A method, device and medium for determining a forbidden band width based on linear region identification

Abstract

This invention discloses a method, device, and medium for determining bandgap width based on linear region identification. The method includes: acquiring spectral data of the material to be tested and converting the spectral data into a Tauc plot; segmenting the Tauc plot into data segments and calculating the linear correlation coefficient of each data segment; identifying multiple candidate linear regions within the data segments based on the linear correlation coefficient; filtering target linear regions based on the candidate linear regions and performing linear fitting on the target linear regions to obtain the bandgap width of the material to be tested. By segmenting the Tauc plot into data segments and calculating the linear correlation coefficient for linear region filtering, the objectivity, accuracy, robustness, and efficiency of bandgap width determination can be improved, avoiding the errors and inefficiencies of manual bandgap width determination.

Description

Technical Field

[0001] This invention relates to the field of material physical property analysis and characterization technology, and in particular to a method, device and medium for determining the bandgap width based on linear region identification. Background Technology

[0002] Bandgap is one of the most important physical parameters of semiconductor and insulator materials. It determines the optoelectronic properties of the materials and is the basis for designing and developing new optoelectronic devices such as solar cells, light-emitting diodes, lasers and detectors.

[0003] In existing technologies, the bandgap of materials is typically determined by plotting a Tauc plot and then performing linear extrapolation within the linear absorption edge region. However, this linear extrapolation often relies on a subjective determination of the linear absorption edge region, requiring the user to manually specify the fitting range. This approach results in drawbacks such as high subjectivity in determining the bandgap, low analytical efficiency, lack of standardized procedures, and insufficient capability for handling complex spectra.

[0004] Therefore, there is an urgent need to provide a method that can objectively, automatically, and accurately determine the bandgap of a material from spectral data. Summary of the Invention

[0005] This invention provides a method, device, and medium for determining the bandgap width based on linear region identification, so as to improve the objectivity, accuracy, robustness, and efficiency of bandgap width determination.

[0006] According to one aspect of the present invention, a method for determining the bandgap width based on linear region identification is provided, the method comprising:

[0007] Acquire the spectral data of the material to be tested, and convert the spectral data into a Tauc plot;

[0008] The Tauc plot is divided into data segments, and the linear correlation coefficient of each data segment is calculated. Based on the linear correlation coefficient, multiple candidate linear regions are determined in the data segments.

[0009] Based on the candidate linear regions, target linear regions are screened, and linear fitting is performed on the target linear regions to obtain the bandgap width of the material to be tested.

[0010] According to another aspect of the present invention, a bandgap width determination device based on linear region identification is provided, the device comprising:

[0011] The Tauc plot determination module is used to acquire the spectral data of the material to be tested and convert the spectral data into a Tauc plot.

[0012] The candidate linear region determination module is used to split the Tauc graph into data segments, calculate the linear correlation coefficient of each data segment, and determine multiple candidate linear regions in the data segments based on the linear correlation coefficient.

[0013] The bandgap width determination module is used to filter target linear regions based on the candidate linear regions and perform linear fitting on the target linear regions to obtain the bandgap width of the material to be tested.

[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0015] At least one processor; and a memory communicatively connected to said at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the bandgap width determination method based on linear region identification as described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the bandgap width determination method based on linear region identification as described in any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the bandgap width determination method based on linear region identification as described in any embodiment of the present invention.

[0019] The technical solution of this invention involves acquiring the spectral data of the material to be tested and converting it into a Tauc plot; splitting the Tauc plot into data segments and calculating the linear correlation coefficient of each data segment; determining multiple candidate linear regions in the data segments based on the linear correlation coefficient; screening target linear regions based on the candidate linear regions and performing linear fitting on the target linear regions to obtain the bandgap width of the material to be tested. This solves the problem of determining the bandgap width of the material. By splitting the Tauc plot into data segments and calculating the linear correlation coefficient to screen linear regions, the objectivity, accuracy, robustness, and efficiency of determining the bandgap width can be improved, avoiding the errors and inefficiencies of manually determining the bandgap width.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0022] Figure 1 This is a flowchart of a method for determining the bandgap width based on linear region identification according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a visual representation diagram of the bandgap width provided in Embodiment 1 of the present invention;

[0024] Figure 3 This is a flowchart of a method for determining the bandgap width based on linear region identification according to Embodiment 2 of the present invention;

[0025] Figure 4 This is an application flowchart of a method for determining the bandgap width based on linear region identification according to Embodiment 2 of the present invention;

[0026] Figure 5 This is a schematic diagram of a bandgap width determination device based on linear region identification according to Embodiment 3 of the present invention;

[0027] Figure 6 This is a schematic diagram of the structure of an electronic device that implements the bandgap width determination method based on linear region identification according to the embodiments of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] Figure 1 This is a flowchart of a method for determining the bandgap width based on linear region identification according to Embodiment 1 of the present invention. This embodiment is applicable to the determination of the bandgap width of materials during the research and development and quality control of materials such as semiconductors, photovoltaics, and catalysts. This method can be executed by a bandgap width determination device based on linear region identification. This device can be implemented in hardware and / or software and can be configured in electronic devices such as computers, servers, spectrometers, or photometers. Figure 1 As shown, the method includes:

[0032] Step 110: Obtain the spectral data of the material to be tested and convert the spectral data into a Tauc plot.

