Material classification method and device based on ultraviolet light electron spectrum, medium and product

Abstract

The invention discloses a material classification method and device based on an ultraviolet electron spectrum, a medium and a product, and relates to the technical field of material surface science and data analysis. The method comprises the following steps: acquiring ultraviolet light electron energy spectrum data of a target material to be analyzed, and extracting a first spectrum curve from the energy spectrum data; obtaining a conductor characteristic identification result of the target material according to the first spectrum curve; under the condition of determining that the target material does not have the conductor characteristics based on the conductor characteristic identification result, extracting a second spectrum curve in a preset valence band top energy interval extending towards the negative energy direction by taking the Fermi level as a starting point from the ultraviolet light electron spectrum data, and performing multi-dimensional characteristic fusion on the second spectrum curve, generating a semiconductor characteristic identification result of the target material based on the fusion result; and determining a classification result of the target material according to the semiconductor characteristic identification result. According to the scheme, the conductive attributes of the target material can be quickly and accurately classified.

Description

Technical Field

[0001] This invention relates to the field of materials surface science and data analysis technology, and in particular to a material classification method, device, medium and product based on ultraviolet photoelectron spectroscopy. Background Technology

[0002] Ultraviolet photoelectron spectroscopy (UPS) is a key analytical technique widely used for characterizing the electronic structure of material surfaces, and it plays an irreplaceable role, especially in the research and development of semiconductors, organic optoelectronic devices, and functional thin film materials.

[0003] At present, the determination of the conductivity properties (conductor or semiconductor) of a material mainly relies on the empirical interpretation of the UPS spectrum by human: analysts subjectively judge the material type by observing whether there is a clear "Fermi edge" near the Fermi level and the position of the valence band top.

[0004] Over-reliance on human experience leads to highly subjective, inefficient, and difficult-to-reproduce judgments on the conductivity properties of materials. Summary of the Invention

[0005] This invention provides a material classification method, device, medium, and product based on ultraviolet photoelectron spectroscopy, which solves the technical problems of subjective, inefficient, and difficult-to-reproduce material conductivity property judgments due to reliance on human experience, and achieves rapid and accurate classification of the conductivity properties of target materials.

[0006] According to one aspect of the present invention, a material classification method based on ultraviolet photoelectron spectroscopy is provided, the method comprising:

[0007] Acquire the ultraviolet photoelectron spectroscopy data of the target material to be analyzed, and extract the first spectral curve within a preset energy range centered on the Fermi level from the ultraviolet photoelectron spectroscopy data;

[0008] Based on the first spectral curve, the conductor properties of the target material are identified to obtain the conductor property identification result of the target material;

[0009] If the target material is determined not to have conductor properties based on the conductor property identification result, a second spectral curve within a preset valence band top energy range extending from the Fermi level to the negative energy direction is extracted from the ultraviolet photoelectron spectroscopy data, and multi-dimensional feature fusion is performed on the second spectral curve. Based on the fusion result, a semiconductor property identification result of the target material is generated.

[0010] Based on the semiconductor characteristic identification results, the classification result of the target material is determined.

[0011] According to another aspect of the present invention, a material classification device based on ultraviolet photoelectron spectroscopy is provided, the device comprising:

[0012] The acquisition module is used to acquire the ultraviolet photoelectron spectroscopy data of the target material to be analyzed, and extract the first spectral curve within a preset energy range centered on the Fermi level from the ultraviolet photoelectron spectroscopy data.

[0013] The conductor property identification result determination module is used to identify the conductor properties of the target material based on the first spectral curve, and obtain the conductor property identification result of the target material.

[0014] The semiconductor characteristic identification result determination module is used to extract a second spectral curve from the ultraviolet photoelectron spectroscopy data when the target material is determined not to have conductor characteristics based on the conductor characteristic identification result. The second spectral curve is then subjected to multi-dimensional feature fusion, and a semiconductor characteristic identification result of the target material is generated based on the fusion result.

[0015] The classification result determination module is used to determine the classification result of the target material based on the semiconductor characteristic identification result.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] 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 material classification method based on ultraviolet photoelectron spectroscopy as described in any embodiment of the present invention.

[0020] 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 material classification method based on ultraviolet photoelectron spectroscopy as described in any embodiment of the present invention.

[0021] 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 material classification method based on ultraviolet photoelectron spectroscopy as described in any embodiment of the present invention.

[0022] The technical solution of this invention involves acquiring ultraviolet photoelectron spectroscopy (UVP) data of the target material to be analyzed, and extracting a first spectral curve within a preset energy range centered on the Fermi level from the UVP data. Conductor properties of the target material are identified based on the first spectral curve to obtain a conductor property identification result. If the conductor property identification result determines that the target material does not possess conductor properties, a second spectral curve is extracted from the UVP data within a preset valence band top energy range extending from the Fermi level towards the negative energy direction. Multidimensional feature fusion is performed on the second spectral curve, and a semiconductor property identification result is generated based on the fusion result. The classification result of the target material is determined based on the semiconductor property identification result. This solution solves the technical problems of high subjectivity, low efficiency, and difficulty in repeating material conductivity property judgments due to reliance on manual experience, and achieves rapid and accurate classification of the conductivity properties of target materials.

[0023] 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

[0024] 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.

[0025] Figure 1 This is a flowchart of a material classification method based on ultraviolet photoelectron spectroscopy provided in Embodiment 1 of the present invention;

[0026] Figure 2 This is a flowchart of a material classification method based on ultraviolet photoelectron spectroscopy provided in Embodiment 2 of the present invention;

[0027] Figure 3 This is a flowchart of another material classification method based on ultraviolet photoelectron spectroscopy provided in Embodiment 2 of the present invention.

[0028] Figure 4 This is a schematic diagram of a conductor characteristic identification process provided in Embodiment 2 of the present invention;

[0029] Figure 5 This is a schematic diagram of a semiconductor characteristic identification process provided in Embodiment 2 of the present invention;

[0030] Figure 6This is a schematic diagram of the structure of a material classification device based on ultraviolet photoelectron spectroscopy according to Embodiment 3 of the present invention;

[0031] Figure 7 This is a schematic diagram of the structure of an electronic device that implements the material classification method based on ultraviolet photoelectron spectroscopy according to embodiments of the present invention. Detailed Implementation

[0032] 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.

[0033] 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.

[0034] Example 1

[0035] Figure 1 This is a flowchart of a material classification method based on ultraviolet photoelectron spectroscopy according to Embodiment 1 of the present invention. This embodiment is applicable to determining the conductivity properties of materials. The method can be executed by a material classification device based on ultraviolet photoelectron spectroscopy, which can be implemented in hardware and / or software. This material classification device can be configured in electronic devices such as computers, servers, or tablet computers. Figure 1 As shown, the method includes:

[0036] Step 110: Obtain the ultraviolet photoelectron spectroscopy data of the target material to be analyzed, and extract the first spectral curve within the preset energy range centered on the Fermi level from the ultraviolet photoelectron spectroscopy data.

