of functional metal glass, it is necessary to establish a theoretical reference without deformation, and therefore it is necessary to determine the set of theoretical trough locations.

[0055] If the preset value of the mold cycle during mold processing is known, the parameter is directly called as the benchmark; if the design parameter is missing or the parameter drifts due to mold wear, the preset fast Fourier transform algorithm is called to perform frequency domain analysis on the strong diffraction spectrum, extract the non-zero frequency component with the largest amplitude in the frequency domain, and use its reciprocal as the initial estimate of the preset value of the mold cycle. In the absence of design parameters, a reliable cycle benchmark is obtained based on the measured spectrum.

[0056] Next, using a fixed incident angle for measurement and based on the Rayleigh anomalous condition in grating diffraction, the position of the trough in the ideal state without contraction is calculated, and the corresponding theoretical wavelength formula is: In the formula, Characterizes the preset value of the mold cycle (or its initial estimate); k characterizes the non-zero integer order covering the measurement band; The fixed incident angle used in the characterization measurement; Characterizes the theoretical wavelength corresponding to the k-th theoretical trough; First, obtain by fixing the incident angle. (Characterizing the angular correlation between incident and diffracted light), then divide the preset value of the mold period by the absolute value of the order to obtain the period contribution corresponding to the unit order. Multiply the two to obtain the theoretical trough wavelength at that order. The final result is the set of theoretical trough positions, which includes the trough wavelengths corresponding to each diffraction order under ideal conditions. It is the rigid coordinate frame for subsequent trough matching. The value range is the length value (e.g., nanometer) within the measurement band, and the dimension is length.

[0057] It should be noted that, according to the Rayleigh anomaly condition, the calculation of the theoretical trough position includes two grazing directions, and the corresponding theoretical wavelength expression is as follows: Wherein, "+1" corresponds to a positive grazing refraction with a diffraction angle of +90°, and "-1" corresponds to a negative grazing refraction with a diffraction angle of -90°. Since the strong diffraction spectra in this method are typically acquired at an azimuth angle perpendicular to the texture grooves, and the incident angle... The energy is relatively large, mainly inducing a positive grazing effect (i.e., the "+1" case). Therefore, the "+1" case is used to determine the theoretical trough location set in actual calculations. Negative grazing characteristics, which may occur, are weaker or exceed the preset measurement band, and are therefore not considered here.

[0058] S32. Extract the set of measured valley locations from the strong diffraction spectrum.

[0059] Specifically, in order to obtain the actual diffraction valley characteristics on the surface of functional metal glass components, it is necessary to extract the measured valley information from the strong diffraction spectrum as the actual object to match the theoretical valley.

[0060] The pre-defined second-derivative peak-finding algorithm is invoked to perform second-derivative operations on the strong diffraction spectrum. By identifying the zero-crossing points of the operation results, all significant local minima in the spectrum (i.e., the actual locations of diffraction valleys) are located. Subsequently, the wavelength corresponding to each local minima is recorded as the measured center wavelength, and the difference between the reflectance intensity at that point and the intensity of the surrounding baseline is extracted as the valley depth. This information is integrated to construct a set of measured valley locations, where each element contains the measured center wavelength and the corresponding valley depth, thus accurately capturing the valley features in the strong diffraction spectrum.

[0061] S33. By minimizing the overall matching deviation between the measured valley position set and the theoretical valley position set, a mapping relationship between the measured valley positions and the theoretical valley positions is established.

[0062] Specifically, since functional metallic glasses undergo significant volume shrinkage when cooled in the supercooled liquid phase region, the measured trough position may be significantly blue-shifted relative to the theoretical position, making conventional nearest neighbor matching algorithms prone to failure. Therefore, a global cost minimization approach is required to establish the mapping.

[0063] First, construct a matching cost matrix using the theoretical number of troughs as the number of rows and the measured number of troughs as the number of columns. The matrix elements are defined as the normalized absolute values ​​of wavelength deviations. In the formula, It is the center wavelength of the j-th measured trough; It is the wavelength of the k-th theoretical trough. This element quantifies the matching deviation between a single measured trough and the theoretical trough; the smaller the deviation, the smaller the element value and the higher the matching degree.