[0033] The material to be tested can be a novel material such as a semiconductor, photovoltaic, or catalyst. Spectral data can be information such as wavelength, energy, and light intensity of the material to be tested, acquired by a spectrometer in test mode. By analyzing the absorption, transmission, or diffuse reflection of light at different wavelengths, the band gap of the material to be tested can be determined. For example, the spectral data can be the ultraviolet-visible-infrared (UV-Vis-NIR) spectral data of the material to be tested. The band gap of the material to be tested can be measured using UV-Vis-NIR spectral data.

[0034] Spectral data can be converted into Tauc plots. Tauc plots can be based on relationships. Generated. In the formula, Let be the absorption coefficient of the material under test, hv be the incident photon energy, Eg be the band gap, A be a constant, and the exponent n be the type of electronic transition in the material under test. (Drawing...) The relationship between hv and the photon energy can be used to obtain the Tauc diagram. That is, in the Tauc diagram, the horizontal axis represents the photon energy hv, and the vertical axis represents the photon energy hv. .

[0035] When converting spectral data into Tauc plots, the absorption coefficient, photon energy, and transition type of the material under test can be determined based on the spectral data. Preprocessing of the spectral data is also possible before conversion to Tauc plots.

[0036] Optionally, the spectral data is converted into a Tauc plot, including: preprocessing the spectral data; determining the absorption coefficient based on the test mode of the spectral data; determining the corresponding photon energy based on the preprocessed spectral data; and determining the Tauc plots of the direct bandgap and the indirect bandgap based on the photon energy and the absorption coefficient.

[0037] Preprocessing of spectral data can involve denoising and smoothing operations to eliminate random noise interference in the analysis. For example, the original spectral data can be smoothed using a convolutional smoothing filtering algorithm based on local polynomial fitting (Savitzky-Golay, SG). The window size for SG filtering can be 5-15 data points (e.g., fixed at 10 data points), and the polynomial order can be 2-4 (e.g., 3rd order). By limiting the number of data points in the window and the polynomial order, the smoothing performance of the spectral data can be improved.

[0038] When determining the absorption coefficient based on spectral data, it can be specifically determined based on the test mode of the spectral data. The method of determining the absorption coefficient can differ depending on the test mode. Optionally, determining the absorption coefficient based on the test mode of the spectral data includes: determining the absorption coefficient based on the absorptivity when the test mode is absorption mode; determining the absorption coefficient based on the transmittance when the test mode is transmission mode; and determining the absorption coefficient based on the reflectance when the test mode is diffuse reflectance mode.

[0039] For example, in absorption mode, the spectrometer can output the absorbance of the material being tested, and the absorption coefficient is proportional to the absorbance. For instance, the absorption coefficient... Let A be the absorptivity and d be the thickness of the material being tested. In transmission mode, the spectrometer can output the transmittance T of the material being tested. The absorption coefficient is proportional to the negative of the logarithm of T to the base 10, i.e. For example, absorption coefficient In diffuse reflectance mode, the spectrometer can output the reflectance R of the material being tested, which can be converted into an absorption coefficient. For example, the Kubeka-Munk function (KM) can be used to determine the absorption coefficient. .

[0040] Photon energy The unit of photon energy is the electron volt (eV). The wavelength is measured in nanometers (nm).

[0041] When plotting Tauc diagrams, different transition types can be plotted based on photon energy and absorption coefficient. The transition type of the material under test can be selected from the set {2, 1 / 2, 2 / 3, 1 / 3}. Here, n=2 represents a directly allowed transition; n=1 / 2 represents an indirectly allowed transition; n=2 / 3 represents a directly forbidden transition; and n=1 / 3 represents an indirectly forbidden transition. Multiple transition types can be plotted depending on the exponent n. Tauc plot for hv. In this embodiment of the invention, it can be plotted when n=2. Tauc plot of the direct bandgap for hv; plotting when n=1 / 2. Tauc plot of the indirect band gap for hv.

[0042] By analyzing various types of Tauc diagrams, the band gap of the material under test can be obtained under different conditions, which can provide researchers with more comprehensive material band information for accurate analysis of the material's optoelectronic properties.

