Method for testing energy resolution of a continuous tunable ultraviolet photoelectron spectrometer

Abstract

The application provides a continuous adjustable ultraviolet photoelectron spectrometer energy resolution test method, and relates to the technical field of instrument analysis, and comprises the following steps: a photon beam with a target wavelength is used to irradiate a to-be-tested sample; an ultraviolet photoelectron spectrum of the to-be-tested sample is constructed according to the distribution quantity of photoelectrons with different kinetic energies excited from the to-be-tested sample; a Fermi edge region is determined from the ultraviolet photoelectron spectrum of the to-be-tested sample, and shape information of the Fermi edge region is extracted; a convolution model used for describing thermal broadening and instrument broadening is constructed; the shape information of the Fermi edge region is fitted by using a particle swarm optimization algorithm based on the convolution model, and the energy resolution of the ultraviolet photoelectron spectrometer is determined according to a fitting result. The application objectively quantifies the Fermi edge position and a fitting interval, is not easy to be disturbed by noise, has high repeatability and is easy to be automatically implemented, and well meets the modern analysis requirements such as high-throughput testing or online monitoring.

Description

Technical Field

[0001] This invention relates to the field of instrumental analysis technology, specifically to a method for testing the energy resolution of a continuously adjustable ultraviolet photoelectron spectrometer. Background Technology

[0002] Ultraviolet photoelectron spectroscopy (UPS) is a key surface analysis technique widely used in semiconductors, metals, organic materials, and two-dimensional materials to characterize the electronic structure information of materials, such as work function, valence band edge, and surface states. Energy resolution is a core indicator for evaluating the performance of UPS instruments, directly affecting the accuracy and reliability of data analysis. According to the national standard GB / T41072-2021 "Guideline for Ultraviolet Photoelectron Spectroscopy Analysis," related techniques typically use standard samples (such as gold thin films) and evaluate resolution through Fermi edge energy distribution, with the "20%~80% energy intensity method" being the most commonly used simplified scheme.

[0003] However, this method uses the energy gap corresponding to 20% and 80% intensity of the Fermi edge as an approximation of resolution, relying solely on empirical formulas. This fails to separate broadening introduced by various factors, leading to resolution estimates deviating from the true physical reality. The fitting process is highly dependent on manual visual inspection and empirical judgment, requiring subjective determination of the Fermi edge position and fitting interval. It is susceptible to noise interference, has poor repeatability, and cannot be automated. Summary of the Invention

[0004] This invention provides a method for testing the energy resolution of a continuously tunable ultraviolet photoelectron spectrometer, aiming to solve the problems existing in the background technology. To solve the above-mentioned technical problems, this invention is implemented as follows: In a first aspect, embodiments of the present invention provide a method for testing the energy resolution of a continuously tunable ultraviolet photoelectron spectrometer, comprising: The sample to be tested is irradiated with a photon beam of the target wavelength. The ultraviolet photoelectron spectrum of the sample to be tested is constructed based on the distribution of the number of photoelectrons with different kinetic energies excited by the sample. The photon beam belongs to the vacuum ultraviolet band. The horizontal axis of the ultraviolet photoelectron spectrum is the photoelectron kinetic energy, and the vertical axis is the photoelectron intensity, which represents the distribution of the number of photoelectrons. The Fermi edge region is determined from the ultraviolet photoelectron spectrum of the sample to be tested, and the shape information of the Fermi edge region is extracted. The Fermi edge region is a preset width range centered on the position of the Fermi edge. Construct a convolutional model to describe thermal broadening and instrument broadening; Based on a pre-constructed convolution model using the Gaussian broadening parameters of the UV photoelectron spectrometer to be tested as fitting parameters, the shape information of the Fermi edge region is fitted using the particle swarm optimization algorithm, and the energy resolution of the UV photoelectron spectrometer is determined based on the fitting results; the convolution model is used to describe thermal broadening and instrument broadening.

[0005] Alternatively, the convolutional model can be constructed according to the following steps: A Fermi-Dirac distribution function considering the density of states distribution is constructed. This Fermi-Dirac distribution function considering the density of states distribution is used to describe the combined effect of the electronic density of states distribution and thermal broadening effect on the photoelectron intensity distribution in the sample material under test. The Fermi level parameter in the Fermi-Dirac distribution function considering the density of states distribution is a pre-calibrated known value. A Gaussian function is constructed to describe the energy broadening effect introduced by the energy resolution of the ultraviolet photoelectron spectrometer; the Gaussian broadening parameters contained in the Gaussian function are used as unknown parameters to be fitted. The convolution operation is performed between the Fermi-Dirac distribution function and the Gaussian function to obtain the convolution model, which includes the Gaussian broadening parameters to be fitted.