[0064] Subsequently, global optimization algorithms such as the Hungarian algorithm are used to find the injective mapping relationship that minimizes the total matching cost. This relationship allows a theoretical valley to match at most one measured valley, establishing a correct mapping relationship between the measured valley and the corresponding theoretical level, thus avoiding matching errors of conventional algorithms.

[0065] S34. Determine the period reduction rate based on the ratio of the measured trough position to the theoretical trough position matched in the mapping relationship.

[0066] Specifically, in order to quantify the overall dimensional shrinkage of the periodic structure on the surface of the functional metal glass element during the molding process, the periodic shrinkage rate needs to be calculated based on the matched trough pairs.

[0067] Based on the established mapping relationship, the ratios of the measured center wavelength of all successfully matched troughs to the corresponding theoretical trough wavelengths are extracted, and the average value of these ratios is calculated. Then, the period reduction rate is calculated using the following formula: In the formula, L represents the total number of successfully matched trough pairs; The measured center wavelength of the j-th matched valley; Characterizes the wavelength corresponding to the theoretical trough; Characterizes the mapping relationship between measured troughs and theoretical troughs; Characterize the measured and theoretical valley pairs matched in the mapping relationship; Characterizing the cycle reduction rate; First, the ratio of the measured wavelength to the theoretical wavelength for each matching pair is calculated (reflecting the degree of shrinkage of a single trough). Then, the average value of these ratios is taken (reflecting the average level of overall shrinkage). Finally, the periodic shrinkage rate is obtained by subtracting the average value from 1. This is a dimensionless parameter that characterizes the degree of overall dimensional shrinkage of the periodic structure on the surface of the component. It quantifies the degree of thermal shrinkage effect and provides a basis for the geometric dimension of subsequent molding quality evaluation.

[0068] S35. Determine the waveform skew factor based on the difference in the left and right half widths of the measured troughs corresponding to the mapping relationship in the strong diffraction spectrum.

[0069] In some implementations, for each measured valley in the mapping relationship, the wavelengths corresponding to the intensity rising to half the valley depth on both sides of the center position of the measured valley are determined on the strong diffraction spectrum; the waveform skew factor corresponding to the measured valley is determined by the ratio of the difference between the right wavelength and the center wavelength, and the difference between the center wavelength and the left wavelength, to the total width of the measured valley.

[0070] Specifically, functional metallic glass is affected by non-uniform shear stress during molding and filling, resulting in asymmetrical shapes of surface periodic units. The degree of asymmetry needs to be quantified by waveform skew factor.

[0071] For each measured valley in the mapping relationship, the preset half-height position search algorithm is invoked. Starting from the center wavelength of the valley, the search is performed in the short-wavelength direction (left) and long-wavelength direction (right) of the spectrum, until the reflectivity intensity rises back to half the depth of the valley. The corresponding left and right wavelengths are recorded at this time to accurately locate the half-height width boundary of the valley and reflect the asymmetric morphological characteristics of the valley.

[0072] Then, the waveform skew factor corresponding to this trough is calculated using the formula: In the formula, The waveform skew factor characterizing the j-th measured trough; The wavelength corresponding to the right half-height position of the j-th measured trough; Characterizes the center wavelength of the j-th measured trough; The wavelength corresponding to the left half-height position of the j-th measured trough; First, calculate the difference between the right half-height position and the center wavelength (right half-width), and the difference between the center wavelength and the left half-height position (left half-width). Then, calculate the difference between the two (reflecting the degree of asymmetry). Finally, normalize this difference by dividing it by the total width of the trough. The denominator is the total width of the trough. The trough is a significant local minimum. Since there is a difference between the left and right half-height positions, the total width is greater than 0 and will not be zero. The final waveform skew factor is a dimensionless parameter that characterizes the degree of asymmetry in the shape of a single periodic unit. It reflects the uneven distribution of rheological shear stress during molding and provides a basis for the microscopic morphological dimension for subsequent molding quality evaluation.

[0073] Simulated diffraction waveforms are used to characterize the diffraction energy distribution caused solely by surface periodic structures.

[0074] In some implementations, the method of constructing simulated diffraction waveforms based on geometric deformation parameters can be specifically implemented through the following steps S36 to S37, which are explained in detail below: S36. Based on the measured position and depth of the valleys and the corresponding waveform skew factor in the mapping relationship, construct multiple single-peak profile functions.