[0043] Step 120: Divide the Tauc plot into data segments and calculate the linear correlation coefficient of each data segment. Based on the linear correlation coefficient, determine multiple candidate linear regions in the data segments.

[0044] There are several ways to segment the data in a Tauc plot. For example, the Tauc plot can be divided into multiple initial seed segments using a preset data length; the linear correlation coefficient of each initial seed segment can be calculated; when the linear correlation coefficient is greater than or equal to a preset correlation coefficient threshold, the length of the initial seed segment is recursively increased by a preset step size, and the linear correlation coefficient is recalculated until the linear correlation coefficient is less than the preset correlation coefficient threshold or the initial seed segment reaches the end of the spectrum; one or more initial seed segments obtained can be used as candidate linear regions.

[0045] Alternatively, the entire Tauc plot can be used as the initial data segment. The linear correlation coefficient of the initial data segment is calculated. When the linear correlation coefficient is greater than or equal to a preset correlation coefficient threshold, the initial data segment is selected as a candidate linear region. When the linear correlation coefficient is less than the preset correlation coefficient threshold, the initial data segment is divided into two data sub-segments. Each data sub-segment is updated as the initial data segment, and the process of calculating the linear correlation coefficient of the initial data segment is repeated until each initial data segment meets the iteration termination condition, resulting in multiple candidate linear regions.

[0046] By segmenting the Tauc plot into data segments using various methods, calculating the linear correlation coefficient of each segment, and further segmenting or lengthening the data segments based on the linear correlation coefficient, multiple candidate linear regions can be obtained. The linear correlation coefficient of each candidate linear region is greater than or equal to a preset correlation coefficient threshold, and can be used as the basis for determining the bandgap width of the material under test, thereby improving the accuracy, objectivity, and efficiency of determining the material's bandgap width.

[0047] The linear correlation coefficient can be found in the data segment. linear fit between hv The linear correlation coefficient can be determined in several ways, such as by squared Pearson correlation coefficient or by automatically determining it using a scatter plot with a calculation tool.

[0048] The preset correlation coefficient threshold can be a value greater than or equal to 0.98, such as 0.999. The iteration termination condition can be that the linear correlation coefficient of each initial data segment is greater than or equal to the preset correlation coefficient threshold, or that the length of the initial data segment is less than or equal to the preset data length. The preset data length can be 1-5 data points, such as 2 data points.

[0049] Step 130: Filter the target linear region based on the candidate linear region, and perform linear fitting on the target linear region to obtain the bandgap width of the material to be tested.

[0050] There are several ways to select the target linear region. For example, one could sort the candidate linear regions and select the top N with the highest linear correlation coefficients as the target linear regions. Alternatively, other constraints could be considered in the selection of the target linear region to ensure the reliability of the determined bandgap width of the material under test.

[0051] When multiple target linear regions exist, linear fitting can be performed on each target linear region separately. By linear extrapolating from each target linear region, the intersection points with the energy axis (horizontal axis) in the Tauc plot can be obtained. The x-coordinate of each intersection point is taken as the bandgap of the material under test. The linear fitting can be linear least squares fitting, yielding the linear equation y = kx + b. The x-value of the intersection point of this line with the energy axis (y = 0) is the bandgap of the material under test, Eg = -b / k.

[0052] Optionally, linear fitting is performed on the target linear region to obtain the bandgap width of the material under test, including: linear fitting of the target linear region corresponding to the Tauc plot of the direct bandgap to obtain the bandgap width of the direct bandgap of the material under test; linear fitting of the target linear region corresponding to the Tauc plot of the indirect bandgap to obtain the bandgap width of the indirect bandgap of the material under test; and displaying the bandgap width of the direct bandgap and the bandgap width of the indirect bandgap side by side in the same visualization.

