Photoelectron spectroscopy method and apparatus, electronic device, storage medium

Abstract

The embodiment of the application provides a photoelectron energy spectrum analysis method and device, electronic equipment and storage medium, and belongs to the technical field of surface material detection. The method comprises the following steps: performing photoelectron energy spectrum detection on a target sample to obtain an initial photoelectron energy spectrum; performing energy spectrum data point selection according to the initial photoelectron energy spectrum to obtain a photoelectron energy spectrum data point sequence; updating a preset initial photoelectron energy spectrum model according to an expectation maximization algorithm and the photoelectron energy spectrum data point sequence to obtain a first photoelectron energy spectrum fitting model composed of all photoelectron peaks; obtaining a target quantum effect constraint of the target sample; updating all first photoelectron peaks according to a quantum effect constraint under a central field approximation to obtain a second photoelectron energy spectrum fitting model; and performing satellite peak correction fitting according to a satellite peak energy ratio of the photoelectron peaks in the second photoelectron energy spectrum to obtain a complete target photoelectron energy spectrum. The embodiment of the application can improve the accuracy of photoelectron energy spectrum analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of surface material detection, and particularly relates to a photoelectron energy spectrum analysis method and device, an electronic device and a storage medium. BACKGROUND

[0002] X-ray photoelectron energy spectrum analysis is a technology of detecting a sample surface by X-rays to make photoelectrons emitted from the sample surface, so as to measure a photoelectron energy spectrum. The photoelectron energy spectrum can represent physical and chemical properties of the sample.

[0003] At present, specific parameters of photoelectron peaks of spectral lines in the measured photoelectron energy spectrum, such as peak shape or peak area of the photoelectron peak, are often not accurate enough. For example, since photoelectrons emitted from the sample surface have different scattering intensities, the measured photoelectron energy spectrum often has errors, and the peak area of the photoelectron energy spectrum does not conform to quantum effect constraints of quantum mechanics.

[0004] Therefore, how to improve the authenticity and accuracy of the photoelectron energy spectrum has become a technical problem to be solved. SUMMARY

[0005] The main purpose of the embodiments of the present application is to provide a photoelectron energy spectrum analysis method and device, an electronic device and a storage medium, which aims to improve the authenticity and accuracy of the photoelectron energy spectrum and obtain a photoelectron energy spectrum conforming to quantum effect constraints.

[0006] To achieve the above purpose, a first aspect of the embodiments of the present application provides a photoelectron energy spectrum analysis method, which comprises:

[0007] Performing photoelectron energy spectrum detection on a target sample to obtain an initial photoelectron energy spectrum;

[0008] Performing energy spectrum data point selection according to the initial photoelectron energy spectrum to obtain a photoelectron energy spectrum data point sequence;

[0009] Performing first model parameter updating on a preset initial photoelectron energy spectrum fitting model according to a preset expectation maximization algorithm and the photoelectron energy spectrum data point sequence to obtain a first photoelectron energy spectrum fitting model;

[0010] Obtaining a target quantum effect constraint of the target sample;

[0011] Performing second model parameter updating on the first photoelectron energy spectrum fitting model according to a preset central field approximation relationship and the target quantum effect constraint to obtain a second photoelectron energy spectrum fitting model;

[0012] Performing energy spectrum fitting on the photoelectron energy spectrum data point sequence according to the second photoelectron energy spectrum fitting model to obtain a target photoelectron energy spectrum.

[0013] In some embodiments, the target quantum effect constraint includes peak pairing constraint conditions and peak attribute ratio thresholds;

[0014] The step of updating the second photoelectron spectrum fitting model by updating the second model parameters of the first photoelectron spectrum fitting model according to the preset centralized field approximation relationship and the target quantum effect constraint includes:

[0015] The first photoelectron spectrum fitting model is used to fit the photoelectron peaks of the photoelectron spectrum data point sequence to obtain at least two target photoelectron peaks.

[0016] By applying the peak pairing constraint condition, peak pairing test is performed on any two target photoelectron peaks to obtain the first target photoelectron peak and the second target photoelectron peak.

[0017] The peak attribute value ratio is determined based on the attribute values ​​of the first target photoelectron peak and the second target photoelectron peak.

[0018] When the peak attribute value ratio is not equal to the peak attribute ratio threshold, the model parameters of the first photoelectron spectrum fitting model are updated according to the centralized field approximation formula to obtain the second photoelectron spectrum fitting model.

[0019] In some embodiments, the model parameters of the first photoelectron spectroscopy fitting model include initial peak intensity parameters and initial peak position parameters; the peak attribute value ratio includes peak intensity ratio, the peak attribute ratio threshold includes peak intensity ratio threshold, and the centralized field approximation relationship includes a centralized field approximation splitting peak relationship.

[0020] When the peak attribute value ratio is not equal to the peak attribute ratio threshold, the model parameters of the first photoelectron spectrum fitting model are updated according to the centralized field approximation formula to obtain the second photoelectron spectrum fitting model, including:

[0021] When the peak intensity ratio is not equal to the peak intensity ratio threshold, the first peak intensity constraint formula and the peak position constraint formula are determined according to the centralized field approximate splitting peak relationship formula.

[0022] The first peak intensity constraint factor is determined based on the first peak intensity constraint formula, the total angular quantum number of the first target photoelectron peak, and the total angular quantum number of the second target photoelectron peak.

[0023] The initial peak intensity parameter is updated according to the first peak intensity constraint factor to obtain the first target peak intensity parameter;

[0024] Based on the peak position constraint formula, the principal quantum number, angular quantum number and total angular quantum number of the first target photoelectron peak, and the principal quantum number, angular quantum number and total angular quantum number of the second target photoelectron peak, the peak position constraint factor is determined;

[0025] The initial peak position parameters are updated according to the peak position constraint factor to obtain the target peak position parameters;

[0026] The second photoelectron spectroscopy fitting model is determined based on the first target peak intensity parameter and the target peak position parameter.

[0027] In some embodiments, the model parameters of the first photoelectron spectroscopy fitting model include an initial peak intensity parameter; the peak attribute value ratio also includes a peak area ratio; the peak pairing constraint condition also includes a satellite companion peak constraint condition; the peak attribute ratio threshold also includes a peak area ratio threshold; and the centralized field approximation relationship also includes a centralized field approximation satellite companion peak relationship.

[0028] The step of updating the model parameters of the first photoelectron spectrum fitting model according to the centralized field approximation formula to obtain the second photoelectron spectrum fitting model when the peak attribute value ratio is not equal to the peak attribute ratio threshold further includes:

[0029] When the peak area ratio is not equal to the peak area ratio threshold, the second peak strength constraint relationship is determined according to the centralized field approximate satellite companion peak relationship.

[0030] The second peak intensity constraint factor is determined based on the second peak intensity constraint formula, the peak area of ​​the first target photoelectron peak, and the peak area of ​​the second target photoelectron peak.

[0031] The initial peak intensity parameter is updated according to the second peak intensity constraint factor to obtain the second target peak intensity parameter;

[0032] The second photoelectron spectrum fitting model is determined based on the second target peak intensity parameter.