[0075] The linewidth of each single-peak profile function is modulated by the full width of the corresponding measured trough and the waveform skew factor.

[0076] Specifically, in order to generate waveform units that can replicate the true geometric scattering characteristics of the surface of functional metallic glass components, a single-peak profile function needs to be constructed based on the parameters of the measured valleys. Each function needs to reflect the morphological asymmetry caused by rheological shearing.

[0077] First, obtain the center wavelength (i.e., the measured valley position), valley depth, and waveform skew factor corresponding to each measured valley from the mapping relationship; at the same time, retrieve the left and right wavelengths of the valley and calculate the difference between them to obtain the measured full width. The full width is the basic parameter characterizing the degree of valley widening and provides a reference for linewidth modulation.

[0078] Subsequently, linewidth modulation is performed on each measured trough: when the wavelength is less than the center wavelength of the trough, the characteristic linewidth parameter is set to the product of the measured full wavelength and (1 minus the waveform skew factor); when the wavelength is greater than or equal to the center wavelength of the trough, the characteristic linewidth parameter is set to the product of the measured full wavelength and (1 plus the waveform skew factor). The above modulation operation allows the left and right linewidths of the single-peak profile to change with the waveform skew factor, accurately reproducing the asymmetry of the trough shape caused by rheological shear.

[0079] Finally, the preset function construction algorithm is called, using a piecewise modulated pseudo-Voigt function (which is a linear combination of the Lorentz function and the Gaussian function, and can fit the asymmetric broadening characteristics of diffraction peaks in the real spectrum). Combined with the modulated feature linewidth parameters, the center wavelength and valley depth of the measured valley, a single-peak profile function corresponding to each measured valley is constructed to generate a waveform unit with real process distortion characteristics. Each single-peak profile function not only locks the valley position after thermal shrinkage, but also reflects the morphological asymmetry caused by rheology.

[0080] S37. The constructed single-peak profile functions are superimposed to generate a simulated diffraction waveform.

[0081] Specifically, in order to obtain a complete waveform that contains only the geometric scattering characteristics of the component surface, all single-peak profile functions need to be integrated into a simulated diffraction waveform, which serves as a correction operator for subsequent separation of the intrinsic signals of the material.

[0082] Using the measured valley depth corresponding to each single-peak profile function as a weight, a linear superposition operation is performed on all constructed single-peak profile functions. The corresponding formula is: In the formula, Characterizes the simulated diffraction waveform; Characterize the measured and theoretical valley pairs matched in the mapping relationship; Characterizes the depth of the j-th measured trough; The single-peak profile function that characterizes the j-th measured trough; Using the depth of each measured valley as a weight, the corresponding single-peak profile functions are linearly superimposed. The greater the valley depth, the stronger the contribution of its corresponding single-peak profile function to the final waveform. The geometric scattering characteristics of all valleys are integrated to form a diffraction waveform covering the entire band. The final result is a simulated diffraction waveform, whose physical meaning is the diffraction energy distribution curve caused only by the periodic structure of the element surface (including thermal contraction and rheological distortion). The value range is consistent with the range of reflectivity intensity, and the dimension is the reflectivity dimension.

[0083] S4. Determine the intrinsic optical parameters of the functional metallic glass based on the weak diffraction spectrum, diffraction residual ratio, and simulated diffraction waveform.

[0084] In some implementations, the corrected reflection spectrum is determined by a weighted sum of the weak diffraction spectrum, the diffraction residue ratio, and the simulated diffraction waveform. Based on the corrected reflection spectrum, an amorphous dispersion model is used to fit the spectrum and determine the refractive index and extinction coefficient of the functional metallic glass as intrinsic optical parameters.

[0085] Specifically, since weak diffraction spectra are collected at the azimuth angle where the diffraction effect is weakest, but are affected by the numerical aperture effect of the optical system and the microscopic defects left by the texture processing, there are still trace geometric scattering signals mixed in the spectrum. These signals will manifest as energy loss in a specific band. If they are used directly for parameter inversion, the energy loss caused by structural diffraction will be mistakenly identified as the absorption loss of the material itself. Therefore, compensation calculations are needed to eliminate this residual interference.