[0053] For example, Figure 2 This is a schematic diagram illustrating the visualization of bandgap width according to Embodiment 1 of the present invention. For example, the material to be tested is a semiconductor powder with a direct bandgap. Its diffuse reflectance spectrum is measured using a UV-Vis-NIR spectrometer, obtaining spectral data including wavelength (range 200-1100 nm) and corresponding diffuse reflectance (%R). A Savitzky-Golay filter window is set to 10 points, and the polynomial order is 3 to smooth the reflectance data in the spectral data. In diffuse reflectance mode, the Kubelka-Munk function is used to convert the smoothed reflectance R into a KM function value F(R). Then, the photon energy hv is calculated based on the wavelength. Simultaneously, a plot is drawn... The direct bandgap Tauc plot for hv; and For the indirect bandgap Tauc plot of hv, the Tauc plot is divided into data segments, and the linear correlation coefficient of each segment is calculated. Based on the linear correlation coefficient, multiple candidate linear regions in the direct bandgap Tauc plot and multiple candidate linear regions in the indirect bandgap Tauc plot are identified within the data segments. Target linear regions in the direct bandgap Tauc plot are selected based on the candidate linear regions in the direct bandgap Tauc plot; and linear fitting of the target linear regions yields y=kx+b, calculating the direct bandgap bandgap width Eg=-b / k. Similarly, target linear regions in the indirect bandgap Tauc plot are selected based on the candidate linear regions in the indirect bandgap Tauc plot; and linear fitting of the target linear regions yields y=kx+b, calculating the indirect bandgap bandgap width Eg=-b / k. The direct and indirect bandgap bandgap widths are then displayed side-by-side in the same visualization, as shown below. Figure 2 The results are shown below. Figure 2 The solid line represents the preprocessed and smoothed Tauc plot curve; the dashed line represents the fitted straight line of the target linear region automatically identified and screened using the method of this invention. The vertical line of the dashed line indicates the bandgap width value calculated based on the fitted straight line. Figure 2 The left side of the middle band gap is an indirect band gap, and the linear fitting equation is y=3.63x-9.04, which yields Eg≈2.49eV. Figure 2The right side represents the direct bandgap. Two target linear regions were obtained through screening of the direct bandgap, and linear fitting yielded the equations y = 52.34x - 146.75 and y = 18.39x - 53.11, respectively. Eg≈2.80eV and Eg≈2.89eV were calculated for these values. (The text then abruptly shifts to a different topic: "Through...") Figure 2 As shown, the direct bandgap and indirect bandgap are displayed simultaneously. There may be multiple bandgap widths in the direct bandgap and multiple bandgap widths in the indirect bandgap. By displaying multiple types and multiple bandgap widths, researchers can accurately grasp the properties of materials and perform accurate performance analysis.

[0054] Optionally, the material to be tested can be a direct bandgap semiconductor, an indirect bandgap semiconductor, or a multiphase material. When the material to be tested has multiple phases or defect states, the Tauc plot may show multiple linear segments, such as... Figure 2 As shown on the right, the method of determining the target linear region by splitting the Tauc plot data segments and combining them with the linear correlation coefficient provided by the embodiments of the present invention can automatically identify all possible linear regions for researchers to refer to and select, thereby effectively addressing the analytical needs of complex spectra and avoiding the unreliability of manual screening.

[0055] The technical solution of this invention involves acquiring the spectral data of the material to be tested and converting it into a Tauc plot; splitting the Tauc plot into data segments and calculating the linear correlation coefficient of each data segment; determining multiple candidate linear regions in the data segments based on the linear correlation coefficient; screening target linear regions based on the candidate linear regions and performing linear fitting on the target linear regions to obtain the bandgap width of the material to be tested. This solves the problem of determining the bandgap width of the material. By splitting the Tauc plot into data segments and calculating the linear correlation coefficient to screen linear regions, the objectivity, accuracy, robustness, and efficiency of determining the bandgap width can be improved, avoiding the errors and inefficiencies of manually determining the bandgap width.

[0056] Example 2

[0057] Figure 3 This is a flowchart of a method for determining the bandgap width based on linear region identification according to Embodiment 2 of the present invention. This embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment can be combined with various optional solutions in one or more of the above embodiments.

[0058] Optionally, the Tauc plot is split into data segments, and the linear correlation coefficient of each data segment is calculated. Based on the linear correlation coefficient, multiple candidate linear regions are determined in the data segments, including: using the Tauc plot as the initial data segment and calculating the linear correlation coefficient of the initial data segment; when the linear correlation coefficient is greater than or equal to a preset correlation coefficient threshold, the initial data segment is selected as a candidate linear region; when the linear correlation coefficient is less than the preset correlation coefficient threshold, the initial data segment is divided into two data sub-segments; each data sub-segment is updated to the initial data segment, and the step of calculating the linear correlation coefficient of the initial data segment is returned until each initial data segment meets the iteration termination condition, resulting in multiple candidate linear regions.

[0059] Optionally, multiple candidate linear regions are determined in the data segment based on the linear correlation coefficient, including: sorting each candidate linear region according to the photon energy of the corresponding spectral data to obtain the sorting result; calculating the merged linear correlation coefficient when merging adjacent candidate linear regions in the sorting result; merging adjacent candidate linear regions when the merged linear correlation coefficient is greater than or equal to a preset correlation coefficient threshold; otherwise, keeping adjacent candidate linear regions independent; and updating the candidate linear regions based on the result of merging adjacent candidate linear regions.

[0060] like Figure 3 As shown, the method includes:

[0061] Step 310: Obtain the spectral data of the material to be tested and convert the spectral data into a Tauc plot.