[0033] In some embodiments, the initial photoelectron spectrum has an initial photoelectron peak; the step of selecting spectral data points based on the initial photoelectron spectrum to obtain a photoelectron spectrum data point sequence includes:

[0034] The energy spectrum data points are divided according to the spectral lines of the initial photoelectron energy spectrum to obtain at least two initial background data points and at least two initial non-background data points; wherein each of the initial background data points and each of the initial non-background data points has an energy value and an intensity value.

[0035] The background energy spectrum curves are obtained by fitting at least two initial background data points with a preset Gaussian random process function.

[0036] Based on the energy value and intensity value of each initial background data point and the energy value of each initial non-background data point, the intensity value of each initial non-background data point is updated by a first intensity value to obtain the corresponding target non-background data point.

[0037] The intensity value of each initial background data point is updated with a second intensity value based on the background energy spectrum curve to obtain the corresponding target background data point; wherein the intensity value of the target background data point is less than the intensity value of the initial background data point;

[0038] The photoelectron spectroscopy data point sequence is obtained by integrating the target non-background data points and the target background data points.

[0039] In some embodiments, updating the intensity value of each initial non-background data point based on its energy and intensity values, and the energy value of each initial non-background data point, to obtain the corresponding target non-background data point, includes:

[0040] The background intensity is calculated based on the energy and intensity values ​​of each initial background data point and the energy value of each initial non-background data point to obtain the background intensity vector;

[0041] The intensity value of each initial non-background data point is updated by performing a first intensity value update based on the background intensity vector to obtain the target non-background data point.

[0042] In some embodiments, the step of dividing the energy spectrum data points according to the spectral lines of the initial photoelectron energy spectrum to obtain at least two initial background data points and at least two initial non-background data points includes:

[0043] The initial photoelectron energy spectrum is used to select energy spectrum data points to obtain multiple initial energy spectrum data points;

[0044] Each initial energy spectrum data point is divided according to the trough of the initial photoelectron energy spectrum to obtain at least two initial background data points and at least two initial non-background data points; wherein, the initial background data points are located at the trough of the spectral line.

[0045] To achieve the above objectives, a second aspect of this application provides a photoelectron spectroscopy analysis device, the device comprising:

[0046] The photoelectron spectroscopy acquisition module is used to perform photoelectron spectroscopy detection on the target sample and acquire the initial photoelectron spectrum.

[0047] The data point selection module is used to select energy spectrum data points based on the initial photoelectron energy spectrum to obtain a photoelectron energy spectrum data point sequence.

[0048] The first model parameter update module is used to update the first model parameters of the preset initial photoelectron spectrum fitting model according to the preset expectation maximization algorithm and the photoelectron spectrum data point sequence, so as to obtain the first photoelectron spectrum fitting model.

[0049] A quantum effect constraint acquisition module is used to acquire the target quantum effect constraint of the target sample.

[0050] The second model parameter update module is used to update the second model parameters of the first photoelectron spectrum fitting model according to the preset centralized field approximation relationship and the target quantum effect constraint, so as to obtain the second photoelectron spectrum fitting model.

[0051] The energy spectrum fitting module is used to perform energy spectrum fitting on the photoelectron energy spectrum data point sequence according to the second photoelectron energy spectrum fitting model to obtain the target photoelectron energy spectrum.

[0052] To achieve the above objectives, a third aspect of the present application provides an electronic device, the electronic device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method described in the first aspect.

[0053] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0054] The photoelectron spectroscopy analysis method, apparatus, electronic device, and storage medium proposed in this application, after obtaining the photoelectron spectral data point sequence corresponding to the initial photoelectron spectrum and updating the initial photoelectron spectral fitting model according to the expectation-maximization algorithm and the photoelectron spectral data point sequence to obtain the first photoelectron spectral fitting model, also obtain the target quantum effect constraint of the target sample; according to the centralized field approximation relation and the target quantum effect constraint, the first photoelectron spectral fitting model is updated with a second model parameter to obtain the second photoelectron spectral fitting model, thereby ensuring that the target photoelectron spectrum fitted by the second photoelectron spectral fitting model conforms to the target quantum effect constraint of the target sample, thus improving the accuracy of photoelectron spectral analysis. Attached Figure Description

[0055] Figure 1 This is a flowchart of the photoelectron spectroscopy analysis method provided in the embodiments of this application;

[0056] Figure 2 yes Figure 1 The flowchart for step 102 in the document;

[0057] Figure 3 yes Figure 2 The flowchart for step 201 in the document;

[0058] Figure 4 This is a detailed schematic diagram of the initial photoelectron spectrum provided in the embodiments of this application;

[0059] Figure 5 This is a schematic diagram of the initial photoelectron spectrum provided in another embodiment of this application;

[0060] Figure 6 yes Figure 2 The flowchart for step 203 in the document;

[0061] Figure 7 yes Figure 1 The flowchart for step 105 in the document;

[0062] Figure 8 This is an embodiment provided by this application. Figure 7 The flowchart for step 504 in the document;

[0063] Figure 9 This is provided by another embodiment of the present application. Figure 7 The flowchart for step 504 in the document;

[0064] Figure 10 This is a schematic diagram of the structure of the photoelectron spectroscopy analysis device provided in the embodiments of this application;

[0065] Figure 11 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0066] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0067] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0069] First, let's analyze some of the terms used in this application:

[0070] X-ray photoelectron spectroscopy (XPS), also known as electron spectroscopy for chemical analysis (ESCA), is a surface analysis technique. In X-ray photoelectron spectroscopy, X-rays are irradiated onto the sample, exciting electrons in the atoms or molecules of the sample to emit photoelectrons from the sample surface. Electrons excited by photons are called photoelectrons, and these photoelectrons primarily originate from the inner shells of atoms on the sample surface. The energy of the photoelectrons can be measured, and a photoelectron spectrum is obtained by plotting the kinetic energy of the photoelectrons on the x-axis and the relative intensity of the photoelectrons on the y-axis. This provides physical and chemical information about the sample, such as its chemical composition.

[0071] The central field approximation method is a method that treats each electron in an atom as moving in a potential field that is spherically symmetric about the atomic nucleus, and is used to calculate the energy of the electron.

[0072] Background signals refer to signals present in XPS measurements other than the photoelectron signals from the sample. These signals primarily originate from the energy lost by photoelectrons during their escape, such as energy lost due to inelastic collisions. This energy loss affects the measured value of the photoelectron binding energy, thus forming the background signal.

[0073] Splitting peaks: These are pairs of photoelectron peaks caused by the spin-orbit coupling of electrons. In photoelectron spectroscopy (such as XPS), due to the spin-orbit coupling effect of electrons, energy levels with the same principal quantum number but different spins and orbital angular momentum split, resulting in two or more peaks in the spectrum. These peaks are called splitting peaks.

[0074] Satellite peaks are lower-energy spectral lines that appear in addition to the main spectral line (also known as the primary peak) during spectral analysis due to the excitation of atoms or molecules. These satellite peaks are caused by various factors affecting atoms or molecules when they transition from higher to lower energy levels, such as multiple splitting (energy level splitting), electron tremor (electrons being deviated from their original orbits by external forces), and tremor separation (electrons being completely stripped from atoms or molecules), resulting in lower-energy spectral lines adjacent to the primary peak.

[0075] Artificial Intelligence (AI) is the study and development of theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence. Research in this field includes robotics, speech recognition, image recognition, and natural language processing.