[0086] First, we call up the weak diffraction spectrum, the diffraction residual ratio, and the simulated diffraction waveform. The weak diffraction spectrum is the spectral base least affected by structural diffraction, the diffraction residual ratio is the amplitude ratio of the residual scattering signal in the weak diffraction spectrum, and the simulated diffraction waveform is the energy distribution profile that only contains the structural geometric scattering characteristics. The three together provide matching basic data for the compensation calculation, ensuring the accuracy of the compensation process.

[0087] Then, a linear compensation operation is performed, and the corresponding formula is: In the formula, Characterizing the corrected reflectance spectrum; Characterizing weak diffraction spectra; Characterizes the diffraction residue ratio; The simulated diffraction waveform is characterized. Using the diffraction residual ratio as a weight, the simulated diffraction waveform is multiplied by this weight to obtain the geometric scattering signal component remaining in the weak diffraction spectrum. This component is then superimposed on the weak diffraction spectrum to fill the energy loss caused by diffraction. The final result is the corrected reflection spectrum, which physically represents the reflection response of the functional metallic glass under ideal flat surface conditions. It contains only the intrinsic optical properties determined by the material's electronic transitions and free carrier motion, with the dimension being the reflectivity dimension. This spectrum eliminates the interference of structural diffraction and provides accurate basic data for the subsequent inversion of intrinsic optical parameters.

[0088] It is important to note that after obtaining the corrected reflectance spectrum... Next, its physical rationality needs to be verified. Since reflectivity is defined as the ratio of reflected light intensity to incident light intensity, its value should be within the range [0,1]. If at certain wavelengths... A value greater than 1 indicates that the compensation amount is too large, possibly caused by measurement noise, diffraction residual ratio estimation error, or overshoot introduced by model approximation. In this case, the corrected reflectance spectrum should be truncated: Let That is, all values ​​greater than 1 are assigned the value 1.

[0089] Next, the intrinsic optical parameters are retrieved based on the corrected reflection spectrum: Since functional metallic glasses belong to a long-range disordered amorphous alloy system, their dielectric function follows the dispersion law of amorphous semiconductors. Therefore, a preset amorphous dispersion model (such as the Tauc-Lorentz oscillator model) can be used to accurately describe the optical response characteristics of amorphous materials.

[0090] An objective function is constructed, which is defined as the root mean square error between the theoretical reflectance calculated by the amorphous dispersion model and the corrected reflectance spectrum, thereby quantifying the degree of deviation between the theoretical and measured values.

[0091] The preset iterative optimization algorithm (such as the Levenberg-Marquardt algorithm) is invoked to iteratively solve for the parameters of the amorphous dispersion model (including band gap width, oscillator amplitude, peak energy, broadening factor, etc.), and a preset convergence threshold is set (e.g., root mean square error less than 1 × 10⁻⁶). -5 The iteration stops when the value of the objective function converges to below the threshold.

[0092] The aforementioned thresholds (convergence threshold, deviation threshold, tolerance range, and limit) can be set according to the design specifications, material system, and process control requirements of the component under test. Typically, the convergence threshold can be set based on the spectrometer noise level; the material deviation threshold can refer to the typical performance fluctuation range of similar amorphous alloys; and the geometric tolerance and waveform limit can be determined based on the mold design accuracy and the functional requirements of the optical component.

[0093] Finally, based on the converged model parameters, the refractive index and extinction coefficient of the material across the entire wavelength range are calculated. These two parameters are used as the intrinsic optical parameters of the functional metallic glass. The intrinsic optical properties of the material are accurately extracted from the modified reflection spectrum. The refractive index characterizes the material's ability to deflect incident light, and the extinction coefficient characterizes the material's ability to absorb incident light. Together, they constitute the core basic data for evaluating the optical performance of the material and also provide a basis for the material property dimension for subsequent evaluation of molding quality.

[0094] In some implementations, the method further includes generating molding quality evaluation information for the functional metallic glass element under test based on at least one of the period reduction rate, waveform skew factor, and intrinsic optical parameters.