[0037] The target material can be any material with unknown conductivity properties. This embodiment does not limit the material type; it can be inorganic semiconductors, organic semiconductors, metallic conductors, insulators, novel low-dimensional materials, doped materials, composite thin films, or multilayer heterostructures, etc. As long as the material can yield valid valence band spectral data through ultraviolet photoelectron spectroscopy, it can be used as the analysis object in this embodiment.

[0038] In this embodiment, the preset energy range can be symmetrically or asymmetrically extended from the Fermi level towards the high binding energy (negative direction) and low binding energy (positive direction). Its specific value can be pre-configured according to the instrument resolution, material type, and analytical requirements. In a typical embodiment, the preset energy range is from -2.0 eV to +2.0 eV (i.e., a total width of 4.0 eV); in other embodiments, this range can also be set to -1.5 eV to +1.5 eV, -3.0 eV to 0 eV, etc., and is not limited in this embodiment.

[0039] The first spectral curve refers to the standardized spectrum extracted from the ultraviolet photoelectron spectroscopy data of the target material within a preset energy range centered on the Fermi level. It reflects the relationship between the electron emission intensity of the material in the region near the Fermi level and the binding energy.

[0040] Optionally, in this embodiment, if it is necessary to determine the conductivity properties of the target material, ultraviolet photoelectron spectroscopy (UV PES) can be performed on the target material to obtain its raw spectral data. Further, a sub-data segment is extracted from this raw spectral data, centered on the Fermi level and with a preset width (e.g., ±2.0 eV, i.e., a total window width of 4.0 eV). For example, this extraction operation can be achieved through data indexing or energy range filtering. Finally, this sub-data segment is normalized (e.g., by dividing the intensity sequence by its maximum value or integral area) to eliminate absolute intensity deviations caused by fluctuations in light source intensity, differences in sample surface condition, or changes in instrument response, thereby obtaining a standardized first spectral curve. This first spectral curve, as input data for subsequent conductor characteristic identification, retains key morphological features near the Fermi level and possesses good comparability and robustness.

[0041] Step 120: Identify the conductor properties of the target material based on the first spectral curve to obtain the conductor property identification results of the target material.

[0042] Optionally, in this embodiment, after obtaining the first spectral curve, the intensity data corresponding to the Fermi level (usually calibrated to 0 eV) and its adjacent energy range in the first spectral curve can be further used as the analysis object; further, the spectral morphology of this local region can be feature extracted. For example, the steepness of the intensity drop at the Fermi level can be evaluated, the presence of obvious intensity abrupt boundary can be detected, and the continuity and trend of intensity changes in adjacent energy ranges can be analyzed; then, the extracted morphological features are compared with a preset conductor discrimination standard. If the morphological features meet the discrimination standard, the target material is determined to have conductor properties; otherwise, it is determined not to have conductor properties, and a corresponding conductor property identification result is generated.

[0043] Understandably, since conductive materials typically exhibit well-defined and significant Fermi edge characteristics in ultraviolet photoelectron spectroscopy, this embodiment employs a conductor-priority judgment strategy to achieve rapid identification and efficient current splitting of typical conductive materials. This strategy is based on a set of conductor discrimination rules targeting Fermi edge morphological characteristics, used to analyze the local behavior of the first spectral curve near the Fermi level (typically calibrated to 0 eV). Among these, the intensity drop rule is used to detect whether the photoelectron count drops sharply and significantly when the binding energy crosses the Fermi level from a positive value (slightly above the Fermi level) to a negative value (valence band direction). This phenomenon directly reflects the continuous distribution of electronic states in the conductor at the Fermi level. This rule can be implemented by calculating quantitative indicators such as the rate of change of intensity, difference value, or slope within a preset energy window and comparing them with empirically or statistically determined thresholds.

[0044] Furthermore, the symmetry rules primarily focus on the overall contour morphology of the Fermi edge: a typical metallic Fermi edge, under ideal conditions, exhibits an approximately "S"-shaped transition centered on the Fermi level, with its rising and falling edges possessing a certain degree of mirror symmetry. By evaluating the degree of morphological symmetry of the spectral curves on both sides of the Fermi level, the authenticity of the Fermi edge can be further verified, and noise or spurious signal interference can be eliminated. When the input spectrum satisfies one or more of the above conductor discrimination rules, the target material can be determined with high confidence to possess conductor properties, and corresponding conductor property identification results can be generated accordingly. In this case, the subsequent semiconductor analysis process can be terminated early, thereby improving overall classification efficiency and reducing computational overhead.

[0045] Step 130: If the target material does not have conductor properties based on the conductor property identification results, extract the second spectral curve from the ultraviolet photoelectron spectroscopy data within the preset valence band top energy range starting from the Fermi level and extending towards the negative energy direction, and perform multi-dimensional feature fusion on the second spectral curve to generate the semiconductor property identification results of the target material based on the fusion results.

[0046] Understandably, when the conductor characteristic identification results indicate that the target material does not have conductor characteristics, it means that there is no significant Fermi edge near its Fermi level, and the material is more likely to be a semiconductor or an insulator. Further analysis of its valence band structure is needed to determine its semiconductor properties.

[0047] Optionally, in this embodiment, after identifying the conductor properties of the target material based on the first spectral curve and obtaining the conductor property identification result of the target material, if it is determined that the target material does not have conductor properties, then a second spectral curve within a preset valence band top energy range extending from the Fermi level to the negative energy direction can be further extracted from the ultraviolet photoelectron spectroscopy data of the target material. Furthermore, multi-dimensional features can be extracted from the second spectral curve and fused together to obtain the semiconductor property identification result of the target material.

[0048] In this embodiment, the preset valence band top energy range refers to the upper and lower limits of the energy range that have been preset according to the valence band width of typical semiconductor materials before the analysis process is started, for example, from 0 eV to -10 eV; the second spectral curve is the curve of the normalized photoelectron intensity as a function of binding energy within this range, which is used to characterize the distribution of valence band electronic state density.

[0049] In one optional implementation of this embodiment, a second spectral curve can be extracted from the original ultraviolet photoelectron spectroscopy data within a preset valence band top energy range extending from the Fermi level towards negative energy. Here, extending from the Fermi level towards negative energy means selecting a continuous range from 0 eV (Fermi level) towards lower energies (i.e., higher binding energies, for example, the range of -1 eV to -15 eV). This range covers the valence band top and its adjacent main valence band region, representing a crucial window reflecting the semiconductor's electronic structure.

[0050] Furthermore, multi-dimensional feature fusion is performed on the second spectral curve. For example, multiple quantitative indicators characterizing the electronic state distribution in the valence band top region can be extracted from the spectral curve. These indicators reflect the physical characteristics of the valence band in terms of position, shape, intensity distribution, and broadening. Then, each indicator is weighted and integrated according to its contribution to semiconductor discrimination, generating a comprehensive fusion result. Finally, based on the comparison between this fusion result and a preset discrimination criterion, a semiconductor characteristic identification result is generated—that is, determining whether the target material possesses semiconductor characteristics.

[0051] Step 140: Determine the classification result of the target material based on the semiconductor characteristic identification results.