[0062] Optionally, the spectral data is converted into a Tauc plot, including: preprocessing the spectral data; determining the absorption coefficient based on the test mode of the spectral data; determining the corresponding photon energy based on the preprocessed spectral data; and determining the Tauc plots of the direct bandgap and the indirect bandgap based on the photon energy and the absorption coefficient.

[0063] Optionally, the absorption coefficient can be determined based on the test mode of the spectral data, including: when the test mode of the spectral data is absorption mode, determining the absorption coefficient based on the absorptivity; when the test mode of the spectral data is transmission mode, determining the absorption coefficient based on the transmittance; and when the test mode of the spectral data is diffuse reflection mode, determining the absorption coefficient based on the reflectance.

[0064] Step 320: Using the Tauc plot as the initial data segment, calculate the linear correlation coefficient of the initial data segment.

[0065] Step 330: When the linear correlation coefficient is greater than or equal to the preset correlation coefficient threshold, the initial data segment is taken as a candidate linear region.

[0066] For example, the preset correlation coefficient threshold is 0.999.

[0067] Step 340: When the linear correlation coefficient is less than the preset correlation coefficient threshold, the initial data segment is divided into two data sub-segments.

[0068] For example, the initial data segment can be divided in half into two sub-segments from the middle. Alternatively, other partitioning methods can be used, such as partitioning in a 4:6 or 3:7 ratio.

[0069] Step 350: Update each data segment to the initial data segment, return to the step of calculating the linear correlation coefficient of the initial data segment, until each initial data segment meets the iteration termination condition, and obtain multiple candidate linear regions.

[0070] Each data segment can be updated to the initial data segment, and the linear correlation coefficient of each initial data segment is recalculated. Then, it is determined whether the linear correlation coefficient is greater than or equal to a preset correlation coefficient threshold. If so, the initial data segment is considered a candidate linear region; otherwise, the initial data segment is divided into two data segments, and each data segment is updated to the initial data segment. This process is iterated until each initial data segment meets the iteration termination condition, resulting in multiple candidate linear regions. The iteration termination condition is that the linear correlation coefficient of the initial data segment is greater than or equal to the preset correlation coefficient threshold, or the length of the initial data segment is less than or equal to a preset length (e.g., 2 data points). This top-down recursive binary search strategy efficiently locates all data regions that meet the linearity condition within the entire Tauc graph, yielding multiple candidate linear regions.

[0071] Step 360: Sort each candidate linear region according to the photon energy of the corresponding spectral data to obtain the sorting result.

[0072] For example, the candidate linear region can be sorted in ascending order based on the median photon energy of the spectral data, which is the median value of the horizontal coordinate of the corresponding horizontal axis segment, to obtain the sorting result.

[0073] Step 370: Calculate the merging linear correlation coefficient when merging adjacent candidate linear regions in the sorting results.

[0074] For two adjacent candidate linear regions in the sorting results, you can try to merge them and calculate the merged linear correlation coefficient.

[0075] Step 380: When the merged linear correlation coefficient is greater than or equal to the preset correlation coefficient threshold, merge adjacent candidate linear regions; otherwise, keep adjacent candidate linear regions independent; update the candidate linear regions based on the result of merging adjacent candidate linear regions.

[0076] When the current candidate linear region successfully merges with the adjacent next candidate linear region, the current candidate linear region and the adjacent next candidate linear region can be updated as a whole into the current candidate linear region, and merging of the current candidate linear region with subsequent adjacent candidate linear regions can continue. When merging the current candidate linear region with the adjacent next candidate linear region fails, the current candidate linear region can be saved, and merging with subsequent adjacent candidate linear regions can continue starting from the next candidate linear region. This merging process can be completed in a single sequential scan. By merging adjacent candidate linear regions, the length of the linear region can be maximized, more accurately reflecting the bandgap width of the material.

[0077] Step 390: Filter the target linear region based on the candidate linear region, and perform linear fitting on the target linear region to obtain the bandgap width of the material to be tested.

[0078] Optionally, the target linear region is screened based on the candidate linear regions, including: preliminary screening of the candidate linear regions according to at least one of the following screening conditions: the band gap of the candidate linear region is within a preset range, the minimum photon energy of the candidate linear region is less than a preset energy value, the linear fitting slope of the candidate linear region is greater than a preset slope value, and the span of the candidate linear region on the vertical axis is greater than the full range of the vertical axis of the Tauc plot with a preset ratio; the candidate linear regions obtained from the preliminary screening are sorted in descending order according to the corresponding linear correlation coefficient, and the candidate linear regions ranked in the top N positions are taken as the target linear regions.