[0095] Specifically, in order to achieve closed-loop diagnosis of the molding manufacturing process, it is necessary to combine the intrinsic properties of the material with the geometric deformation parameters induced by the process, evaluate the molding quality of the component from multiple dimensions, and provide targeted basis for process optimization.

[0096] First, the period reduction rate, waveform skew factor, and material extinction coefficient from the intrinsic optical parameters are called. The period reduction rate and waveform skew factor are derived from the solution of the geometric deformation parameters, and the material extinction coefficient is derived from the inversion of the intrinsic optical parameters. The three correspond to the geometric dimensions, micromorphology, and material structure dimensions of the process, respectively, providing data support for multi-dimensional evaluation.

[0097] Then, material property evaluation is performed: a preset reference database (such as a library of extinction coefficient curves for standard amorphous alloys of functional metal glass) is called, and a preset deviation analysis algorithm (such as a mean square error calculation algorithm) is used to compare the measured material extinction coefficient curve with the standard curve in the database. A preset deviation threshold is set (e.g., deviation exceeds 5%). If the comparison result exceeds the threshold, it indicates that the material may have undergone unexpected crystallization or oxidation during the molding process. This determines whether the intrinsic structure of the material is abnormal due to the process and ensures that the optical performance of the material meets the design requirements of amorphous state.

[0098] Next, perform a geometrical shape evaluation: retrieve the preset periodic shrinkage rate tolerance range (e.g., the preset tolerance is ±2%), and check whether the measured periodic shrinkage rate is within this tolerance range. If it exceeds the range, it indicates that improper cooling rate control has led to abnormal thermal shrinkage. Quantify the degree of shrinkage of macroscopic geometric dimensions during the molding process to reflect whether the molding accuracy of the periodic structure on the surface of the component meets the standard.

[0099] Next, a rheological filling evaluation is performed: the preset waveform skew factor limit is retrieved (for example, the preset limit is 0.2), and the absolute value of the waveform skew factor corresponding to each measured valley is checked one by one. If the absolute value is greater than the limit, it indicates that the molding pressure or temperature distribution is uneven, resulting in an excessive shear stress gradient during the groove filling process. The filling uniformity of the microstructure during the molding process is evaluated to reflect the degree of influence of the rheological shear effect on the surface morphology.

[0100] Finally, the information integration module is invoked to summarize the results of material property evaluation, geometric forming evaluation, and rheological filling evaluation, and generate molding quality evaluation information (such as a comprehensive test report) that includes evaluation conclusions and anomaly alerts for each dimension. This completes the entire process from optical measurement to process quality diagnosis, providing a clear target basis for subsequent parameter adjustment and optimization of the molding process.

[0101] Based on the above technical solution, by acquiring multi-angle reflectance datasets and separating strong diffraction spectra, weak diffraction spectra, and diffraction residual ratios, the surface geometric deformation parameters induced by the molding process are determined by combining the strong diffraction spectra, and a simulated diffraction waveform is constructed. Finally, the intrinsic optical parameters are determined based on the weak diffraction spectra, diffraction residual ratios, and simulated diffraction waveforms. This effectively decouples the interference of surface periodic texture diffraction and process flow deformation on the measurement of the intrinsic optical parameters of the material, and realizes the accurate determination of intrinsic optical parameters without destroying the sample. At the same time, it quantifies the process-related surface structure size and morphological changes, providing a key foundation for subsequent process quality assessment.

[0102] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0103] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0104] In this embodiment of the invention, the optical parameter measuring device for functional metallic glass can be divided into functional units according to the above method example. For example, each function can be divided into its own functional unit, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or software. It should be noted that the unit division in this embodiment is illustrative and only represents one logical functional division; other division methods may be used in actual implementation.

[0105] This invention also provides a schematic diagram of the hardware structure of a device for measuring the optical parameters of functional metallic glasses, see [link / reference]. Figure 3 The optical parameter measuring device 300 for the functional metallic glass includes a processor 301, and optionally, a memory 302 connected to the processor 301.