[0052] Optionally, in this embodiment, the conductor characteristic identification result can be read first: if it indicates that the target material has conductor characteristics, then regardless of the semiconductor characteristic identification result, the material is directly classified as a conductor material and the subsequent discrimination process is terminated; if the conductor characteristic identification result indicates that the target material does not have conductor characteristics, then the semiconductor characteristic identification result is read further. When the result indicates that the material has semiconductor characteristics, the target material is classified as a semiconductor material; when the result indicates that the material does not have semiconductor characteristics, it is classified as other materials (e.g., insulators, half-metals, or functional materials that cannot be clearly classified).

[0053] The technical solution of this embodiment acquires the ultraviolet photoelectron spectroscopy (UVP) data of the target material to be analyzed, and extracts a first spectral curve within a preset energy range centered on the Fermi level from the UVP data. Based on the first spectral curve, the conductor properties of the target material are identified, yielding a conductor property identification result. If the conductor property identification result determines that the target material does not possess conductor properties, a second spectral curve is extracted from the UVP data within a preset valence band top energy range extending from the Fermi level towards the negative energy direction. Multidimensional feature fusion is performed on the second spectral curve, and a semiconductor property identification result is generated based on the fusion result. Based on the semiconductor property identification result, the classification result of the target material is determined. This solves the technical problem of high subjectivity, low efficiency, and difficulty in repeating material conductivity property judgments due to reliance on manual experience, achieving rapid and accurate classification of the conductivity properties of the target material.

[0054] Example 2

[0055] Figure 2 This is a flowchart of a material classification method based on ultraviolet photoelectron spectroscopy 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. Figure 2 As shown, the method includes:

[0056] Step 210: Obtain the ultraviolet photoelectron spectroscopy data of the target material to be analyzed, and extract the first spectral curve within the preset energy range centered on the Fermi level from the ultraviolet photoelectron spectroscopy data.

[0057] Optionally, in this embodiment, acquiring the ultraviolet photoelectron spectroscopy (UVPS) data of the target material to be analyzed, and extracting a first spectral curve within a preset energy range centered on the Fermi level from the UVPS data, may include: performing UVPS testing on the target material to obtain UVPS data of the target material; wherein the UVPS data includes an energy value sequence and a corresponding electron count value sequence; extracting reference spectral data from the UVPS data whose binding energy is located within an energy window centered on the Fermi level and with a preset width; and normalizing the reference spectral data to obtain the first spectral curve.

[0058] In one optional implementation of this embodiment, the target material to be analyzed can be placed in an ultraviolet photoelectron spectroscopy testing system. Under ultra-high vacuum conditions, a monochromatic ultraviolet light source is used to irradiate the sample surface, exciting photoelectrons with specific kinetic energies. These photoelectrons are separated by kinetic energy by an energy analyzer, and their intensity is recorded by an electron detector, ultimately forming the raw ultraviolet photoelectron spectroscopy data. This data is stored in the form of a discrete sequence, including an energy value sequence (i.e., a binding energy sequence, in eV, usually calibrated to 0 eV at the Fermi level) and a corresponding electron count value sequence (which reflects the photoelectron emission intensity at each energy position and is proportional to the local electronic density of states).

[0059] Furthermore, a subset of the original spectral data is extracted from an energy window centered at the Fermi level (0 eV) with a preset width (e.g., 4.0 eV, corresponding to a binding energy range of -2.0 eV to +2.0 eV), and used as reference spectral data. This energy window covers the key region upon which conductor discrimination depends, including the Fermi edge and the adjacent valence band initiation.

[0060] Finally, the reference spectral data is normalized: each item in the electron count sequence is divided by the maximum value of the sequence, ensuring that the normalized intensity value falls within the [0,1] interval. This eliminates absolute intensity deviations caused by factors such as fluctuations in light source intensity, differences in sample surface morphology, and changes in instrument response function. It is understood that the standardized curve obtained after this processing in this embodiment is the first spectral curve, which retains the morphological characteristics of the original spectrum near the Fermi level and possesses good comparability and robustness, serving as input data for subsequent conductor characteristic identification.

[0061] For example, a nickel oxide (NiO) thin film prepared by solution method has an uncontrolled annealing temperature and oxygen partial pressure, resulting in unknown conductivity properties (it may be a p-type semiconductor, an insulator, or even exhibit conductor-like behavior under high doping). This sample was placed in an ultra-high vacuum ultraviolet photoelectron spectroscopy system and irradiated with a HeI light source. Raw spectral data with a binding energy range of −15.0 eV to +2.0 eV were acquired. The energy axis was calibrated by simultaneously measuring the Fermi edge of a gold standard sample, aligning the Fermi level of the target material to 0 eV, resulting in UPS data consisting of an energy value sequence and its corresponding electron count sequence. Subsequently, reference spectral data within an energy window centered on the Fermi level with a total width of 4.0 eV was extracted. The intensity sequence of this sub-data segment was normalized to generate a first spectral curve with values ​​ranging from [0,1], used for subsequent conductor characteristic identification.

[0062] Step 220: Identify the conductor properties of the target material based on the first spectral curve to obtain the conductor property identification results of the target material.

[0063] Optionally, in this embodiment, identifying the conductor properties of the target material based on the first spectral curve to obtain the conductor property identification result of the target material may include: calculating the first energy derivative of the first spectral curve at the energy position corresponding to the Fermi level; when the absolute value of the first energy derivative is greater than a preset first kurtosis threshold, determining that the target material has obvious Fermi edge characteristics and determining that the target material has conductor properties; when the absolute value of the first energy derivative is not greater than the first kurtosis threshold, calculating the average intensity change rate of the first spectral curve within a second preset energy range centered on the Fermi level; when the absolute value of the average intensity change rate is greater than a preset second intensity change threshold, determining that the target material has obvious Fermi edge characteristics and determining that the target material has conductor properties; when the absolute value of the average intensity change rate is not greater than the second intensity change threshold, determining that the target material does not have obvious Fermi edge characteristics and determining that the target material does not have conductor properties.

[0064] The first-order energy derivative refers to the first-order numerical derivative of photoelectron intensity with respect to binding energy in the first spectral curve. It is used to quantify the local rate of change of the spectrum at a specific energy position (such as the Fermi level). The larger its absolute value, the steeper the spectrum at that point, and the more likely it corresponds to the Fermi edge of the conductor.

[0065] The first steepness threshold is a standard used to determine whether the first energy derivative reaches a significant Fermi edge. For example, it can be 0.3 or other values, and is not limited in this embodiment. The second preset energy range can be a relatively wide local window centered on the Fermi level (such as -0.5eV to +0.5eV), which can be used to evaluate the overall intensity change trend of the Fermi edge region.

[0066] In this embodiment, the second intensity change threshold can be lower than the first kurtosis threshold to capture weak Fermi edge signals. For example, the second intensity change threshold can be set to 0.15. If the calculated average intensity change rate is 0.18, the conductor characteristic is determined to be present; if it is 0.10, it is determined not to possess the characteristic.

[0067] It should be noted that, in this embodiment, the first steepness threshold and the second intensity change threshold can be preset according to the actual instrument resolution, typical material database or historical calibration data. Their specific values ​​(for example, the first steepness threshold is 0.3 and the second intensity change threshold is 0.15) are only illustrative examples and do not constitute a limitation on the scope of protection of this invention.