[0079] For example, the bandgap width of the candidate linear region is within a preset range, such as the bandgap value Eg must be within a reasonable physical range of 1.5 eV to 4.5 eV. The minimum photon energy value of the candidate linear region is less than a preset energy value, such as the minimum photon energy value of the candidate linear region must be less than 4.5 eV. The linear fitting slope of the candidate linear region is greater than a preset slope value, such as the linear fitting slope of the candidate linear region must be greater than 0.1, and the linear fitting slope must be greater than 30% of the difference between the maximum and minimum values ​​of the vertical axis of the Tauc plot. The span of the candidate linear region on the vertical axis is greater than a preset proportion of the entire vertical axis range of the Tauc plot, such as the span of the candidate linear region on the vertical axis must be greater than 8% of the entire vertical axis range of the Tauc plot.

[0080] In practical applications, one or more of the above physical rules can be used to initially filter out physically unreasonable results and improve the reliability of bandgap width determination.

[0081] Based on the initial screening, the candidate linear regions can be further filtered. For example, they can be sorted in descending order according to their corresponding linear correlation coefficients, and the top N candidate linear regions can be selected as target linear regions. Here, N can be a number less than or equal to 5, such as N=3. Linear fitting can then be performed on each target linear region to calculate the corresponding bandgap.

[0082] Optionally, linear fitting is performed on the target linear region to obtain the bandgap width of the material under test, including: linear fitting of the target linear region corresponding to the Tauc plot of the direct bandgap to obtain the bandgap width of the direct bandgap of the material under test; linear fitting of the target linear region corresponding to the Tauc plot of the indirect bandgap to obtain the bandgap width of the indirect bandgap of the material under test; and displaying the bandgap width of the direct bandgap and the bandgap width of the indirect bandgap side by side in the same visualization.

[0083] The technical solution of this invention involves acquiring spectral data of the material to be tested and converting it into a Tauc plot; using the Tauc plot as an initial data segment and calculating the linear correlation coefficient of the initial data segment; when the linear correlation coefficient is greater than or equal to a preset correlation coefficient threshold, the initial data segment is selected as a candidate linear region; when the linear correlation coefficient is less than the preset correlation coefficient threshold, the initial data segment is divided into two data sub-segments; each data sub-segment is updated to the initial data segment, and the process of calculating the linear correlation coefficient of the initial data segment is repeated until each initial data segment meets the iteration termination condition, resulting in multiple candidate linear regions; each candidate linear region is sorted according to the photon energy of the corresponding spectral data to obtain a sorting result; the merged linear correlation coefficient of adjacent candidate linear regions in the sorting result is calculated; when the merged linear correlation coefficient is greater than or equal to a preset correlation coefficient threshold, adjacent candidate linear regions are merged; otherwise, adjacent candidate linear regions remain independent; the candidate linear regions are updated based on the result of merging adjacent candidate linear regions; target linear regions are screened based on the candidate linear regions, and linear fitting is performed on the target linear regions to obtain the bandgap width of the material to be tested, thus solving the problem of determining the bandgap width of the material.

[0084] By objectively segmenting the Tauc plot based on the linear correlation coefficient, errors caused by subjective human factors can be eliminated, ensuring that the calculation results for the same spectral data are unique and repeatable. The determination of the band gap is fully automated, reducing the manual analysis time that originally required tens of minutes or even hours to just a few seconds, thus improving the efficiency of band gap determination. The recursive binary search algorithm accurately locates candidate linear regions that meet the linearity condition, and the multi-dimensional physical rules effectively eliminate physically unreasonable results, ensuring that the output band gap value has practical physical meaning. The standardized analysis process and objective judgment criteria lay the foundation for establishing a unified evaluation system for the optical performance of materials. The system simultaneously calculates and outputs both direct and indirect band gap results, displaying them side by side in the same visualization, providing researchers with more comprehensive information on the material's band structure.

[0085] Figure 4 This is an application flowchart of a bandgap width determination method based on linear region identification according to Embodiment 2 of the present invention. Figure 4 As shown, UV-Vis-NIR spectral data of the material under test can be acquired, and the spectral data can be preprocessed and test mode identified. Preprocessing may include baseline correction and noise removal. Test modes include absorption mode, transmission mode, and diffuse reflection mode. The spectral data is converted into a Tauc plot, and the absorption coefficient can be determined according to the test mode. The exponent n is determined based on the material type, and the photon energy hv is determined based on the wavelength of the spectral data. The calculation is then performed. ,draw The Tauc plot of hv is used. Candidate linear regions can be identified within the Tauc plot, for example, using the recursive bisection algorithm and data segment merging algorithm described in this embodiment to identify linear regions in the Tauc plot. Target linear regions can be determined by filtering candidate linear regions using physical rules. The bandgap width of the test material is obtained by linear fitting the target linear region. The bandgap width of the test material can be visualized. This method achieves automated and objective identification of linear regions in the Tauc plot, significantly improving the accuracy, repeatability, and analysis efficiency of bandgap width calculation. This method can be widely applied in the research and development and quality control of new materials such as semiconductors, photovoltaics, and catalysts.