[0006] Optionally, based on a pre-constructed convolution model using the Gaussian broadening parameters of the UV photoelectron spectrometer to be measured as fitting parameters, the shape information of the Fermi edge region is fitted using a particle swarm optimization algorithm, and the energy resolution of the UV photoelectron spectrometer is determined based on the fitting results, including: Using the particle swarm optimization algorithm, with the goal of minimizing the sum of squared residuals between the fitted function and the shape information, the Gaussian broadening parameter in the convolution model is iteratively adjusted. When the residual converges to a preset threshold, a fitting result including the optimal Gaussian broadening parameter is obtained; The energy resolution of the ultraviolet photoelectron spectrometer is calculated based on the optimal Gaussian broadening parameter in the fitting results.

[0007] Optionally, using a particle swarm optimization algorithm, with the objective of minimizing the sum of squared residuals between the fitted function and the shape information, the Gaussian broadening parameter in the convolutional model is iteratively adjusted, including: Set up a particle swarm containing multiple particles; where each particle represents a candidate value of a Gaussian broadening parameter, the position of each particle represents the current solution of the corresponding Gaussian broadening parameter, and the velocity of each particle represents the search direction of the corresponding Gaussian broadening parameter. In each iteration, the position of each particle in the particle swarm is updated based on the current residual sum of squares until the minimum value of the residual sum of squares reaches a preset convergence threshold.

[0008] Optionally, in each iteration, the position of each particle in the particle swarm is updated based on the current residual sum of squares until the minimum residual sum of squares reaches a preset convergence threshold, including: In each iteration, the Gaussian broadening parameter corresponding to the minimum residual sum of squares reached by each particle in the particle swarm is taken as the historical best position of that particle. From the historical best positions of all particles in the particle swarm, select the Gaussian broadening parameter with the smallest sum of squared residuals as the current global best position of the particle swarm. Based on each particle's current position, historical best position, and global best position, as well as preset inertia weights and random perturbation factors, calculate the particle's movement direction and step size. Based on the direction and step size of each particle's movement, update the particle's velocity, and then update the particle's position based on the updated velocity; Repeat the above steps until the sum of squared residuals converges to a preset threshold.

[0009] Optionally, the sample to be tested is irradiated with a photon beam of the target wavelength, and the ultraviolet photoelectron spectrum of the sample to be tested is constructed based on the distribution of photoelectrons with different kinetic energies excited by the sample, including: The sample to be tested is irradiated with a photon beam of the target wavelength to obtain photoelectrons with different kinetic energies excited by the sample to be tested. Using a hemispherical energy analyzer, photoelectrons with different kinetic energies are screened from the photoelectrons with different kinetic energies excited by the sample under test. The signals of photoelectrons with different kinetic energies are amplified by a microchannel plate, and the corresponding light emission images are obtained on a fluorescent screen. The light emission images include pixels at multiple different positions. The luminescent image is captured using an industrial camera; All the light-emitting images captured by the industrial camera are superimposed, and the pixels with the same electron kinetic energy are integrated to obtain the ultraviolet photoelectron spectrum of the sample under test.

[0010] Optionally, before irradiating the sample to be tested with photon beams of different wavelengths, the method further includes: A reference sample is irradiated with an electron beam of target energy emitted by an electron gun to obtain photoelectrons excited in the reference sample; The hemispherical energy analyzer is used to filter out photoelectrons with different kinetic energies. The microchannel plate amplifies the signals of photoelectrons with different kinetic energies and obtains corresponding luminescent images on the fluorescent screen. The luminescent image is captured using an industrial camera; The energy calibration coefficient is calculated based on the correspondence between the pixel position and photoelectron kinetic energy of each pixel in the light emission imaging. The ultraviolet photoelectron spectrum of the sample under test is obtained by superimposing all the luminescent images captured by the industrial camera and integrating the pixels with the same electron kinetic energy, including: Based on the energy calibration coefficient, the pixel position of each pixel in the light emission imaging is converted into the corresponding photoelectron kinetic energy; The photoelectron kinetic energies corresponding to the pixel positions of each pixel in the light emission imaging are superimposed, and the data points with the same photoelectron kinetic energy are integrated to obtain the ultraviolet photoelectron spectrum of the sample under test.