[0068] In an optional implementation of this embodiment, after obtaining the first spectral curve, the first energy derivative of the first spectral curve at the Fermi level can be further calculated. If the absolute value is greater than a preset first kurtosis threshold, it can be determined that the material has a significant intensity abrupt change near the Fermi level and has obvious Fermi edge characteristics, thereby determining that the target material has conductor properties.

[0069] If the above condition is not met, i.e., the absolute value of the first energy derivative is not greater than the first kurtosis threshold, then the second criterion is further executed: specifically, within the second preset energy range centered on the Fermi level, the average intensity change rate of photoelectron intensity is calculated. When the absolute value of this average intensity change rate is greater than the preset second intensity change threshold, it can still be considered that there is an identifiable Fermi edge feature in the spectrum, and thus the target material is determined to have conductor properties; conversely, if the absolute value of this average intensity change rate is not greater than the second intensity change threshold, it is considered that there is no significant intensity change in the spectrum near the Fermi level, and there is no obvious Fermi edge feature, and the target material is ultimately determined not to have conductor properties.

[0070] Step 230: If the target material does not have conductor properties based on the conductor property identification results, extract the second spectral curve from the ultraviolet photoelectron spectroscopy data within the preset valence band top energy range, starting from the Fermi level and extending towards the negative energy direction.

[0071] Optionally, in this embodiment, if it is determined that the target material does not have conductor properties, a second spectral curve can be further extracted from the ultraviolet photoelectron spectroscopy data within a preset valence band top energy range extending from the Fermi level towards the negative energy direction.

[0072] Optionally, extracting a second spectral curve from ultraviolet photoelectron spectroscopy data within a preset valence band top energy range extending from the Fermi level towards the negative energy direction can include: extracting original spectral data from the ultraviolet photoelectron spectroscopy data where the binding energy is located between the Fermi level and the negative first energy threshold; normalizing the original spectral data to obtain a normalized valence band spectrum; calculating the first derivative sequence of the normalized valence band spectrum to obtain a derivative enhancement sequence; and merging the normalized valence band spectrum and the derivative enhancement sequence to generate the second spectral curve.

[0073] In an optional implementation of this embodiment, after determining that the target material does not possess conductor properties, the original spectral data ranging from the Fermi level to the negative first energy threshold (e.g., -10.0 eV) of the binding energy can be further extracted from the ultraviolet photoelectron spectroscopy data. This original spectral data is then normalized by scaling the photoelectron intensity sequence according to its maximum value or integral area to eliminate absolute intensity fluctuations caused by differences in testing conditions, thus obtaining a normalized valence band spectrum. Based on this normalized valence band spectrum, its first derivative at each energy point is calculated using a numerical differentiation method, forming a derivative enhancement sequence aligned with the original energy grid. Furthermore, the normalized valence band spectrum and its corresponding derivative enhancement sequence are spliced ​​or superimposed along the feature dimension to generate a second spectral curve containing both the original intensity information and edge enhancement information, which serves as input data for subsequent multidimensional feature fusion.

[0074] The advantage of this setup is that it effectively integrates the overall shape of the valence band with local gradient features, thereby improving the discrimination ability and robustness of semiconductor characteristic identification.

[0075] Step 240: Perform multi-dimensional feature fusion on the second spectral curve, and generate semiconductor characteristic identification results of the target material based on the fusion results.

[0076] Optionally, in this embodiment, multi-dimensional feature fusion is performed on the second spectral curve, and a semiconductor characteristic identification result of the target material is generated based on the fusion result. This may include: extracting at least two valence band morphology features characterizing the electronic state distribution characteristics of the valence band top region from the second spectral curve; the valence band morphology features include: valence band edge position features, intensity distribution features, peak symmetry features, or band width features; determining reference scores that match each valence band morphology feature; determining a target score based on each reference score and a preset weight that matches each valence band morphology feature; and determining the semiconductor characteristic identification result of the target material based on the target score.

[0077] Furthermore, determining the semiconductor characteristic identification result of the target material based on the target score may include: comparing the target score with a preset semiconductor determination threshold; generating a semiconductor characteristic identification result characterizing the target material as a semiconductor material when the target score is greater than or equal to the semiconductor determination threshold; and generating a semiconductor characteristic identification result characterizing the target material as a non-semiconductor material when the target score is less than the semiconductor determination threshold.

[0078] In an optional implementation of this embodiment, after obtaining the second spectral curve, multiple valence band morphological features that can reflect the physical essence of the electronic state distribution in the top region of the valence band can be further extracted from the curve. For example, it may include: valence band edge position characteristics, which determine the binding energy position (i.e., the maximum valence band value VBM) corresponding to the starting edge of the valence band by linear extrapolation or inflection point detection method, to determine whether the band gap of the material conforms to the typical range of semiconductors; intensity distribution characteristics, which characterize the rate and continuity of the increase in photoelectron intensity from low to high from the Fermi level to the deep energy level. Semiconductor materials usually show a smooth and monotonically increasing intensity distribution near VBM; peak shape symmetry characteristics, which quantify whether the profile is close to symmetry by calculating the ratio of the left and right half widths of the main valence band peak, the skewness coefficient, or the cross-correlation function. Typical inorganic semiconductors often have relatively symmetrical valence band peaks, while insulators or amorphous materials may exhibit tailing or asymmetrical shapes; and band width characteristics, which is the energy range covered from the valence band edge to the end of the main valence band peak (or the intensity drops to the baseline level), reflecting the degree of broadening of the valence band electronic states, and is closely related to the covalent nature and crystal quality of the material.

[0079] In this embodiment, for each feature, its value can be mapped to a preset scoring function, outputting a reference score between 0 and 1 to quantify the degree to which the feature supports semiconductor properties. Subsequently, based on the importance of each feature in the discrimination task, corresponding preset weights are assigned (e.g., valence band edge position weight 0.35, intensity distribution weight 0.25, symmetry weight 0.20, band width weight 0.20). A comprehensive target score is calculated through weighted averaging or other fusion strategies (such as weighted sum, product rules, or machine learning classifiers) to serve as a unified criterion for semiconductor characteristic identification.

[0080] Furthermore, after obtaining the target score, it can be compared with a preset semiconductor determination threshold to generate the final semiconductor characteristic identification result. In this embodiment, the semiconductor determination threshold is a critical value determined based on statistical analysis of a large amount of known semiconductor (such as Si, GaAs, ZnO, P3HT, etc.) and non-semiconductor materials (such as SiO2, Al2O3, PMMA, etc.) data. It is used to delineate the decision boundary between materials possessing semiconductor characteristics and those not possessing semiconductor characteristics. When the target score is greater than or equal to the threshold (for example, the threshold is set to 0.70), it indicates that multiple valence band morphological features are highly consistent with typical semiconductors, and a semiconductor characteristic identification result characterizing the target material as a semiconductor material can be generated accordingly. When the target score is less than the threshold, it indicates that the valence band structure lacks sufficient evidence to support semiconductor properties, and may exhibit non-semiconductor characteristics such as blurred valence band edges, discontinuous intensity growth, severely asymmetrical peak shape, or excessively narrow band structure. A semiconductor characteristic identification result characterizing the target material as a non-semiconductor material can be generated accordingly. Non-semiconductor materials include insulators, half-metals, or functional materials that cannot be clearly classified.