[0076] Machine learning is a branch of artificial intelligence that enables computer systems to automatically learn and improve their performance using data and algorithms. Specifically, machine learning models can be trained using sample data to make predictions or decisions.

[0077] The photoelectron spectroscopy analysis method, apparatus, electronic device, and storage medium provided in this application are specifically described through the following embodiments. First, the photoelectron spectroscopy analysis method in this application is described.

[0078] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0079] The photoelectron spectroscopy analysis method provided in this application can be applied to a terminal, a server, or software running on a terminal or a server.

[0080] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0081] Figure 1 This is an optional flowchart of the photoelectron spectroscopy analysis method provided in the embodiments of this application. Figure 1 The method may include, but is not limited to, steps 101 to 106.

[0082] Step 101: Perform photoelectron spectroscopy on the target sample to obtain the initial photoelectron spectrum;

[0083] Step 102: Select energy spectrum data points based on the initial photoelectron energy spectrum to obtain a photoelectron energy spectrum data point sequence;

[0084] Step 103: Update the first model parameters of the preset initial photoelectron spectrum fitting model according to the preset expectation maximization algorithm and the photoelectron spectrum data point sequence to obtain the first photoelectron spectrum fitting model.

[0085] Step 104: Obtain the target quantum effect constraint of the target sample;

[0086] Step 105: Based on the preset centralized field approximation relation and the target quantum effect constraint, update the second model parameters of the first photoelectron spectrum fitting model to obtain the second photoelectron spectrum fitting model.

[0087] Step 106: Perform energy spectrum fitting on the photoelectron energy spectrum data point sequence according to the second photoelectron energy spectrum fitting model to obtain the target photoelectron energy spectrum.

[0088] The beneficial effects of the embodiments of this application include, but are not limited to: after obtaining the photoelectron spectrum data point sequence corresponding to the initial photoelectron spectrum, and updating the initial photoelectron spectrum fitting model according to the expectation-maximization algorithm and the photoelectron spectrum data point sequence to obtain the first photoelectron spectrum fitting model, the target quantum effect constraint of the target sample is also obtained; according to the centralized field approximation relation and the target quantum effect constraint, the second model parameter of the first photoelectron spectrum fitting model is updated to obtain the second photoelectron spectrum fitting model, thereby ensuring that the target photoelectron spectrum fitted by the second photoelectron spectrum fitting model conforms to the target quantum effect constraint, and improving the reliability and authenticity of the photoelectron spectrum analysis method.

[0089] In some embodiments, step 101 may include: (1) Pre-treating the target sample. Specifically, appropriate pre-treatment may be performed according to experimental needs, such as polishing or cleaning the target sample. (2) Setting the target sample in a vacuum system. The vacuum system has a sample tank for fixing the target sample; X-ray source, photoelectron spectrometer, and other equipment may also be installed in the vacuum system. (3) Setting the X-ray source. Specifically, the X-ray source may be adjusted according to preset position information to ensure that the X-ray beam emitted by the X-ray source irradiates the sampling point on the surface of the target sample; or, the angle between the X-ray source and the target sample may be adjusted to ensure that the surface of the target sample is perpendicular to the X-ray emitted by the X-ray source; or the X-ray source may be set according to preset X-ray energy value to excite electrons on the surface of the target sample to generate photoelectrons. Monochromatic X-ray source may be used. (4) Initializing the instrument parameters of the photoelectron spectrometer. Specifically, the photoelectron spectrometer is used to collect data to generate a photoelectron spectrum. Before this, the instrument parameters such as the energy resolution and the number of sampling points of the photoelectron spectrometer need to be initialized. (5) The initial photoelectron spectrum is obtained by measuring the target sample using an X-ray source and a photoelectron spectrometer. Multiple measurements can be performed at different sampling points on the target sample surface to obtain the initial measured spectrum. The average value of these multiple initial measured spectra is then calculated to obtain the initial photoelectron spectrum, thereby improving the accuracy of the spectrum measurement. Other methods can also be used to obtain the initial photoelectron spectrum, and are not limited to these.

[0090] It should be noted that the target sample is a condensed matter, including both solid and liquid substances. In some embodiments, the target sample may be a metal (iron, copper, etc.) or a semiconductor, such as a sample of divalent copper. In another embodiment, the target photoelectron spectrum corresponding to the target sample may be the 2p orbital photoelectron spectrum of divalent copper.

[0091] It should be noted that photoelectron spectra (such as initial and target photoelectron spectra) contain spectral lines, and these lines have peaks and troughs. A photoelectron peak refers to the peak of a spectral line. Photoelectron peaks have peak attributes, such as peak position, peak width, peak shape, and peak area (also called integrated intensity). The horizontal axis of a photoelectron spectrum represents energy values, and the vertical axis represents intensity values. The energy value characterizes the electron binding energy of the photoelectrons, and the intensity value characterizes the relative intensity of the photoelectrons; there is a one-to-one correspondence between intensity and energy values.

[0092] In step 102 of some embodiments, the photoelectron spectral data point sequence includes multiple spectral data points, which are points on the spectral lines of the initial photoelectron spectrum. Specifically, the photoelectron spectral data point sequence is the data point sequence obtained after subtracting the background signal. In another embodiment, after obtaining the photoelectron spectral background data point sequence, the photoelectron spectral background data point sequence can be fitted according to a Gaussian random process to obtain the background curve of the entire spectrum (initial photoelectron spectrum).

[0093] In step 103 of some embodiments, the initial photoelectron spectrum fitting model is a machine learning model. In some embodiments, it should be noted that in related technologies, the photoelectron spectrum can be solved using the least squares method; however, this method has low spectral analysis efficiency, is prone to getting trapped in local optima, and results in low fidelity of the photoelectron spectrum. In the embodiments of this application, the initial photoelectron spectrum fitting model is updated using the expectation-maximization algorithm to obtain the target photoelectron spectrum, thereby improving the spectral analysis efficiency and the fidelity of the target photoelectron spectrum.

[0094] In step 104 of some embodiments, the target quantum effect constraint is used to reflect whether the peak parameter ratio between the two photoelectron peaks conforms to quantum mechanics theory. In one embodiment, for example, if the target sample is a titanium sample, the initial photoelectron spectrum is the 2p orbital photoelectron spectrum of titanium (Ti), and the two photoelectron peaks in the initial photoelectron spectrum are a pair of split peaks, then based on quantum mechanics theory, the peak intensity ratio of the two photoelectron peaks should be 3:2. In another embodiment, if the two photoelectron peaks in the initial photoelectron spectrum of the titanium sample are a pair of satellite companion peaks, then based on quantum mechanics theory, the peak area ratio of the two photoelectron peaks should be 2:1.

[0095] In step 105 of some embodiments, it should be noted that in related technologies, the spectral lines of the fitted photoelectron spectrum can be matched with the experimentally observed spectral lines, and the peak parameters (such as peak position and peak intensity) of the photoelectron peaks can be adjusted to update the photoelectron spectrum. However, this method is easily affected by data noise and scattering intensity, resulting in low consistency between the photoelectron spectrum and quantum mechanical theory, or even violation of quantum mechanical theory. For example, the photoelectron peaks of the photoelectron spectrum may not meet the constraint conditions of splitting peaks. Considering the above technical problems, the embodiments of this application update the first photoelectron spectrum fitting model through the centralized field approximation relation and the target quantum effect constraint to obtain the second photoelectron spectrum fitting model. This ensures that the target photoelectron spectrum output by the second photoelectron spectrum fitting model satisfies the target quantum effect constraint, thereby obtaining the target photoelectron spectrum that satisfies the target quantum effect constraint and improving the authenticity of the spectral analysis results.