[0106] In the first possible implementation, see Figure 3 The optical parameter measuring device 300 for functional metallic glasses also includes a transceiver 303. The processor 301, memory 302, and transceiver 303 are connected via a bus. The transceiver 303 is used to communicate with other devices or communication networks. Optionally, the transceiver 303 may include a transmitter and a receiver. The device in the transceiver 303 that performs the receiving function can be considered as a receiver, which performs the receiving steps in the embodiments of the present invention. The device in the transceiver 303 that performs the transmitting function can be considered as a transmitter, which performs the transmitting steps in the embodiments of the present invention.

[0107] Based on the first possible implementation method Figure 3 The schematic diagram shown can be used to illustrate the structure of the optical parameter measuring device for the functional metallic glass involved in the above embodiments.

[0108] in, Figure 3 The system chip in the optical parameter measuring device for functional metallic glass can also be illustrated. In this case, the actions performed by the aforementioned optical parameter measuring device for functional metallic glass can be implemented by this system chip. The specific actions performed can be found above and will not be repeated here.

[0109] Although the invention has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings and the disclosure, will understand and implement other variations of the disclosed embodiments in carrying out the claimed invention. In this invention, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality. A single processor or other unit can implement several of the functions listed in this invention.

[0110] Although the invention has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made therein without departing from the spirit and scope of the invention. Accordingly, this specification and drawings are merely illustrative of the invention and are to be considered as covering any and all modifications, variations, combinations, or equivalents within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if such modifications and modifications of the invention fall within the scope of the invention and its equivalents, the invention is also intended to include such modifications and modifications.

Claims

1. A method for measuring the optical parameters of functional metallic glasses, characterized in that, include: Acquire a multi-angle reflectivity dataset of the surface of the functional metallic glass component under test; Based on the multi-angle reflectance dataset, strong diffraction spectra and weak diffraction spectra are separated, and the diffraction residue ratio is determined. The strong diffraction spectrum is the reflectance spectrum with the largest standard deviation of spectral intensity in the multi-angle reflectance dataset, and the weak diffraction spectrum is the reflectance spectrum with the smallest standard deviation of spectral intensity in the multi-angle reflectance dataset. The diffraction residual ratio is used to characterize the amplitude ratio of the residual geometric scattering signal in the weak diffraction spectrum; Based on the strong diffraction spectrum, the geometric deformation parameters of the surface of the functional metal glass element under test are determined, and a simulated diffraction waveform is constructed based on the geometric deformation parameters. The geometric deformation parameters are used to quantify the size and morphological changes of the surface periodic structure caused by the molding process; the simulated diffraction waveform is used to characterize the diffraction energy distribution caused only by the surface periodic structure. The intrinsic optical parameters of the functional metallic glass are determined based on the weak diffraction spectrum, the diffraction residual ratio, and the simulated diffraction waveform.

2. The method for measuring the optical parameters of functional metallic glass according to claim 1, characterized in that, Separate strong and weak diffraction spectra and determine the diffraction residue ratio, including: Determine the standard deviation of the spectral intensity of the reflectance spectrum corresponding to each azimuth angle in the multi-angle reflectance dataset; The reflectance spectrum corresponding to the maximum spectral intensity standard deviation in the multi-angle reflectance data set is determined as the strong diffraction spectrum. The reflectance spectrum corresponding to the minimum spectral intensity standard deviation in the multi-angle reflectance dataset is determined as the weak diffraction spectrum. The diffraction residual ratio is determined based on the ratio of the minimum spectral intensity standard deviation to the maximum spectral intensity standard deviation.

3. The method for measuring the optical parameters of functional metallic glass according to claim 1, characterized in that, The geometric deformation parameters include the periodicity reduction rate and the waveform skew factor; the periodicity reduction rate is used to characterize the overall dimensional shrinkage of the surface periodic structure. The waveform skew factor is used to characterize the degree of shape asymmetry of a single periodic unit in a surface periodic structure; Based on the strong diffraction spectrum, the geometric deformation parameters of the surface of the functional metallic glass element under test are determined, including: The theoretical valley position set is determined based on the preset mold period value or the period information extracted from the strong diffraction spectrum. Extract the set of measured valley locations from the strong diffraction spectrum; A mapping relationship between the measured valley locations and the theoretical valley locations is established by minimizing the overall matching deviation between the measured valley location set and the theoretical valley location set. The period reduction rate is determined based on the ratio of the measured trough position to the theoretical trough position matched in the mapping relationship. The waveform skew factor is determined based on the difference in the left and right half widths of the measured valleys corresponding to the mapping relationship in the strong diffraction spectrum.