[0081] The solution in this embodiment achieves integrated decision-making based on multi-dimensional features through a single quantitative index, which not only improves classification efficiency but also ensures the objectivity and repeatability of the results, providing a reliable basis for subsequent material classification.

[0082] Step 250: If the conductor characteristic identification result indicates that the target material does not have conductor characteristics, and the semiconductor characteristic identification result indicates that the target material has semiconductor characteristics, classify the target material as a semiconductor material; if the conductor characteristic identification result indicates that the target material does not have conductor characteristics, and the semiconductor characteristic identification result indicates that the target material does not have semiconductor characteristics, classify the target material as an insulator material or an unknown material; if the conductor characteristic identification result indicates that the target material has conductor characteristics, classify the target material as a conductor material and ignore the semiconductor characteristic identification result.

[0083] Optionally, in this embodiment, after completing the two sub-processes of conductor characteristic identification and semiconductor characteristic identification, a set of preset, mutually exclusive and complete classification decision rules can be executed based on the binary discrimination results output by the two to determine the final category of the target material.

[0084] In practical implementation, when the conductor characteristic identification result is that it does not have conductor characteristics (i.e., the target material does not show significant Fermi edge characteristics near the Fermi level), and the semiconductor characteristic identification result is that it has semiconductor characteristics (i.e., the comprehensive score of the multidimensional morphological characteristics of its valence band top region reaches or exceeds the semiconductor determination threshold), it is determined that the material has a typical semiconductor electronic structure, its Fermi level is located in the band gap, the valence band top is clearly identifiable, and the electronic state density distribution conforms to the general law of semiconductor materials. Therefore, the target material is classified as a semiconductor material.

[0085] When the conductor characteristic identification result is that it does not possess conductor characteristics, and the semiconductor characteristic identification result is that it does not possess semiconductor characteristics (i.e., the valence band feature score is lower than the judgment threshold, manifested as atypical semiconductor characteristics such as blurred valence band edge, discontinuous intensity growth, diffuse peak shape, or excessively narrow band), it indicates that the material has neither the continuous state Fermi edge of a metallic conductor nor the clear valence band top structure unique to semiconductors. Its electronic state distribution is closer to that of insulators (such as wide bandgap oxides and polymers) or novel functional materials for which a standard spectral model has not yet been established. Therefore, it is classified as an insulator material or an unknown material to retain the inclusiveness for atypical systems.

[0086] When the conductor characteristic identification result indicates that the material possesses conductor characteristics (i.e., a significant Fermi edge drop or an overall downward trend satisfying the conductor criterion is detected), regardless of the semiconductor characteristic identification result, even if the valence band analysis misclassifies it as having semiconductor characteristics due to noise or algorithm limitations, the conductor conclusion is forcibly adopted based on prior physical knowledge. It is understandable that the Fermi level of a conductor is located within the conduction band, and the Fermi edge in its UPS spectrum is an irreplaceable decisive criterion, while semiconductor discrimination is only applicable to secondary analysis under non-conductor premises. In this case, the target material is directly classified as a conductor, and the semiconductor characteristic identification result is actively ignored or marked as invalid, thereby avoiding logical conflicts and classification errors.

[0087] In one example of this embodiment, an indium tin oxide (ITO) thin film prepared by the sol-gel method is used as the target material to be analyzed. The doping concentration of this sample fluctuates during preparation, and its conductivity is unknown, potentially exhibiting as a conductor, semiconductor, or weakly conductive state. In a specific implementation, the ITO thin film sample can be placed in an ultra-high vacuum ultraviolet photoelectron spectroscopy system, irradiated with a HeI ultraviolet light source, and the energy analyzer operates in fixed lens mode with an energy step size of 0.05 eV, acquiring raw spectral data with a binding energy range of −15.0 eV to +2.0 eV. Simultaneously, the Fermi edge of a gold (Au) standard sample is measured, the instrument's work function is calibrated, and the Fermi level of the ITO sample is precisely aligned to 0 eV, obtaining ultraviolet photoelectron spectral data composed of the binding energy sequence and the corresponding electron count sequence.

[0088] Reference spectral data within an energy window centered on the Fermi level and with a width of 4.0 eV was extracted from the data. The intensity sequence was then normalized by dividing by its maximum value to obtain the first spectral curve. The first energy derivative of this curve at 0 eV was calculated, and its absolute value was 0.38. The preset first kurtosis threshold was 0.30. Since 0.38 > 0.30, it was determined that the material exhibits a significant intensity abrupt change at the Fermi level, demonstrating a clear Fermi edge characteristic. Therefore, the conductor property identification result indicates that it possesses conductor properties.

[0089] In this embodiment of the invention, once the conductor characteristic identification result indicates that the material possesses conductor characteristics, regardless of the subsequent semiconductor analysis results, the target material is directly classified as a conductor material, and the semiconductor characteristic identification process is skipped. The system ultimately outputs the classification result: conductor material.

[0090] In another example of this embodiment, another ITO sample with the same process but insufficient annealing is used to repeat the above procedure. The absolute value of the first derivative of its first spectral curve at 0 eV is 0.12 (<0.30), which does not meet the first-level criterion; further calculation of the average intensity change rate in the range of −0.5 eV to +0.5 eV shows an absolute value of 0.09 (<preset second threshold 0.15), therefore the conductor characteristic identification result is that it does not possess conductor characteristics. Then, the original spectral data from 0 eV to −10.0 eV is extracted, normalized, and its first derivative is calculated to generate the second spectral curve. Four valence band morphology features were extracted: valence band edge position (−2.6 eV), intensity distribution continuity (score 0.85), peak symmetry (score 0.80), and band width (6.8 eV, score 0.88). A weighted average (0.35, 0.25, 0.20, 0.20) yielded a target score of 0.84, which is greater than the semiconductor determination threshold of 0.70. Therefore, the semiconductor characteristic identification result indicates that it possesses semiconductor properties. Ultimately, it was classified as a semiconductor material.

[0091] In another example of this embodiment, a silicon dioxide film was taken as a control sample. Its conductor identification result was that it did not have conductor characteristics, and its semiconductor multidimensional score was only 0.42 (<0.70). Therefore, it was classified as an insulating material or an unknown material.

[0092] This invention, through the construction of a two-level automated discrimination architecture prioritizing conductors and then reclassifying non-conductors as semiconductors, achieves efficient, objective, and repeatable classification of material conductivity properties in ultraviolet photoelectron spectroscopy data. On one hand, the quantified conductor identification mechanism based on Fermi edge morphology avoids the subjectivity and experience-dependent nature of manual visual interpretation. On the other hand, the introduction of multi-dimensional valence band feature fusion and scoring strategies for non-conductor materials significantly improves the ability to distinguish between semiconductors and insulators. The entire process requires no manual intervention, supports high-throughput processing, and is applicable to various inorganic, organic, and novel functional material systems. Compared to traditional manual analysis methods relying on expert experience, this solution not only significantly improves analysis efficiency and result consistency but also provides a standardized and embeddable algorithmic foundation for material database construction, high-throughput screening platforms, and intelligent characterization systems, effectively promoting the intelligent and industrial application of surface electronic structure analysis technology.