[0096] In step 106 of some embodiments, the photoelectron spectrum data point sequence can be fitted with peaks using a second photoelectron spectrum fitting model to obtain multiple photoelectron peaks; then, the multiple photoelectron peaks can be fitted with energy spectra using the second photoelectron spectrum fitting model to obtain the target photoelectron spectrum.

[0097] It should be noted that the embodiments of this application automatically perform energy spectrum fitting through the second photoelectron spectroscopy fitting model, enabling high-throughput photoelectron spectroscopy analysis. This can be applied to scenarios involving large-scale photoelectron spectroscopy analysis in high-throughput laboratories / robotic laboratories. High-throughput photoelectron spectroscopy analysis refers to the detection and photoelectron spectroscopy analysis of a large number of samples.

[0098] Please see Figure 2 In some embodiments, the initial photoelectron spectrum has an initial photoelectron peak. Step 102 may include, but is not limited to, steps 201 to 205:

[0099] Step 201: Divide the energy spectrum data points according to the spectral lines of the initial photoelectron energy spectrum to obtain at least two initial background data points and at least two initial non-background data points; wherein each initial background data point and each initial non-background data point has an energy value and an intensity value.

[0100] Step 202: Fit the background energy spectrum curves of at least two initial background data points according to the preset Gaussian random process function to obtain the background energy spectrum curves.

[0101] Step 203: Based on the energy value and intensity value of each initial background data point and the energy value of each initial non-background data point, update the intensity value of each initial non-background data point with the first intensity value to obtain the corresponding target non-background data point.

[0102] Step 204: Update the intensity value of each initial background data point according to the background energy spectrum curve to obtain the corresponding target background data point; wherein, the intensity value of the target background data point is less than the intensity value of the initial background data point.

[0103] Step 205: Integrate the data points based on the non-background data points of the target and the background data points of the target to obtain the photoelectron spectroscopy data point sequence.

[0104] It should be noted that during the photoelectron spectroscopy measurement of the target sample, a background signal will be present. This background signal is an interference signal that can affect the accuracy of photoelectron spectral analysis. For example, the background signal can affect the intensity values ​​of spectral lines, causing a deviation between the intensity values ​​and the true photoelectron intensity values. To eliminate the influence of the background signal of the target sample on the photoelectron spectrum, the photoelectron spectrum needs to be calibrated, i.e., background stripping is required.

[0105] The advantage of this embodiment lies in that it divides the initial background data points and initial non-background data points based on the initial photoelectron spectrum, and updates the intensity values ​​of the initial background data points and initial non-background data points respectively, thereby eliminating the influence of the background signal on the intensity values ​​of the data points. This achieves background stripping of the initial background data points and initial non-background data points, resulting in more accurate target background data points and target non-background data points. Then, the photoelectron spectrum data point sequence is obtained, which can be used for subsequent spectral fitting to obtain the target photoelectron spectrum, thereby improving the accuracy of target photoelectron spectrum resolution.

[0106] In step 201 of some embodiments, the difference between the initial background data point and the initial non-background data point is that the initial background data point is located at the trough of the spectral line, while the initial non-background data point is located at the peak of the spectral line (i.e., the photoelectron peak).

[0107] In step 202 of some embodiments, the initial background data points can be fitted using a Gaussian Random Process function to obtain the background energy spectrum curve. The background energy spectrum curve overlaps with or closely approximates the portion of the initial photoelectron energy spectrum where the spectral line intensities are relatively lowest. A suitable background energy spectrum curve fitting method can also be selected according to requirements; this application embodiment does not limit this approach.

[0108] In step 203 of some embodiments, the intensity value of the target non-background data point is less than or equal to the intensity value of the initial non-background data point. Step 203 may also involve calculating the difference between the intensity value of the initial background data point and the intensity value of the corresponding data point on the background energy spectrum curve to obtain the corresponding target non-background data point. Here, the corresponding data point on the background energy spectrum curve refers to a data point with the same energy value as the initial background data point.

[0109] In step 204 of some embodiments, the intensity value of each initial background data point can be subtracted from the intensity value of the corresponding data point on the background energy spectrum curve. For example, the initial background data point is a data point located on the background energy spectrum curve, so the intensity value of each target background data point can be 0.

[0110] In step 205 of some embodiments, the photoelectron spectral data point sequence includes all target non-background data points and target background data points. In some embodiments, the spectral data points (including target background data points and target non-background data points) in the photoelectron spectral data point sequence are sorted in ascending order of energy value.

[0111] Please see Figure 3 In some embodiments, step 201 may include, but is not limited to, steps 301 to 302:

[0112] Step 301: Select energy spectrum data points from the initial photoelectron energy spectrum to obtain multiple initial energy spectrum data points;

[0113] Step 302: Divide each initial energy spectrum data point according to the valley of the initial photoelectron energy spectrum to obtain at least two initial background data points and at least two initial non-background data points; wherein, the initial background data points are located at the valley of the spectral line.

[0114] The advantage of this embodiment is that after obtaining the initial energy spectrum data points, each initial energy spectrum data point is divided according to the trough of the spectral line of the initial photoelectron energy spectrum, thereby obtaining initial background data points and initial non-background data points. This facilitates the subsequent removal of background signals from the initial background data points and initial non-background data points, resulting in more accurate target background data points and target non-background data points, thereby improving the accuracy of target photoelectron energy spectrum analysis.

[0115] In step 301 of some embodiments, initial energy spectrum data points can be extracted based on a preset number of data points, or based on special data points such as the apex of the photoelectron peak. Other suitable data point extraction methods can also be selected as needed, and this application embodiment does not limit this.

[0116] like Figure 4 and Figure 5 As shown, in some embodiments, Figure 4 This is a schematic diagram of the initial photoelectron spectrum, specifically the photoelectron spectrum of the 2p orbital of divalent copper. The horizontal axis X represents the energy value, specifically the electron binding energy of the photoelectrons, measured in eV (electron volts). The vertical axis Y represents the intensity value, specifically the relative intensity of the photoelectrons; its unit is not limited and can be any unit. Figure 4 The original intensity spectral lines in the initial photoelectron spectrum refer to the original spectral lines of the initial photoelectron spectrum. The Fourier transform intensity spectral lines are obtained by smoothing the original intensity spectral lines using a Fourier transform algorithm, thereby improving the anti-interference capability of the photoelectron spectroscopy analysis method. The spectral lines of the initial photoelectron spectrum can be either the original intensity spectral lines or the Fourier transform intensity spectral lines; there is no limitation on which. Figure 5 and Figure 4 Correspondingly, in Figure 5 In this context, the background energy spectrum curve is obtained by Gaussian fitting based on the spectral lines of the initial photoelectron energy spectrum (taking the original intensity spectral lines as an example in this embodiment). The background data points are the points on the overlapping portion of the background energy spectrum curve and the spectral lines of the initial photoelectron energy spectrum.