[0086] Example 3

[0087] Figure 5 This is a schematic diagram of a bandgap width determination device based on linear region identification according to Embodiment 3 of the present invention. Figure 5 As shown, the device includes: a Tauc map determination module 510, a candidate linear region determination module 520, and a bandgap width determination module 530. Wherein:

[0088] Tauc plot determination module 510 is used to acquire the spectral data of the material to be tested and convert the spectral data into a Tauc plot;

[0089] The candidate linear region determination module 520 is used to split the Tauc plot into data segments, calculate the linear correlation coefficient of each data segment, and determine multiple candidate linear regions in the data segments based on the linear correlation coefficient.

[0090] The bandgap width determination module 530 is used to screen the target linear region based on the candidate linear region and perform linear fitting on the target linear region to obtain the bandgap width of the material to be tested.

[0091] Candidate linear region determination module 520 includes:

[0092] The linear correlation coefficient calculation unit is used to calculate the linear correlation coefficient of the initial data segment using the Tauc plot as the initial data segment.

[0093] The candidate linear region determination unit is used to identify the initial data segment as a candidate linear region when the linear correlation coefficient is greater than or equal to a preset correlation coefficient threshold.

[0094] The data segmentation unit is used to divide the initial data segment into two data sub-segments when the linear correlation coefficient is less than a preset correlation coefficient threshold.

[0095] The iteration termination unit is used to update each data segment to the initial data segment, return to the step of calculating the linear correlation coefficient of the initial data segment, until each initial data segment meets the iteration termination condition, and obtain multiple candidate linear regions.

[0096] Optionally, the candidate linear region determination module 520 includes:

[0097] The region sorting unit is used to sort each candidate linear region according to the photon energy of the corresponding spectral data to obtain the sorting result;

[0098] The merged linear correlation coefficient calculation unit is used to calculate the merged linear correlation coefficient when merging adjacent candidate linear regions in the sorting results.

[0099] The region merging unit is used to merge adjacent candidate linear regions when the merging linear correlation coefficient is greater than or equal to a preset correlation coefficient threshold; otherwise, it keeps adjacent candidate linear regions independent.

[0100] The candidate linear region update unit is used to update the candidate linear region based on the result of merging adjacent candidate linear regions.

[0101] Optionally, the bandgap width determination module 530 includes:

[0102] The preliminary screening unit is used to perform preliminary screening of candidate linear regions based on at least one of the following screening criteria:

[0103] The candidate linear region has a bandgap width within a preset range, a minimum photon energy value less than a preset energy value, a linear fitting slope greater than a preset slope value, and a span of the candidate linear region on the vertical axis greater than a preset ratio of the entire vertical axis range of the Tauc plot.

[0104] The target linear region determination unit is used to sort the candidate linear regions obtained from the initial screening in descending order according to their corresponding linear correlation coefficients, and to take the candidate linear regions that rank in the top N as the target linear regions.

[0105] Optionally, the Tauc diagram determination module 510 includes:

[0106] The preprocessing unit is used to preprocess the spectral data;

[0107] The absorption coefficient determination unit is used to determine the absorption coefficient based on the test mode of the spectral data.

[0108] The Tauc diagram determination unit is used to determine the corresponding photon energy based on the preprocessed spectral data, and to determine the Tauc diagrams of the direct and indirect band gaps based on the photon energy and absorption coefficient.

[0109] Optional, absorption coefficient determination unit, specifically used for:

[0110] When the spectral data is tested in absorption mode, the absorption coefficient is determined based on the absorption rate.

[0111] When the spectral data is tested in transmission mode, the absorption coefficient is determined based on the transmittance.

[0112] When the spectral data is tested in diffuse reflectance mode, the absorption coefficient is determined based on reflectance.

[0113] Optionally, the bandgap width determination module 530 includes:

[0114] The first bandgap width determination unit is used to perform linear fitting on the target linear region corresponding to the Tauc plot of the direct bandgap to obtain the bandgap width of the direct bandgap of the material to be tested.

[0115] The second bandgap width determination unit is used to perform linear fitting on the target linear region corresponding to the Tauc diagram of the indirect bandgap to obtain the bandgap width of the indirect bandgap of the material to be tested.

[0116] The bandgap width display unit is used to display the bandgap width of the direct bandgap and the bandgap width of the indirect bandgap side by side in the same visualization.