[0011] Optionally, the Fermi edge region is determined from the ultraviolet photoelectron spectrum of the sample to be tested, and the shape information of the Fermi edge region is extracted, including: Calculate the first derivative of the ultraviolet photoelectron spectrum, and determine the position of the Fermi edge based on the first derivative; Centered on the position of the Fermi edge, a range of the preset width is selected as the Fermi edge region; Based on the maximum and minimum values ​​of photoelectron intensity within the Fermi edge region, the photoelectron intensity within the Fermi edge region is mapped to a standard intensity range to obtain the shape information.

[0012] Optionally, the sample to be tested is irradiated with a photon beam of the target wavelength, including: Metal films with a purity higher than the preset value were used as the test samples; The surface of the sample to be tested is etched using an argon ion beam to remove the oxide layer and contaminants from the surface of the sample to be tested. The ultraviolet spectrum of the continuous band generated by the deuterium lamp is processed using a vacuum ultraviolet monochromator to obtain a photon beam of the target wavelength. In a vacuum sample chamber, the sample to be tested is irradiated with a photon beam of the target wavelength.

[0013] In a second aspect, embodiments of the present invention provide a continuously adjustable ultraviolet photoelectron spectrometer energy resolution measurement device, applied to the steps of performing the method as described in the first aspect; including: The first construction module is used to irradiate the sample to be tested with a photon beam of the target wavelength, and construct the ultraviolet photoelectron spectrum of the sample to be tested based on the distribution of the number of photoelectrons with different kinetic energies excited by the sample to be tested. The photon beam belongs to the vacuum ultraviolet band, and the horizontal axis of the ultraviolet photoelectron spectrum is the photoelectron kinetic energy, and the vertical axis is the photoelectron intensity that characterizes the distribution of the number of photoelectrons. The second construction module is used to determine the Fermi edge region from the ultraviolet photoelectron spectrum of the sample to be tested, and extract the shape information of the Fermi edge region, wherein the Fermi edge region is a preset width range centered on the position of the Fermi edge. The fitting module is used to fit the shape information of the Fermi edge region based on a pre-constructed convolution model with the Gaussian broadening parameters of the UV photoelectron spectrometer to be tested as the fitting parameters, and to determine the energy resolution of the UV photoelectron spectrometer based on the fitting results; the convolution model is used to describe thermal broadening and instrument broadening.

[0014] Optionally, the fitting module includes: The first construction submodule is used to construct a Fermi-Dirac distribution function considering the density of states distribution. The Fermi-Dirac distribution function considering the density of states distribution is used to describe the combined effect of the electronic density of states distribution and thermal broadening effect on the photoelectron intensity distribution in the sample material under test. The Fermi level parameter in the Fermi-Dirac distribution function considering the density of states distribution is a pre-calibrated known value. The second construction submodule is used to construct a Gaussian function, which describes the energy broadening effect introduced by the energy resolution of the ultraviolet photoelectron spectrometer; the Gaussian broadening parameters contained in the Gaussian function are used as unknown parameters to be fitted. The third construction submodule is used to perform a convolution operation between the Fermi-Dirac distribution function and the Gaussian function to obtain the convolution model, which includes the Gaussian broadening parameters to be fitted.

[0015] Optionally, the fitting module includes: The first fitting submodule is used to iteratively adjust the Gaussian broadening parameter in the convolution model by using the particle swarm optimization algorithm with the goal of minimizing the sum of squared residuals between the fitting function and the shape information. The second fitting submodule is used to obtain a fitting result including the optimal Gaussian broadening parameters when the residual converges to a preset threshold. The third fitting submodule is used to calculate the energy resolution of the ultraviolet photoelectron spectrometer based on the optimal Gaussian broadening parameter in the fitting result.

[0016] Optionally, the first fitting submodule includes: The first fitting unit is used to set up a particle swarm containing multiple particles; wherein each particle represents a candidate value of a Gaussian broadening parameter, the position of each particle represents the current solution of the corresponding Gaussian broadening parameter, and the velocity of each particle represents the search direction of the corresponding Gaussian broadening parameter. The second fitting unit is used to update the position of each particle in the particle swarm based on the current residual sum of squares in each iteration until the minimum value of the residual sum of squares reaches a preset convergence threshold.