[0117] In step 302 of some embodiments, it should be noted that the initial background data point is not located at the photoelectron peak. The initial background data point can be a data point located at the trough of the spectral line, or a data point with a lower intensity value.

[0118] In some embodiments, the initial photoelectron energy spectrum has multiple spectral data points on its spectral lines. Specifically, energy spectrum data points Has energy value and intensity value Where j represents the j-th energy spectrum data point, On the initial photoelectron spectrum, m independent background intervals are defined, and all spectral data points falling within these intervals are identified as background data points. Thus, all spectral data points on the initial photoelectron spectrum are divided into two parts: One back-bottom data point ,as well as Non-background data points Where k represents the k-th background data point, l represents the l-th non-background data point. ,satisfy .

[0119] Please see Figure 6 In some embodiments, step 203 may include, but is not limited to, steps 401 to 402:

[0120] Step 401: Calculate the background intensity based on the energy and intensity values ​​of each initial background data point and the energy value of each initial non-background data point to obtain the background intensity vector.

[0121] Step 402: Update the intensity value of each initial non-background data point according to the background intensity vector to obtain the target non-background data point.

[0122] The advantage of this embodiment is that after calculating the background intensity vector based on the energy and intensity values ​​of each initial background data point and the energy value of each initial non-background data point, the intensity value of each initial non-background data point is updated using the background intensity vector to obtain the target non-background data point, thereby improving the accuracy of the target non-background data point.

[0123] In step 401 of some embodiments, according to Initial backbed data points Determine the initial backbed energy value vector based on the energy value. , ;according to The initial background strength value vector is determined by the intensity values ​​of the initial background data points. , ;according to Initial non-background data points Determine the initial non-background energy value vector based on the energy value. , ;according to The initial non-background intensity value vector is determined by the intensity values ​​of the initial non-background data points. , This means dividing the data into two complementary sets.

[0124] Initial non-background energy value vector Corresponding backside strength vector As shown in formula (1):

[0125] , formula (1);

[0126] in, This is a hyperparameter, with a default setting of 0.01; Represents the identity matrix. Let represent the Gaussian kernel function. The definition of the Gaussian kernel function is shown in formula (2):

[0127] , formula (2);

[0128] in, The two input variables represent the Gaussian kernel function. express The square of the L2 norm, As an optimization variable, it can be set to a fixed value of 0.01.

[0129] In step 402 of some embodiments, the target non-background data point may be obtained by subtracting the corresponding vector element in the background intensity vector from the intensity value of each initial non-background data point.

[0130] In some embodiments, an initial non-background intensity value vector can be determined based on the intensity value of each initial non-background data point. Then, based on the initial non-background strength value vector and background strength vector By performing difference calculations, the backwater strength vector of the target interval is obtained. Furthermore, (see step 203) the intensity value of each initial background data point is updated with a second intensity value based on preset standard intensity data to obtain the corresponding target background data point. This can be achieved by converting the initial background intensity value vector... All elements are replaced with standard intensity data to obtain the target interval background intensity value vector. This results in two new sets of data points: the target background data point set. and target non-background data point set Then (see step 204), data points are integrated based on the target non-background data points and the target background data points to obtain a photoelectron spectroscopy data point sequence. This can be achieved by concatenating the target background data point set and the target non-background data point set into a photoelectron spectroscopy data point sequence containing n data pairs. .

[0131] Please see Figure 7 In some embodiments, the target quantum effect constraint includes peak pairing constraint conditions and peak attribute ratio thresholds. Step 105 may include, but is not limited to, steps 501 to 504:

[0132] Step 501: Fit the photoelectron peaks of the photoelectron spectrum data point sequence using the first photoelectron spectrum fitting model to obtain at least two target photoelectron peaks.

[0133] Step 502: Using the peak pairing constraint condition, perform peak pairing test on any two target photoelectron peaks to obtain the first target photoelectron peak and the second target photoelectron peak;

[0134] Step 503: Determine the peak attribute value ratio based on the attribute values ​​of the first target photoelectron peak and the second target photoelectron peak;

[0135] Step 504: When the peak attribute value ratio is not equal to the peak attribute ratio threshold, update the model parameters of the first photoelectron spectrum fitting model according to the centralized field approximation formula to obtain the second photoelectron spectrum fitting model.

[0136] The advantage of this embodiment is that after obtaining at least two target photoelectron peaks, the first target photoelectron peak and the second target photoelectron peak are obtained through peak pairing constraint conditions, thereby determining the peak attribute value ratio. Then, when the peak attribute value ratio is not equal to the peak attribute ratio threshold, the model parameters of the first photoelectron spectrum fitting model are updated according to the centralized field approximation relationship to ensure that the target photoelectron spectrum obtained by the second photoelectron spectrum fitting model conforms to the target quantum effect constraint, thereby improving the reliability and authenticity of the photoelectron spectrum analysis method.

[0137] It should be noted that the peak pairing constraint condition is used to characterize the peak attribute ratio relationship between two photoelectron peaks, and the peak attribute ratio threshold is used to characterize the peak attribute ratio value that conforms to quantum mechanical theory.

[0138] In step 501 of some embodiments, the photoelectron spectrum fitting model (e.g., an initial photoelectron spectrum fitting model, a first photoelectron spectrum fitting model, and a second photoelectron spectrum fitting model) has a photoelectron peak fitting function and a spectrum fitting function. The photoelectron peaks of the photoelectron spectrum data point sequence can be fitted using the photoelectron peak fitting function of the first photoelectron spectrum fitting model to obtain at least two target photoelectron peaks. Then, the spectrum of the target photoelectron peaks can be fitted using the spectrum fitting function to obtain the corresponding photoelectron spectrum.

[0139] In some embodiments, the photoelectron peak fitting function is shown in Equation (3), and the energy spectrum fitting function is shown in Equation (4):

[0140] , formula (3);

[0141] , formula (4);

[0142] in Indicates the first One photoelectron peak; Representation function The independent variable x can be set to the energy value of any energy spectrum data point, such as the energy value of the initial background data point, as needed. Or the energy value of the initial non-background data points ; Indicates photoelectron peak The parameters are the model parameters of the photoelectron spectroscopy fitting model, where i represents the i-th photoelectron peak. ; This represents the total number of photoelectron peaks; corresponding to the i-th photoelectron peak, This represents the first peak intensity parameter. This represents the second peak intensity parameter. Indicates peak position parameter. Indicates the first peak shape parameter. Indicates the second peak shape parameter; This represents the photoelectron spectrum (e.g., initial photoelectron spectrum and target photoelectron spectrum) output by the photoelectron spectrum fitting model.

[0143] In some embodiments, updating the first model parameters according to a preset expectation-maximization algorithm refers to defining an iterative update strategy for the model parameters based on the expectation-maximization algorithm, i.e., given the ( ) The model parameters for step () are obtained through an iterative method. (Step-by-step model parameters. Specifically, these include:) Starting from 0, when At that time, the model parameters were initial values. , ,when When the model parameters are updated, they are updated according to formulas (5) to (9).

[0144] , formula (5);

[0145] , formula (6);

[0146] , formula (7);

[0147] , formula (8);

[0148] , formula (9);

[0149] in, , , The definition is shown in formulas (10) to (12):

[0150] , formula (10);

[0151] , formula (11);

[0152] , formula (12);

[0153] in, This represents the experimental data after removing the background signal, i.e., the intensity values ​​of the photoelectron spectroscopy data point sequence. It should be noted that the parameters in formulas (5) to (12) above, other than the model parameters (e.g., ... , , (etc.) are all intermediate parameters or preset parameters, which can be initialized according to requirements. This application embodiment does not make specific limitations on this.