[0093] To better understand the material classification method based on ultraviolet photoelectron spectroscopy involved in the embodiments of the present invention Figure 3 This is a flowchart of another material classification method based on ultraviolet photoelectron spectroscopy provided in Embodiment 2 of the present invention. Figure 4 This is a schematic diagram of a conductor characteristic identification process according to Embodiment 2 of the present invention. Figure 5 This is a schematic diagram of a semiconductor characteristic identification process according to Embodiment 2 of the present invention, combined with... Figures 3-5 The material classification method based on ultraviolet photoelectron spectroscopy includes the following steps:

[0094] Step 1: Data Acquisition and Preprocessing.

[0095] Acquire the UPS spectral data of the material to be analyzed. This data is typically a set of data points containing a sequence of energy values ​​(x-axis) and a corresponding sequence of electron count values ​​(y-axis). Preprocess the data, including selecting a predetermined energy range (e.g., -0.5 eV to +0.5 eV) near the Fermi level as the Region of Interest (ROI) for analysis, and normalizing the data within this region to eliminate interference from differences in absolute signal intensity of the samples in subsequent analyses.

[0096] Step 2: Conductor priority judgment logic.

[0097] Since the Fermi edge characteristics of conductors are relatively well-defined, a conductor-priority judgment logic is first implemented to quickly identify typical conductor materials. This logic includes a series of rules targeting Fermi edge characteristics. The intensity drop rule detects whether the electron count value drops sharply and significantly as the energy moves from positive to negative near the Fermi level (0 eV). This rule is implemented by calculating the rate of change or difference in intensity within a specific energy range and comparing it to a preset threshold. The symmetry rule analyzes that a typical Fermi edge approximates an S-shape centered on the Fermi level, and the symmetry of the spectral curve near the Fermi level is used for judgment.

[0098] If the spectrum meets one or more conductor identification rules, the system will likely classify the material as a conductor and can terminate the analysis early or output the result with high confidence.

[0099] Step 3: Semiconductor heuristic rule set judgment.

[0100] If a material does not pass the conductor priority assessment, the algorithm will continue to execute a more complex set of heuristic rules specific to semiconductor characteristics. Semiconductors are characterized by extremely weak signals near the Fermi level. This set of rules provides a quantitative evaluation of the spectrum from multiple dimensions.

[0101] The low-intensity rule determines whether the electron count is generally low across the entire range near the Fermi level, below a preset absolute or relative intensity threshold. The intensity ratio rule calculates the ratio of the intensity in the valence band region (e.g., -0.2 eV) to the intensity at the Fermi level; for semiconductors, this ratio is typically small. The asymmetric intensity rule compares the intensity ratio at symmetrical positions on either side of the Fermi level (e.g., -0.2 eV and +0.2 eV); the asymmetry is usually more significant in semiconductors. The Fermi-level peak-free rule uses a peak-finding algorithm to detect the presence of significant spectral peaks near the Fermi level; semiconductors, due to their band gap, should not have peaks near 0 eV.

[0102] Each rule will give an independent judgment score or Boolean value. The algorithm will eventually combine the outputs of all rules to form a comprehensive judgment on whether the material is a semiconductor.

[0103] Step 4: Classification Decision and Output.

[0104] The system makes a final classification decision based on a combination of the conductor priority judgment and the semiconductor heuristic rule set judgment. If the judgment results of the two paths consistently point to a certain type, the classification result (conductor or semiconductor) is output with high confidence. If there is a conflict in the rule judgment or all features are not obvious, the system may output a default classification (e.g., guessing based on the most likely features) or mark the sample as uncertain, indicating that manual review is required.

[0105] The present invention also provides a system for implementing the above method, the system comprising a data receiving module, a data preprocessing module, an intelligent classification module configured with the above-mentioned heuristic rule set, and a result output module.

[0106] In one example of this embodiment, a Python function named classify_conductor_or_semiconductor is used to illustrate the specific implementation process of the method of the present invention.

[0107] 1. Function input and parameter configuration.

[0108] The function accepts two main input parameters: energy_list (a list of energy values) and counts_list (a list of electron counts). These data typically come directly from the output of the UPS experimental equipment, where the energy values ​​are in eV and the counts reflect the intensity of photoelectrons detected at the corresponding energy.

[0109] The core parameter configuration includes: the energy range interval for analysis, which is set to (-0.5, 0.5) eV by default, covering the key region near the Fermi level; the tolerance fermi_tolerance for the Fermi level position, which is set to 0.2 eV by default, used to determine whether the offset of the feature position is within an acceptable range; the intensity change rate thresholds delta_y_ratio_rule1 and delta_y_ratio_rule2 for conductor identification, which are set to 0.3 and 0.8 respectively; and various parameters for semiconductor identification, such as the low energy intensity threshold low_energy_threshold (default 2000) and the low energy ratio threshold low_energy_ratio_threshold (default 0.001).

[0110] 2. Data preprocessing implementation.

[0111] The program first extracts data segments near the Fermi level from the input energy_list and counts_list based on the interval parameter. Specifically, this is achieved by using Boolean indices in a NumPy array to select all data points with energy values ​​within a specified interval. The extracted counts_list data is then normalized, typically using maximum value normalization to scale the data to the [0, 1] interval. The formula is: normalized_counts = (counts - min(counts)) / (max(counts) - min(counts)).

[0112] 3. Conductor priority determination algorithm.

[0113] The specific implementation of Rule 1: Calculate the maximum decrease in the count value as the energy moves from the right boundary to the left boundary of the interval. The algorithm iterates through the truncated data, calculates the difference between adjacent data points, finds the largest negative difference (i.e., the maximum decrease), and then compares it with the dynamic range (maximum value - minimum value) of the entire interval. If this ratio is greater than delta_y_ratio_rule1 (e.g., 0.3) and less than delta_y_ratio_rule2 (e.g., 0.8), a Fermi edge is considered to be possible. These upper and lower limits are set to exclude interference from noise or anomalous signals.

[0114] Implementation of Rule 2: Find the point of fastest intensity decrease by calculating the numerical derivative. Use the `numpy.gradient` function to calculate the discrete derivative, find the energy position corresponding to the minimum derivative value (maximum rate of decrease), and determine whether this position is within the `fermi_tolerance` range of the Fermi level. If it is, then the enhancement is the confidence level for the conductor.

[0115] If any of the above rules are met, the function will directly return "Conductor".

[0116] 4. Implementation of semiconductor heuristic rule sets.

[0117] If the conductor determination fails, the semiconductor determination rules are executed sequentially. Rule 3 / 4 is implemented by checking whether the maximum count value within the interval is lower than `low_energy_threshold`, or whether the ratio of the average intensity to the maximum intensity in the valence band is lower than `low_energy_ratio_threshold`. If these conditions are met, it is considered a strong semiconductor characteristic.

[0118] Implementation of Rule 5: Find the data point on the energy axis that is closest to rule5_energy (e.g., -0.2 eV), calculate the ratio of the count value of that point to the maximum value of the entire interval, and if it is less than rule5_ratio (e.g., 0.2), then the rule is satisfied.