[0017] Optionally, the second fitting unit includes: The first fitting subunit is used in each iteration to take the Gaussian broadening parameter corresponding to the minimum residual sum of squares reached by each particle in the particle swarm as the historical best position of that particle. The second fitting subunit is used to select the Gaussian broadening parameter with the smallest sum of squared residuals from the historical best positions of all particles in the particle swarm, and use it as the current global best position of the particle swarm. The third fitting subunit is used to calculate the direction and step size of the particle's movement based on the particle's current position, historical best position, global best position, and preset inertial weight and random perturbation factor. The fourth fitting subunit is used to update the velocity of each particle based on the direction and step size of its movement, and to update the position of the particle based on the updated velocity. The fifth fitting subunit is used to repeat the above steps until the sum of squared residuals converges to a preset threshold.

[0018] Optionally, the second building module includes: The fourth construction submodule is used to irradiate the sample under test with a photon beam of the target wavelength to obtain photoelectrons with different kinetic energies excited by the sample under test. The fifth construction submodule is used to screen out photoelectrons with different kinetic energies from the photoelectrons with different kinetic energies excited by the sample under test using a hemispherical energy analyzer. The sixth construction submodule is used to amplify the signals of photoelectrons with different kinetic energies through a microchannel plate and obtain corresponding light emission images on a fluorescent screen. The light emission images include pixels at multiple different positions. The seventh construction submodule is used to capture the luminescent image using an industrial camera; The eighth construction submodule is used to superimpose all the light-emitting images captured by the industrial camera and integrate the pixels with the same electron kinetic energy to obtain the ultraviolet photoelectron spectrum of the sample to be tested.

[0019] Optionally, the device further includes: An irradiation module is used to irradiate a reference sample with an electron beam of target energy emitted by an electron gun, thereby obtaining photoelectrons excited by the reference sample. The screening module is used to screen out photoelectrons with different kinetic energies through the hemispherical energy analyzer; The amplification module is used to amplify the signals of photoelectrons with different kinetic energies through the microchannel plate and obtain the corresponding luminescent images on the fluorescent screen; A scanning module is used to capture the luminescent image using an industrial camera; The calculation module is used to calculate the energy calibration coefficient based on the correspondence between the pixel position of each pixel in the light emission imaging and the photoelectron kinetic energy. The eighth construction submodule includes: The first building unit is used to superimpose all the light-emitting images captured by the industrial camera and integrate the pixels with the same electron kinetic energy to obtain the initial ultraviolet photoelectron spectrum. The second building unit is used to convert the pixel position of each pixel in the initial ultraviolet photoelectron energy spectrum into the corresponding photoelectron kinetic energy based on the energy calibration coefficient. The third building unit is used to obtain the ultraviolet photoelectron spectrum of the sample under test based on the converted photoelectron kinetic energy and its corresponding photoelectron intensity.

[0020] Optionally, the second building module includes: The ninth construction submodule is used to calculate the first derivative of the ultraviolet photoelectron spectrum and determine the position of the Fermi edge based on the first derivative; The tenth construction submodule is used to select an interval of the preset width range as the Fermi edge region, with the position of the Fermi edge as the center. The eleventh construction submodule is used to map the photoelectron intensity in the Fermi edge region to a standard intensity range based on the maximum and minimum values ​​of the photoelectron intensity in the Fermi edge region, thereby obtaining the shape information.

[0021] Optionally, the first building module includes: The eleventh construction submodule is used to use metal thin films with higher than preset purity as test samples; The twelfth construction submodule is used to etch the surface of the sample to be tested using an argon ion beam to remove the oxide layer and contaminants on the surface of the sample to be tested. The thirteenth construction submodule is used to process the continuous-band ultraviolet spectrum generated by the deuterium lamp using a vacuum ultraviolet monochromator to obtain a photon beam of the target wavelength. The fourteenth construction submodule is used to irradiate the sample under test with a photon beam of the target wavelength in a sample chamber in a vacuum environment.