[0154] With the ( Model parameters for step ) Then the ( ) can be obtained through formulas (5) to (9). +1) Step model parameters Substituting this into formula (3) yields the ()th +1) Step 1 The peak function of the ( ) photoelectron is obtained by substituting it into formula (4). +1) The theoretical photoelectron energy spectrum.

[0155] It should be noted that the splitting peak refers to a pair of photoelectron peaks caused by the spin-orbit coupling of electrons. The satellite companion peak refers to a photoelectron peak caused by multiple excitation states due to the final state effect. These multiple excitation states can include multiple splitting, electron tremor excitation, and tremor separation. Among them, the final state effect refers to the energy changes and influences generated when photoelectrons escape the atomic nucleus and escape from the surface during photoemission.

[0156] In step 502 of some embodiments, the peak pairing constraint condition includes at least one of a split peak constraint condition and a satellite companion peak constraint condition, and the peak attribute ratio threshold includes at least one of a peak intensity ratio threshold and a peak area ratio threshold. In one embodiment, step 502 includes: performing a split peak pairing test on any two target photoelectron peaks using the split peak constraint condition to obtain a first target split peak and a second target split peak. The split peak constraint condition characterizes that the elements to which the two target photoelectron peaks belong are the same element with the same valence state and have the same principal quantum number and angular momentum quantum number. In another embodiment, step 502 further includes: performing a satellite companion peak pairing test on any two target photoelectron peaks using the satellite companion peak constraint condition to obtain a first target satellite companion peak and a second target satellite companion peak. The satellite companion peak constraint condition characterizes that the elements to which the two target photoelectron peaks belong are the same element with the same valence state and have the same principal quantum number and total angular momentum quantum number. It should be noted that the principal quantum number, angular momentum quantum number, and total angular momentum quantum number of any target photoelectron peak can be obtained from a preset photoelectron peak database. The element to which any target photoelectron peak belongs and the initial value of its valence state can be determined by looking up a table.

[0157] In step 503 of some embodiments, the attribute values ​​include peak intensity and peak area, and the peak attribute value ratio includes peak intensity ratio and peak area ratio. In one embodiment, after obtaining the first target splitting peak and the second target splitting peak, step 503 includes: determining the peak intensity ratio based on the peak intensity of the first target splitting peak and the peak intensity of the second target splitting peak. In another embodiment, after obtaining the first target satellite companion peak and the second target satellite companion peak, step 503 further includes: determining the peak area ratio based on the peak area of ​​the first target satellite companion peak and the peak area of ​​the second target satellite companion peak.

[0158] In step 504 of some embodiments, the centralized field approximation relationship includes at least one of the centralized field approximation split-peak relationship and the centralized field approximation satellite companion peak relationship. In one embodiment, after determining the peak intensity ratio, step 504 includes: updating the model parameters of the first photoelectron spectrum fitting model according to the centralized field approximation split-peak relationship to obtain a second photoelectron spectrum fitting model when the peak intensity ratio is not equal to a peak intensity ratio threshold. In another embodiment, after determining the peak area ratio, step 504 further includes: updating the model parameters of the first photoelectron spectrum fitting model according to the centralized field approximation satellite companion peak relationship to obtain a second photoelectron spectrum fitting model when the peak area ratio is not equal to a peak area ratio threshold. Alternatively, the model parameters of the second photoelectron spectrum fitting model can be updated according to the centralized field approximation satellite companion peak relationship to obtain an updated second photoelectron spectrum fitting model.

[0159] Please see Figure 8 In some embodiments, the model parameters of the first photoelectron spectroscopy fitting model include initial peak intensity parameters and initial peak position parameters; the peak attribute value ratio includes the peak intensity ratio, the peak attribute ratio threshold includes the peak intensity ratio threshold, and the centralized field approximation relationship includes the centralized field approximation splitting peak relationship. Step 504 may include, but is not limited to, steps 601 to 606:

[0160] Step 601: When the peak intensity ratio is not equal to the peak intensity ratio threshold, determine the first peak intensity constraint formula and the peak position constraint formula according to the centralized field approximate splitting peak relationship formula.

[0161] Step 602: Determine the first peak strength constraint factor based on the first peak strength constraint relationship, the total angular quantum number of the first target photoelectron peak, and the total angular quantum number of the second target photoelectron peak;

[0162] Step 603: Update the initial peak intensity parameters according to the first peak intensity constraint factor to obtain the first target peak intensity parameters;

[0163] Step 604: Determine the peak position constraint factor based on the peak position constraint formula, the principal quantum number, angular quantum number and total angular quantum number of the first target photoelectron peak, and the principal quantum number, angular quantum number and total angular quantum number of the second target photoelectron peak;

[0164] Step 605: Update the initial peak position parameters according to the peak position constraint factor to obtain the target peak position parameters;

[0165] Step 606: Determine the second photoelectron spectrum fitting model based on the first target peak intensity parameter and the target peak position parameter.

[0166] The advantage of this embodiment is that the first peak intensity constraint formula and the peak position constraint formula are determined by the centralized field approximation splitting peak relationship formula. The initial peak intensity parameter is updated according to the first peak intensity constraint factor, and the peak position constraint factor is determined according to the peak position constraint formula. Then, the initial peak intensity parameter is updated according to the first peak intensity constraint factor to obtain the first target peak intensity parameter, and the initial peak position parameter is updated according to the peak position constraint factor to obtain the target peak position parameter. This ensures that the target photoelectron spectrum output by the second photoelectron spectrum fitting model conforms to the relevant constraints of the splitting peak, that is, the peak intensity ratio of any pair of splitting peaks in the target photoelectron spectrum is equal to the peak intensity ratio threshold, thereby improving the authenticity of the spectrum analysis results.

[0167] It should be noted that the initial peak intensity parameter refers to the first peak intensity parameter in formula (4). The initial peak position parameter refers to the peak position parameter in formula (3). .

[0168] In step 601 of some embodiments, the centralized field approximate splitting peak relation includes a first peak strength constraint relation and a peak position constraint relation.

[0169] In step 602 of some embodiments, the first peak strength constraint factor is defined as shown in formula (13):

[0170] , formula (13);

[0171] in, This represents the first peak strong constraint factor. This represents the total angular momentum quantum number of the first target photoelectron peak. The total angular quantum number of the second target photoelectron peak is represented by formula (13), which is also the strong constraint relationship of the first peak.

[0172] Please see Figure 9 In some embodiments, the model parameters of the first photoelectron spectroscopy fitting model include initial peak intensity parameters; the peak attribute value ratio also includes peak area ratio; the peak pairing constraint condition also includes satellite companion peak constraint condition; the peak attribute ratio threshold also includes peak area ratio threshold; and the centralized field approximation relationship also includes the centralized field approximation satellite companion peak relationship. Step 504 may also include, but is not limited to, steps 701 to 704:

[0173] Step 701: When the peak area ratio is not equal to the peak area ratio threshold, determine the second peak strength constraint formula based on the approximate satellite companion peak relationship formula of the centralized field.