[0119] Implementation of rule 6: Find the corresponding count values ​​at rule6_energy1 (e.g., -0.2 eV) and rule6_energy2 (e.g., +0.2 eV), calculate the ratio between the two, and if it is less than rule6_ratio (e.g., 0.4), then the rule is satisfied.

[0120] Implementation of Rule 7: Use the `scipy.signal.find_peaks` function to find significant peaks in the truncated data, setting the minimum peak height to a multiple of the maximum data value's `peak_prominence_threshold` (e.g., 0.05). If the found peak position deviates from 0 eV by more than `rule7_tolerance`, it is also considered a semiconductor feature.

[0121] The algorithm integrates the results of these rules. A scoring system can be designed, awarding 1 point for each rule met; the more rules met, the higher the confidence level for classifying it as "Semiconductor". If the total score exceeds a certain threshold (e.g., 3 points), the function returns "Semiconductor".

[0122] 5. Default category and output.

[0123] If, after executing all rules, a clear distinction still cannot be made (e.g., neither the conductor nor semiconductor rules are met, or the semiconductor rule score is insufficient), the function will return a default classification. Based on practical experience, the default classification is usually set to "Semiconductor," because in real-world samples, atypical semiconductor spectra are more common than atypical conductor spectra.

[0124] The function ultimately returns a string: "Conductor" or "Semiconductor", and can also return a confidence score and detailed rule matching information for user reference and verification.

[0125] The method of this invention transforms complex expert interpretation logic into a series of clear, executable computer instructions and quantitative comparisons, thereby achieving automated and high-precision classification of UPS spectral material types. This method not only solves the pain points of existing technologies but also provides new ideas and technical paths for the intelligent analysis of spectral data.

[0126] Example 3

[0127] Figure 6 This is a schematic diagram of a material classification device based on ultraviolet photoelectron spectroscopy according to Embodiment 3 of the present invention. Figure 6 As shown, the device includes: an acquisition module 610, a conductor characteristic identification result determination module 620, a semiconductor characteristic identification result determination module 630, and a classification result determination module 640.

[0128] The acquisition module 610 is used to acquire the ultraviolet photoelectron spectral data of the target material to be analyzed, and extract the first spectral curve within a preset energy range centered on the Fermi level from the ultraviolet photoelectron spectral data.

[0129] The conductor characteristic identification result determination module 620 is used to identify the conductor characteristics of the target material based on the first spectral curve, and obtain the conductor characteristic identification result of the target material.

[0130] The semiconductor characteristic identification result determination module 630 is used to extract a second spectral curve from the ultraviolet photoelectron spectroscopy data in the case where it is determined that the target material does not have conductor characteristics based on the conductor characteristic identification result, and to perform multi-dimensional feature fusion on the second spectral curve, and generate the semiconductor characteristic identification result of the target material based on the fusion result.

[0131] The classification result determination module 640 is used to determine the classification result of the target material based on the semiconductor characteristic identification result.

[0132] In this embodiment, the solution involves acquiring ultraviolet photoelectron spectroscopy (UVP) data of the target material to be analyzed via an acquisition module, and extracting a first spectral curve within a preset energy range centered on the Fermi level from the UVP data. A conductor characteristic identification result determination module identifies the conductor characteristics of the target material based on the first spectral curve, obtaining a conductor characteristic identification result. If the semiconductor characteristic identification result determines that the target material does not possess conductor characteristics, a second spectral curve within a preset valence band top energy range extending from the Fermi level towards the negative energy direction is extracted from the UVP data by the semiconductor characteristic identification result determination module. Multidimensional feature fusion is then performed on the second spectral curve, and a semiconductor characteristic identification result is generated based on the fusion result. Finally, a classification result determination module determines the classification result of the target material based on the semiconductor characteristic identification result. This solution addresses the technical problems of subjective, inefficient, and difficult-to-reproduce material conductivity property judgments due to reliance on manual experience, achieving rapid and accurate classification of the conductivity properties of the target material.

[0133] In an optional implementation of this embodiment, the acquisition module 610 is specifically used to perform ultraviolet photoelectron spectroscopy on the target material to obtain ultraviolet photoelectron spectral data of the target material; wherein, the ultraviolet photoelectron spectral data includes an energy value sequence and a corresponding electron count value sequence;

[0134] Reference spectral data with binding energies located within an energy window centered on the Fermi level and with a preset width are extracted from the ultraviolet photoelectron spectral data.

[0135] The reference spectral data is normalized to obtain the first spectral curve.

[0136] In an optional implementation of this embodiment, the conductor characteristic identification result determination module 620 is specifically used to calculate the first energy derivative of the first spectral curve at the energy position corresponding to the Fermi level;

[0137] When the absolute value of the first energy derivative is greater than a preset first steepness threshold, it is determined that the target material has obvious Fermi edge characteristics and that the target material has conductor properties.

[0138] When the absolute value of the first energy derivative is not greater than the first steepness threshold, calculate the average intensity change rate of the first spectral curve within the second preset energy range centered on the Fermi level.

[0139] When the absolute value of the average intensity change rate is greater than the preset second intensity change threshold, it is determined that the target material has obvious Fermi edge characteristics and that the target material has conductor properties.

[0140] When the absolute value of the average intensity change rate is not greater than the second intensity change threshold, it is determined that the target material does not have obvious Fermi edge characteristics, and it is determined that the target material does not have conductor properties.

[0141] In an optional implementation of this embodiment, the semiconductor characteristic identification result determination module 630 is specifically used to extract the original spectral data from the ultraviolet photoelectron energy spectrum data, where the binding energy is located between the Fermi level and the negative first energy threshold.

[0142] The original spectral data are normalized to obtain the normalized valence band spectrum;

[0143] Calculate the first derivative sequence of the normalized valence band spectrum to obtain the derivative enhancement sequence;

[0144] The normalized valence band spectrum is combined with the derivative enhancement sequence to generate the second spectral curve.

[0145] In an optional implementation of this embodiment, the semiconductor characteristic identification result determination module 630 is further specifically used to extract at least two valence band morphology features characterizing the electronic state distribution characteristics of the valence band top region from the second spectral curve; the valence band morphology features include: valence band edge position features, intensity distribution features, peak shape symmetry features, or band width features;

[0146] Each of the aforementioned price band morphological features is matched with a reference score.

[0147] The target score is determined based on each of the reference scores and a preset weight that matches each of the price band morphological features;

[0148] The semiconductor property identification result of the target material is determined based on the target score.

[0149] In an optional implementation of this embodiment, the semiconductor characteristic identification result determination module 630 is further specifically used to compare the target score with a preset semiconductor determination threshold.

[0150] If the target score is greater than or equal to the semiconductor determination threshold, a semiconductor characteristic identification result is generated to characterize the target material as a semiconductor material;

[0151] If the target score is less than the semiconductor determination threshold, a semiconductor characteristic identification result is generated to characterize the target material as a non-semiconductor material.