[0022] The technical solutions provided by the embodiments of the present invention bring at least the following beneficial effects: First, this invention accurately separates the contributions of thermal broadening and instrument broadening by constructing a convolutional model to describe their interaction, directly capturing the intrinsic characteristics of instrument broadening and thus obtaining more reliable energy resolution data. This not only solves the shortcomings of model simplification in traditional methods but also provides a solid physical basis for instrument performance evaluation. Second, the introduction of a particle swarm optimization algorithm enables automated fitting, significantly improving measurement repeatability and efficiency. No manual intervention is required in the parameter optimization process, making it suitable for high-throughput testing scenarios, such as batch sample analysis or online monitoring systems, significantly reducing operational complexity and improving testing efficiency. Finally, this invention constructs ultraviolet photoelectron spectroscopy based on vacuum ultraviolet photon beams and extracts shape information by combining it with a preset Fermi edge region. This is applicable not only to standard metal samples but can also be seamlessly extended to the verification analysis of organic molecules or semiconductor materials. Therefore, this invention objectively quantifies the Fermi edge position and fitting interval, is less susceptible to noise interference, has high repeatability, and is easy to automate, perfectly meeting the needs of modern analysis such as high-throughput testing or online monitoring. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below.

[0024] Figure 1 This is a schematic diagram of the steps of a continuously adjustable ultraviolet photoelectron spectrometer energy resolution testing method provided in one embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the fitting effect of ultraviolet photoelectron spectroscopy in Fermi level determination in one embodiment of the present invention; Figure 3 This is a schematic diagram of the fitting and iterative process of the particle swarm optimization algorithm in one embodiment of the present invention; Figure 4 This is a structural block diagram of a continuously adjustable ultraviolet photoelectron spectrometer energy resolution measurement device provided in one embodiment of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Related techniques approximate resolution by measuring the energy gap between 20% and 80% intensity at the Fermi edge. Relying solely on empirical formulas, these techniques fail to isolate broadening introduced by various factors, leading to resolution estimates that deviate from the true physical reality. The fitting process heavily relies on manual visual inspection and experience, requiring subjective determination of the Fermi edge position and fitting interval. It is susceptible to noise interference, exhibits poor repeatability, and cannot be automated.

[0027] To address the aforementioned problems, the core concept of this invention lies in constructing a convolutional model incorporating both thermal and instrumental broadening effects. This model utilizes a particle swarm optimization algorithm to automatically fit the Fermi edge region, thereby separating the influence of thermal and instrumental broadening on the ultraviolet photoelectron spectrum and improving the measurement accuracy of energy resolution. Specifically, this invention no longer relies on traditional empirical formulas and manual visual inspection. Instead, it uses a particle swarm optimization algorithm to iteratively fit shape information, overcoming noise interference while avoiding manual intervention, ensuring a high level of automation, repeatability, and accuracy in energy resolution measurement.

[0028] Figure 1 This is a schematic diagram illustrating the steps of a continuously adjustable ultraviolet photoelectron spectrometer energy resolution testing method according to an embodiment of the present invention, as shown below. Figure 1 As shown, it includes: Step S11: Irradiate the sample to be tested with a photon beam of the target wavelength. Based on the distribution of photoelectrons with different kinetic energies excited by the sample to be tested, construct the ultraviolet photoelectron spectrum of the sample to be tested. The photon beam belongs to the vacuum ultraviolet band. The horizontal axis of the ultraviolet photoelectron spectrum is the photoelectron kinetic energy, and the vertical axis is the photoelectron intensity, which represents the distribution of the number of photoelectrons.

[0029] The sample under test is irradiated with a photon beam of the target wavelength, and an ultraviolet photoelectron spectrum is constructed based on the distribution of photoelectrons excited in the sample. The photon beam of the target wavelength refers to electromagnetic radiation with a specific wavelength, belonging to the vacuum ultraviolet band. The photon beam interacts with the sample under test, exciting electrons in the sample and causing them to escape from the surface, thus generating photoelectrons.

[0030] During photoelectron emission, each photoelectron possesses a different kinetic energy due to the varying energy it absorbs when interacting with photons. The distribution of kinetic energy can reveal characteristics of the electronic structure within a sample. Therefore, the kinetic energy and corresponding intensity of photoelectrons are two important dimensions of the photoelectron spectrum, represented as the abscissa and ordinate of the ultraviolet photoelectron spectrum, respectively. Specifically, the ultraviolet photoelectron spectrum is obtained by measuring the kinetic energy distribution of photoelectrons emitted from the sample. Photoelectrons are excited from the sample by irradiating its surface with a photon beam from the vacuum ultraviolet region. By measuring the kinetic energy of the photoelectrons and displaying it in the ultraviolet photoelectron spectrum, the abscissa represents the kinetic energy of the photoelectrons, and the ordinate represents the corresponding photoelectron intensity, i.e., the number of photoelectrons detected at a given kinetic energy.