[0174] Step 702: Determine the second peak intensity constraint factor based on the second peak intensity constraint formula, the peak area of ​​the first target photoelectron peak, and the peak area of ​​the second target photoelectron peak.

[0175] Step 703: Update the initial peak intensity parameters according to the second peak intensity constraint factor to obtain the second target peak intensity parameters;

[0176] Step 704: Determine the second photoelectron spectrum fitting model based on the second target peak intensity parameter.

[0177] The advantage of this embodiment is that, based on the second peak intensity constraint relationship, the peak area of ​​the first target photoelectron peak, and the peak area of ​​the second target photoelectron peak, the second peak intensity constraint factor is determined, and then the second target peak intensity parameter is determined based on the second peak intensity constraint factor. This ensures that the target photoelectron spectrum output by the second photoelectron spectrum fitting model conforms to the relevant constraints of the satellite companion peaks, that is, it ensures that the peak area ratio of any pair of satellite companion peaks in the target photoelectron spectrum is equal to the peak area ratio threshold, thereby improving the authenticity of the spectrum analysis results.

[0178] In step 702 of some embodiments, the first target photoelectron peak includes a first target satellite companion peak and a second target satellite companion peak, which are a pair of satellite companion peaks. The second target photoelectron peak includes a third target satellite companion peak and a fourth target satellite companion peak, which are a pair of satellite companion peaks. The second peak intensity constraint factor can be determined according to the second peak intensity constraint relationship, the peak area of ​​the first target photoelectron peak, and the peak area of ​​the second target photoelectron peak. The definition of the second peak intensity constraint factor is shown in formula (14):

[0179] , formula (14);

[0180] in, This represents the strong constraint factor for the second peak. This represents the peak area of ​​the accompanying peak of the first target satellite. This represents the peak area of ​​the second target satellite's accompanying peak. This represents the peak area of ​​the third target satellite's accompanying peak. The peak area of ​​the fourth target satellite's companion peak is represented by formula (14), which is also the second peak strength constraint relationship.

[0181] Please see Figure 10 This application also provides a photoelectron spectroscopy analysis device that can implement the above-described photoelectron spectroscopy analysis method. The device includes:

[0182] The photoelectron spectroscopy acquisition module 801 is used to perform photoelectron spectroscopy detection on the target sample and acquire the initial photoelectron spectrum.

[0183] The data point selection module 802 is used to select energy spectrum data points based on the initial photoelectron energy spectrum to obtain a photoelectron energy spectrum data point sequence.

[0184] The first model parameter update module 803 is used to update the first model parameters of the preset initial photoelectron spectrum fitting model according to the preset expectation maximization algorithm and the photoelectron spectrum data point sequence, so as to obtain the first photoelectron spectrum fitting model.

[0185] The quantum effect constraint acquisition module 804 is used to acquire the target quantum effect constraint of the target sample.

[0186] The second model parameter update module 805 is used to update the second model parameters of the first photoelectron spectrum fitting model according to the preset centralized field approximation relation and the target quantum effect constraint, so as to obtain the second photoelectron spectrum fitting model.

[0187] The energy spectrum fitting module 806 is used to perform energy spectrum fitting on the photoelectron energy spectrum data point sequence according to the second photoelectron energy spectrum fitting model to obtain the target photoelectron energy spectrum.

[0188] The specific implementation of this photoelectron spectroscopy analysis device is basically the same as the specific implementation of the photoelectron spectroscopy analysis method described above, and will not be repeated here.

[0189] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described photoelectron spectroscopy analysis method. This electronic device can include any smart terminal such as a tablet computer or in-vehicle computer.

[0190] Please see Figure 11 , Figure 11 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0191] The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0192] The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and called and executed by the processor 901 using the photoelectron spectroscopy analysis method of the embodiments of this application.

[0193] The input / output interface 903 is used to implement information input and output;

[0194] The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0195] Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904);

[0196] The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0197] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described photoelectron spectroscopy analysis method.

[0198] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0199] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0200] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0201] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0202] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0203] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application 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 this application 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.

[0204] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0205] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, or indirect coupling or communication connection between the apparatus or units, and may be electrical, mechanical, or other forms.

[0206] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0207] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0208] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0209] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A photoelectron spectroscopy analysis method, characterized in that, The method includes: The initial photoelectron spectrum of the target sample is obtained by performing photoelectron spectroscopy detection. Based on the initial photoelectron energy spectrum, energy spectrum data points are selected to obtain a photoelectron energy spectrum data point sequence; The first model parameter of the preset initial photoelectron spectrum fitting model is updated according to the preset expectation maximization algorithm and the photoelectron spectrum data point sequence to obtain the first photoelectron spectrum fitting model. Obtain the target quantum effect constraint of the target sample; wherein, the target quantum effect constraint includes peak pairing constraint conditions and peak attribute ratio threshold; The first photoelectron spectrum fitting model is used to fit the photoelectron peaks of the photoelectron spectrum data point sequence to obtain at least two target photoelectron peaks. By applying the peak pairing constraint condition, peak pairing test is performed on any two target photoelectron peaks to obtain the first target photoelectron peak and the second target photoelectron peak. The peak attribute value ratio is determined based on the attribute values ​​of the first target photoelectron peak and the second target photoelectron peak. When the peak attribute value ratio is not equal to the peak attribute ratio threshold, the model parameters of the first photoelectron spectrum fitting model are updated according to the preset centralized field approximation relationship to obtain the second photoelectron spectrum fitting model. The photoelectron spectrum data point sequence is fitted with the second photoelectron spectrum fitting model to obtain the target photoelectron spectrum. The model parameters of the first photoelectron spectroscopy fitting model include initial peak intensity parameters and initial peak position parameters; the peak attribute value ratio includes peak intensity ratio; the peak attribute ratio threshold includes peak intensity ratio threshold; and the centralized field approximation formula includes the centralized field approximation splitting peak formula. When the peak attribute value ratio is not equal to the peak attribute ratio threshold, the model parameters of the first photoelectron spectrum fitting model are updated according to a preset centralized field approximation formula to obtain a second photoelectron spectrum fitting model, including: When the peak intensity ratio is not equal to the peak intensity ratio threshold, the first peak intensity constraint formula and the peak position constraint formula are determined according to the centralized field approximate splitting peak relationship formula. The first peak intensity constraint factor is determined based on the first peak intensity constraint formula, the total angular quantum number of the first target photoelectron peak, and the total angular quantum number of the second target photoelectron peak. The initial peak intensity parameter is updated according to the first peak intensity constraint factor to obtain the first target peak intensity parameter; Based on the peak position constraint formula, the principal quantum number, angular quantum number and total angular quantum number of the first target photoelectron peak, and the principal quantum number, angular quantum number and total angular quantum number of the second target photoelectron peak, the peak position constraint factor is determined; The initial peak position parameters are updated according to the peak position constraint factor to obtain the target peak position parameters; The second photoelectron spectroscopy fitting model is determined based on the first target peak intensity parameter and the target peak position parameter; Alternatively, the model parameters of the first photoelectron spectroscopy fitting model include the initial peak intensity parameter; the peak attribute value ratio also includes the peak area ratio; the peak pairing constraint condition also includes the satellite companion peak constraint condition; the peak attribute ratio threshold also includes the peak area ratio threshold; and the centralized field approximation relationship also includes the centralized field approximation satellite companion peak relationship. When the peak attribute value ratio is not equal to the peak attribute ratio threshold, updating the model parameters of the first photoelectron spectrum fitting model according to a preset centralized field approximation formula to obtain a second photoelectron spectrum fitting model further includes: When the peak area ratio is not equal to the peak area ratio threshold, the second peak strength constraint relationship is determined according to the centralized field approximate satellite companion peak relationship. The second peak intensity constraint factor is determined based on the second peak intensity constraint formula, the peak area of ​​the first target photoelectron peak, and the peak area of ​​the second target photoelectron peak. The initial peak intensity parameter is updated according to the second peak intensity constraint factor to obtain the second target peak intensity parameter; The second photoelectron spectrum fitting model is determined based on the second target peak intensity parameter.