[0152] In an optional implementation of this embodiment, the classification result determination module 640 is specifically used to classify the target material as a semiconductor material when the conductor characteristic identification result indicates that the target material does not have conductor characteristics and the semiconductor characteristic identification result indicates that the target material has semiconductor characteristics.

[0153] If the conductor characteristic identification result indicates that the target material does not have conductor characteristics, and the semiconductor characteristic identification result indicates that the target material does not have semiconductor characteristics, the target material is classified as an insulating material or an unknown material.

[0154] If the conductor characteristic identification result indicates that the target material has conductor characteristics, the target material is classified as a conductor material, and the semiconductor characteristic identification result is ignored.

[0155] The material classification device based on ultraviolet photoelectron spectroscopy provided in this embodiment of the invention can execute the material classification method based on ultraviolet photoelectron spectroscopy provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the method.

[0156] The collection, storage, use, processing, transmission, provision, and disclosure of UPS data involved in the technical solutions of this invention comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0157] Example 4

[0158] Figure 7A 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.

[0159] like Figure 7 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, 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. An input / output (I / O) interface 15 is also connected to the bus 14.

[0160] 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.

[0161] 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, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods described above, such as material classification methods based on ultraviolet photoelectron spectroscopy.

[0162] In some embodiments, the ultraviolet photoelectron spectroscopy-based material classification method 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 ultraviolet photoelectron spectroscopy-based material classification method described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to perform the ultraviolet photoelectron spectroscopy-based material classification method by any other suitable means (e.g., by means of firmware).

[0163] 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), complex 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.

[0164] 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.

[0165] 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 thereof. 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), optical fibers, compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0166] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor); 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).

[0167] 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.

[0168] 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. It addresses the shortcomings of traditional physical hosts and Virtual Private Servers (VPS) in terms of management difficulty and weak business scalability.

[0169] 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.

[0170] 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.

[0171] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements a database detection method as provided in any embodiment of this application.

[0172] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LANs or WANs—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0173] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0174] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A material classification method based on ultraviolet photoelectron spectroscopy, characterized in that, The method includes: Acquire the ultraviolet photoelectron spectroscopy data of the target material to be analyzed, and extract the first spectral curve within a preset energy range centered on the Fermi level from the ultraviolet photoelectron spectroscopy data; Based on the first spectral curve, the conductor properties of the target material are identified to obtain the conductor property identification result of the target material; If the target material is determined not to have conductor properties based on the conductor property identification result, a second spectral curve within a preset valence band top energy range extending from the Fermi level to the negative energy direction is extracted from the ultraviolet photoelectron spectroscopy data, and multi-dimensional feature fusion is performed on the second spectral curve. Based on the fusion result, a semiconductor property identification result of the target material is generated. Based on the semiconductor characteristic identification results, the classification result of the target material is determined.

2. The material classification method based on ultraviolet photoelectron spectroscopy according to claim 1, characterized in that, The process of acquiring the ultraviolet photoelectron spectroscopy data of the target material to be analyzed, and extracting the first spectral curve within a preset energy range centered on the Fermi level from the ultraviolet photoelectron spectroscopy data, includes: The target material is subjected to ultraviolet photoelectron spectroscopy (UV PES) to obtain UV PES data of the target material; wherein, the UV PES data includes an energy value sequence and a corresponding electron count value sequence; Reference spectral data with binding energies located within an energy window centered on the Fermi level and with a preset width are extracted from the ultraviolet photoelectron spectral data. The reference spectral data is normalized to obtain the first spectral curve.

3. The material classification method based on ultraviolet photoelectron spectroscopy according to claim 1, characterized in that, The step of identifying the conductor properties of the target material based on the first spectral curve to obtain the conductor property identification result of the target material includes: Calculate the first energy derivative of the first spectral curve at the energy position corresponding to the Fermi level; When the absolute value of the first energy derivative is greater than a preset first steepness threshold, it is determined that the target material has obvious Fermi edge characteristics and that the target material has conductor properties. When the absolute value of the first energy derivative is not greater than the first steepness threshold, calculate the average intensity change rate of the first spectral curve within the second preset energy range centered on the Fermi level. When the absolute value of the average intensity change rate is greater than the preset second intensity change threshold, it is determined that the target material has obvious Fermi edge characteristics and that the target material has conductor properties. When the absolute value of the average intensity change rate is not greater than the second intensity change threshold, it is determined that the target material does not have obvious Fermi edge characteristics, and it is determined that the target material does not have conductor properties.

4. The material classification method based on ultraviolet photoelectron spectroscopy according to claim 1, characterized in that, The extraction of a second spectral curve from the ultraviolet photoelectron spectroscopy data within a preset valence band top energy range extending from the Fermi level towards negative energy includes: Extract the original spectral data from the ultraviolet photoelectron spectroscopy data, where the binding energy is between the Fermi level and the negative first energy threshold. The original spectral data are normalized to obtain the normalized valence band spectrum; Calculate the first derivative sequence of the normalized valence band spectrum to obtain the derivative enhancement sequence; The normalized valence band spectrum is combined with the derivative enhancement sequence to generate the second spectral curve.

5. The material classification method based on ultraviolet photoelectron spectroscopy according to claim 4, characterized in that, The step of performing multidimensional feature fusion on the second spectral curve and generating semiconductor characteristic identification results of the target material based on the fusion result includes: Extract at least two valence band morphology features characterizing the electronic state distribution in the top region of the valence band from the second spectral curve; the valence band morphology features include: valence band edge position features, intensity distribution features, peak shape symmetry features, or band width features; Each of the aforementioned price band morphological features is matched with a reference score. The target score is determined based on each of the reference scores and a preset weight that matches each of the price band morphological features; The semiconductor property identification result of the target material is determined based on the target score.

6. The material classification method based on ultraviolet photoelectron spectroscopy according to claim 5, characterized in that, The determination of the semiconductor characteristic identification result of the target material based on the target score includes: The target score is compared with a preset semiconductor determination threshold; If the target score is greater than or equal to the semiconductor determination threshold, a semiconductor characteristic identification result is generated to characterize the target material as a semiconductor material; If the target score is less than the semiconductor determination threshold, a semiconductor characteristic identification result is generated to characterize the target material as a non-semiconductor material.

7. The material classification method based on ultraviolet photoelectron spectroscopy according to claim 1, characterized in that, The step of determining the classification result of the target material based on the semiconductor characteristic identification result includes: If the conductor characteristic identification result indicates that the target material does not have conductor characteristics, and the semiconductor characteristic identification result indicates that the target material has semiconductor characteristics, the target material is classified as a semiconductor material. If the conductor characteristic identification result indicates that the target material does not have conductor characteristics, and the semiconductor characteristic identification result indicates that the target material does not have semiconductor characteristics, the target material is classified as an insulating material or an unknown material. If the conductor characteristic identification result indicates that the target material has conductor characteristics, the target material is classified as a conductor material, and the semiconductor characteristic identification result is ignored.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the material classification method based on ultraviolet photoelectron spectroscopy 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 material classification method based on ultraviolet photoelectron spectroscopy as described in any one of claims 1-7.

10. A computer program product comprising a computer program that, when executed by a processor, implements the material classification method based on ultraviolet photoelectron spectroscopy according to any one of claims 1-7.

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