[0031] In one optional implementation, the sample to be tested is irradiated with a photon beam of the target wavelength, including: Metal films with a purity higher than the preset value were used as the test samples.

[0032] A high-purity (preferably ≥99.99%) metal thin film is selected as the sample to be tested. For example, a gold (Au) thin film can be used, with a thickness preferably between 20 nm and 100 nm. The sample to be tested has a smooth surface and is free of structural defects to ensure the representativeness of the photoelectron emission signal.

[0033] The surface of the sample to be tested is etched using an argon ion beam to remove the oxide layer and contaminants from the surface of the sample.

[0034] The sample surface was etched using an argon ion beam. Specifically, an argon ion beam with an energy of 2keV was used, and the etching duration was 1 hour. The etching process thoroughly removed the surface oxide layer and organic contaminants, bringing the sample surface to an atomically clean state.

[0035] The continuous-wavelength ultraviolet spectrum generated by the deuterium lamp is processed using a vacuum ultraviolet monochromator to obtain a photon beam of the target wavelength.

[0036] Turn on the deuterium lamp and preheat for 20 minutes to stabilize the light source output. Spectroscopically process the continuous ultraviolet spectrum generated by the deuterium lamp using a vacuum ultraviolet monochromator to select the target wavelength (example range: 115nm–400nm). Adjust the entrance and exit slits of the vacuum ultraviolet monochromator (preferably 0.5mm wide) to improve wavelength resolution and obtain a high monochromatic vacuum ultraviolet photon beam.

[0037] In a vacuum sample chamber, the sample to be tested is irradiated with a photon beam of the target wavelength.

[0038] The cleaned sample to be tested is placed in an ultra-high vacuum sample chamber (vacuum level better than 5 × 10⁻⁶). -8Pa), the target wavelength photon beam is used to vertically irradiate the surface of the sample to be tested.

[0039] In an optional implementation, step S11 further includes steps S111 to S119: Step S111: Irradiate the sample to be tested with a photon beam of the target wavelength to obtain photoelectrons with different kinetic energies excited by the sample to be tested.

[0040] A prepared beam of photons at the target wavelength is irradiated onto the surface of the sample under test. When the photon beam interacts with the sample surface, it transfers energy to electrons within the sample. These electrons are excited, and after gaining sufficient energy, they detach from the sample surface, forming photoelectrons. As mentioned earlier, since each photoelectron gains different amounts of energy when absorbing photons, their kinetic energies will also differ.

[0041] Step S112: Using a hemispherical energy analyzer, photoelectrons with different kinetic energies are screened from the photoelectrons with different kinetic energies excited by the sample to be tested.

[0042] A hemispherical energy analyzer is used to screen photoelectrons emitted from the surface of the sample. The hemispherical energy analyzer is a common instrument for electron energy analysis. Its working principle is to separate the electron beam according to its kinetic energy using a combination of electric and magnetic fields. It consists of two hemispherical electrodes. After the photoelectron beam enters the hemispherical energy analyzer, the electric field between the electrodes separates the electrons according to their energy. By adjusting the strength of the electric field, photoelectrons with different kinetic energies can be screened. The hemispherical energy analyzer can measure the kinetic energy of photoelectrons and select photoelectrons within a specific kinetic energy range, which helps in the subsequent construction of a complete ultraviolet photoelectron spectrum, revealing the energy distribution of electrons in the sample.

[0043] Step S113: The signals of photoelectrons with different kinetic energies are amplified through a microchannel plate, and corresponding light emission images are obtained on a fluorescent screen. The light emission images include pixels at multiple different positions.

[0044] The signals of photoelectrons with different kinetic energies, filtered by a hemispherical energy analyzer, are amplified by a microchannel plate. A microchannel plate is a device composed of a series of tiny channels. Its working principle is that when photoelectrons flow through these channels, they excite the internal material to release secondary electrons, thereby amplifying the original photoelectron signal. This process significantly increases the signal intensity, allowing even weak photoelectron signals to be further detected and analyzed. The amplified photoelectron signal is displayed on a fluorescent screen as a light emission image. This light emission image on the fluorescent screen is generated by the light emission produced when photoelectrons collide with fluorescent material. Each luminous point on the screen corresponds to the detection signal of a photoelectron. Each luminous point represents an electron signal at a different location.