2. The photoelectron spectroscopy analysis method according to claim 1, characterized in that, The initial photoelectron spectrum has an initial photoelectron peak; the step of selecting spectral data points based on the initial photoelectron spectrum to obtain a photoelectron spectrum data point sequence includes: The energy spectrum data points are divided according to the spectral lines of the initial photoelectron energy spectrum to obtain at least two initial background data points and at least two initial non-background data points; wherein each of the initial background data points and each of the initial non-background data points has an energy value and an intensity value. The background energy spectrum curves are obtained by fitting at least two initial background data points with a preset Gaussian random process function. Based on the energy value and intensity value of each initial background data point and the energy value of each initial non-background data point, the intensity value of each initial non-background data point is updated by a first intensity value to obtain the corresponding target non-background data point. The intensity value of each initial background data point is updated with a second intensity value based on the background energy spectrum curve to obtain the corresponding target background data point; wherein the intensity value of the target background data point is less than the intensity value of the initial background data point; The photoelectron spectroscopy data point sequence is obtained by integrating the target non-background data points and the target background data points.

3. The photoelectron spectroscopy analysis method according to claim 2, characterized in that, The step of updating the intensity value of each initial non-background data point by a first intensity value based on the energy value and intensity value of each initial background data point and the energy value of each initial non-background data point to obtain the corresponding target non-background data point includes: The background intensity is calculated based on the energy and intensity values ​​of each initial background data point and the energy value of each initial non-background data point to obtain the background intensity vector; The intensity value of each initial non-background data point is updated by performing a first intensity value update based on the background intensity vector to obtain the target non-background data point.

4. The photoelectron spectroscopy analysis method according to claim 2, characterized in that, The step of dividing the energy spectrum data points based on the spectral lines of the initial photoelectron energy spectrum to obtain at least two initial background data points and at least two initial non-background data points includes: The initial photoelectron energy spectrum is used to select energy spectrum data points to obtain multiple initial energy spectrum data points; Each initial energy spectrum data point is divided according to the trough of the initial photoelectron energy spectrum to obtain at least two initial background data points and at least two initial non-background data points; wherein, the initial background data points are located at the trough of the spectral line.

5. A photoelectron spectroscopy analysis device, characterized in that, The device includes: The photoelectron spectroscopy acquisition module is used to perform photoelectron spectroscopy detection on the target sample and acquire the initial photoelectron spectrum. The data point selection module is used to select energy spectrum data points based on the initial photoelectron energy spectrum to obtain a photoelectron energy spectrum data point sequence. The first model parameter update module is used to update the first model parameters of the preset initial photoelectron spectrum fitting model according to the preset expectation maximization algorithm and the photoelectron spectrum data point sequence, so as to obtain the first photoelectron spectrum fitting model. A quantum effect constraint acquisition module is used to acquire the target quantum effect constraint of the target sample; wherein, the target quantum effect constraint includes peak pairing constraint conditions and peak attribute ratio thresholds; The second model parameter update module is used to fit the photoelectron peaks of the photoelectron spectrum data point sequence to the first photoelectron spectrum fitting model to obtain at least two target photoelectron peaks; perform peak pairing tests on any two target photoelectron peaks through the peak pairing constraint condition to obtain a first target photoelectron peak and a second target photoelectron peak; determine the peak attribute value ratio based on the attribute values ​​of the first target photoelectron peak and the second target photoelectron peak; and update the model parameters of the first photoelectron spectrum fitting model according to a preset centralized field approximation formula when the peak attribute value ratio is not equal to the peak attribute ratio threshold to obtain a second photoelectron spectrum fitting model. The model parameters of the first photoelectron spectroscopy fitting model include initial peak intensity parameters and initial peak position parameters; the peak attribute value ratio includes peak intensity ratio, the peak attribute ratio threshold includes peak intensity ratio threshold, and the centralized field approximation relationship includes a centralized field approximation splitting peak relationship; the step of updating the model parameters of the first photoelectron spectroscopy fitting model according to a preset centralized field approximation relationship to obtain a second photoelectron spectroscopy fitting model when the peak attribute value ratio is not equal to the peak attribute ratio threshold includes: when the peak intensity ratio is not equal to the peak intensity ratio threshold, determining a first peak intensity constraint relationship and a peak position constraint relationship according to the centralized field approximation splitting peak relationship; Based on the first peak intensity constraint equation, the total angular momentum quantum number of the first target photoelectron peak, and the total angular momentum quantum number of the second target photoelectron peak, a first peak intensity constraint factor is determined; the initial peak intensity parameter is updated based on the first peak intensity constraint factor to obtain the first target peak intensity parameter; based on the peak position constraint equation, the principal quantum number, angular momentum quantum number, and total angular momentum quantum number of the first target photoelectron peak, and the principal quantum number, angular momentum quantum number, and total angular momentum quantum number of the second target photoelectron peak, a peak position constraint factor is determined; the initial peak position parameter is updated based on the peak position constraint factor to obtain the target peak position parameter; and a second photoelectron spectrum fitting model is determined based on the first target peak intensity parameter and the target peak position parameter. Alternatively, the model parameters of the first photoelectron spectrum fitting model include an initial peak intensity parameter; the peak attribute value ratio also includes a peak area ratio; the peak pairing constraint condition also includes a satellite companion peak constraint condition; the peak attribute ratio threshold also includes a peak area ratio threshold; and the centralized field approximation relationship also includes a centralized field approximation satellite companion peak relationship. The step of updating the model parameters of the first photoelectron spectrum fitting model according to a preset centralized field approximation relationship to obtain a second photoelectron spectrum fitting model when the peak attribute value ratio is not equal to the peak attribute ratio threshold further includes: determining a second peak intensity constraint relationship according to the centralized field approximation satellite companion peak relationship when the peak area ratio is not equal to the peak area ratio threshold; determining a second peak intensity constraint factor according to the second peak intensity constraint relationship, the peak area of ​​the first target photoelectron peak, and the peak area of ​​the second target photoelectron peak; updating the initial peak intensity parameter according to the second peak intensity constraint factor to obtain a second target peak intensity parameter; and determining the second photoelectron spectrum fitting model according to the second target peak intensity parameter. The energy spectrum fitting module is used to perform energy spectrum fitting on the photoelectron energy spectrum data point sequence according to the second photoelectron energy spectrum fitting model to obtain the target photoelectron energy spectrum.

6. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the photoelectron spectroscopy analysis method according to any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the photoelectron spectroscopy analysis method according to any one of claims 1 to 4.

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