[0117] The bandgap width determination device based on linear region identification provided in this embodiment of the invention can execute the bandgap width determination method based on linear region identification provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0118] Example 4

[0119] Figure 6 A schematic diagram of an electronic device 10, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0120] like Figure 6 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) or random access memory (RAM), communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded into the RAM 13 from the storage unit 18. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. Input / output (I / O) interfaces are also connected to the bus 14.

[0121] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0122] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the bandgap width determination method based on linear region identification.

[0123] In some embodiments, the bandgap width determination method based on linear region identification can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the bandgap width determination method based on linear region identification described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the bandgap width determination method based on linear region identification by any other suitable means (e.g., by means of firmware).

[0124] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0125] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0126] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0127] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0128] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0129] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0130] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0131] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining the bandgap width based on linear region identification, characterized in that, include: Acquire the spectral data of the material to be tested, and convert the spectral data into a Tauc plot; The Tauc plot is divided into data segments, and the linear correlation coefficient of each data segment is calculated. Based on the linear correlation coefficient, multiple candidate linear regions are determined in the data segments. Based on the candidate linear regions, target linear regions are screened, and linear fitting is performed on the target linear regions to obtain the bandgap width of the material to be tested.

2. The method according to claim 1, characterized in that, The Tauc plot is segmented into data segments, and the linear correlation coefficient of each data segment is calculated. Based on the linear correlation coefficient, multiple candidate linear regions are determined in the data segments, including: Using the Tauc plot as the initial data segment, calculate the linear correlation coefficient of the initial data segment; When the linear correlation coefficient is greater than or equal to a preset correlation coefficient threshold, the initial data segment is selected as a candidate linear region. When the linear correlation coefficient is less than the preset correlation coefficient threshold, the initial data segment is divided into two data sub-segments; Each data segment is updated to the initial data segment, and the step of calculating the linear correlation coefficient of the initial data segment is returned until each initial data segment meets the iteration termination condition, resulting in multiple candidate linear regions.

3. The method according to claim 1, characterized in that, Based on the linear correlation coefficient, multiple candidate linear regions are determined in the data segment, including: The candidate linear regions are sorted according to the photon energy of their corresponding spectral data to obtain the sorting results; Calculate the merging linear correlation coefficient when merging adjacent candidate linear regions in the sorting results; When the merged linear correlation coefficient is greater than or equal to a preset correlation coefficient threshold, adjacent candidate linear regions are merged. Otherwise, keep adjacent candidate linear regions independent; The candidate linear regions are updated based on the result of merging adjacent candidate linear regions.

4. The method according to claim 1, characterized in that, Target linear region filtering based on the candidate linear regions includes: The candidate linear regions are initially screened based on at least one of the following screening criteria: The bandgap width of the candidate linear region is within a preset range, the minimum photon energy value of the candidate linear region is less than a preset energy value, the linear fitting slope of the candidate linear region is greater than a preset slope value, and the span of the candidate linear region on the vertical axis is greater than a preset proportion of the entire range of the vertical axis of the Tauc plot. The candidate linear regions obtained from the initial screening are sorted in descending order according to their corresponding linear correlation coefficients, and the candidate linear regions ranked in the top N are taken as the target linear regions.

5. The method according to claim 1, characterized in that, Converting the spectral data into a Tauc plot includes: The spectral data is preprocessed; The absorption coefficient is determined based on the test mode of the spectral data; The corresponding photon energy is determined based on the preprocessed spectral data, and the Tauc diagrams of the direct and indirect band gaps are determined based on the photon energy and the absorption coefficient.

6. The method according to claim 5, characterized in that, The absorption coefficient is determined based on the test mode of the spectral data, including: When the test mode of the spectral data is absorption mode, the absorption coefficient is determined based on the absorption rate; When the test mode of the spectral data is transmission mode, the absorption coefficient is determined based on the transmittance; When the test mode of the spectral data is diffuse reflectance mode, the absorption coefficient is determined based on the reflectance.

7. The method according to claim 5, characterized in that, Linear fitting is performed on the target linear region to obtain the bandgap of the material under test, including: Linear fitting is performed on the target linear region corresponding to the Tauc plot of the direct bandgap to obtain the band gap width of the direct bandgap of the material under test; Linear fitting is performed on the target linear region corresponding to the Tauc plot of the indirect bandgap to obtain the band gap width of the indirect bandgap of the material under test; The bandgap widths of the direct bandgap and the indirect bandgap are displayed side-by-side in the same visualization.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to said at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which enables the at least one processor to perform the bandgap width determination method based on linear region identification as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the bandgap width determination method based on linear region identification as described in any one of claims 1-7.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the bandgap width determination method based on linear region identification according to any one of claims 1-7.

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