4. The method for measuring the optical parameters of functional metallic glass according to claim 3, characterized in that, Based on the preset mold cycle value, determine the set of theoretical trough locations, including: Based on the preset mold cycle value and the fixed incident angle used during measurement, and based on the grating diffraction relationship, the theoretical wavelengths of the multi-order diffraction valleys appearing within the preset wavelength range are determined, forming the set of theoretical valley positions.

5. The method for measuring the optical parameters of functional metallic glass according to claim 3, characterized in that, The waveform skew factor is determined based on the difference in the left and right half widths of the measured valleys corresponding to the mapping relationship in the strong diffraction spectrum, including: For each measured valley in the mapping relationship, determine the wavelength corresponding to the intensity rising to half the valley depth on the left and right sides of the center position of the measured valley on the strong diffraction spectrum. The waveform skew factor corresponding to the measured trough is determined by the ratio of the difference between the right wavelength and the center wavelength, and the difference between the center wavelength and the left wavelength, to the total width of the measured trough.

6. The method for measuring the optical parameters of functional metallic glass according to claim 3, characterized in that, Constructing a simulated diffraction waveform based on the stated geometric deformation parameters includes: Based on the measured position and depth of the valley in the mapping relationship and the corresponding waveform skew factor, multiple single-peak profile functions are constructed; the linewidth of each single-peak profile function is modulated by the full width of the corresponding measured valley and the waveform skew factor. The simulated diffraction waveform is generated by superimposing multiple constructed single-peak profile functions.

7. The method for measuring the optical parameters of functional metallic glass according to claim 6, characterized in that, The intrinsic optical parameters of the functional metallic glass are determined based on the weak diffraction spectrum, the diffraction residual ratio, and the simulated diffraction waveform, including: The corrected reflection spectrum is determined based on the weighted sum of the weak diffraction spectrum, the diffraction residual ratio, and the simulated diffraction waveform; Based on the modified reflection spectrum, the refractive index and extinction coefficient of the functional metallic glass are determined by fitting with an amorphous dispersion model, and are used as the intrinsic optical parameters.

8. The method for measuring the optical parameters of functional metallic glass according to claim 7, characterized in that, Also includes: The forming quality evaluation information of the functional metallic glass component under test is generated based on at least one of the period reduction rate, the waveform skew factor, and the intrinsic optical parameters.

9. The method for measuring the optical parameters of functional metallic glass according to claim 1, characterized in that, Obtain a multi-angle reflectivity dataset of the surface of the functional metallic glass component under test, including: At a fixed incident angle, the functional metal glass element under test is rotated around the surface normal direction, and the reflectance spectrum of the surface within a preset wavelength range is collected at multiple continuous or discrete azimuth angle positions to form the multi-angle reflectance dataset.

10. An optical parameter measuring device for functional metallic glass, characterized in that, include: The data acquisition unit is used to acquire multi-angle reflectivity datasets of the surface of the functional metal-glass component under test. The signal separation unit is used to separate strong diffraction spectra and weak diffraction spectra based on the multi-angle reflectance dataset, and to determine the diffraction residue ratio. The strong diffraction spectrum is the reflectance spectrum with the largest standard deviation of spectral intensity in the multi-angle reflectance dataset, and the weak diffraction spectrum is the reflectance spectrum with the smallest standard deviation of spectral intensity in the multi-angle reflectance dataset. The diffraction residual ratio is used to characterize the amplitude ratio of the residual geometric scattering signal in the weak diffraction spectrum; The geometric modeling unit is used to determine the geometric deformation parameters of the surface of the functional metal glass element under test based on the strong diffraction spectrum, and to construct a simulated diffraction waveform based on the geometric deformation parameters. The geometric deformation parameters are used to quantify the size and morphological changes of the surface periodic structure caused by the molding process; the simulated diffraction waveform is used to characterize the diffraction energy distribution caused only by the surface periodic structure. The parameter inversion unit is used to determine the intrinsic optical parameters of the functional metallic glass based on the weak diffraction spectrum, the diffraction residual ratio, and the simulated diffraction waveform.

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