[0045] Step S114: Take a picture of the light emission using an industrial camera.

[0046] An industrial camera is used to capture images of the light emitted on a fluorescent screen. An industrial camera is a high-resolution, high-stability imaging device capable of accurately capturing the position and brightness of each light-emitting point on the screen. Each frame captured by the industrial camera represents a light emission image with different gating energies (kinetic energies), containing the brightness and position of each pixel, further improving measurement accuracy and data integrity.

[0047] In an alternative embodiment, before irradiating the sample to be tested with photon beams of different wavelengths, the method further includes: Step S21: Irradiate the reference sample with an electron beam of target energy emitted by an electron gun to obtain photoelectrons excited by the reference sample.

[0048] An electron gun is used to emit an electron beam with a target energy to irradiate a reference sample. An electron gun is a device used to generate a high-energy electron beam. Its principle is to accelerate electrons to the target energy using an accelerating electric field, and then focus the electron beam onto the sample surface. The target energy is preset, and different values ​​can be selected according to experimental requirements. When the electron beam interacts with the surface of the reference sample, the electrons excite electrons in the reference sample, causing some of these electrons to gain sufficient energy and escape from the surface, forming photoelectrons. Therefore, the excited photoelectrons from the reference sample can provide a standard signal for calibrating the ultraviolet photoelectron spectrum of the sample under test.

[0049] Step S22: The photoelectrons with different kinetic energies are screened out using the hemispherical energy analyzer.

[0050] Step S23: The signals of photoelectrons with different kinetic energies are amplified through the microchannel plate, and corresponding luminescent images are obtained on the fluorescent screen.

[0051] Step S24: Take a picture of the light emission using an industrial camera.

[0052] The specific implementation methods of steps S22 to S24 are the same as or similar to those of steps S112 to S114, and will not be repeated here.

[0053] Step S25: Calculate the energy calibration coefficient based on the correspondence between the pixel position and photoelectron kinetic energy of each pixel in the light emission imaging.

[0054] The spatial coordinates of each pixel are extracted from the luminescent imaging data captured by an industrial camera. The spatial coordinates of each pixel represent its position on the fluorescent screen.

[0055] The photoelectron kinetic energy of each pixel is known. Using the position of each pixel and its corresponding photoelectron kinetic energy, an energy calibration coefficient is calculated. In this embodiment, the energy calibration coefficient is a parameter describing the mathematical relationship between pixel position and photoelectron kinetic energy, used to convert image coordinates into physical energy values. It can be understood that the energy calibration coefficient is an adjustment factor.

[0056] Step S115: Superimpose all the light-emitting images captured by the industrial camera and integrate the pixels with the same electron kinetic energy to obtain the ultraviolet photoelectron spectrum of the sample to be tested.

[0057] The purpose of overlaying all the luminescence imaging images captured by industrial cameras is to combine information from multiple images to obtain more comprehensive signal data, ensuring that no details are missed. During the image overlay process, the number of photoelectrons at each kinetic energy value is obtained by integrating pixels with the same photoelectron kinetic energy, i.e., the photoelectron intensity. The resulting ultraviolet photoelectron spectrum reflects the energy distribution of photoelectrons in the sample under test.

[0058] In an optional implementation, step S115 specifically includes steps S1151 to S1152: Step S1151: Based on the energy calibration coefficient, convert the pixel position of each pixel in the light emission imaging into the corresponding photoelectron kinetic energy.

[0059] Using energy calibration coefficients obtained beforehand through reference sample calibration, the spatial coordinates of each pixel in the luminescent image captured by the industrial camera are converted into the corresponding photoelectron kinetic energy value. Specifically, based on the two-dimensional coordinates of the pixel on the fluorescent screen (such as row and column numbers), combined with the mapping relationship between pixel position and photoelectron kinetic energy defined by the energy calibration coefficients, mathematical calculations are used to convert the position information of each pixel into a physically meaningful photoelectron kinetic energy value, that is, to achieve a quantitative conversion from image spatial coordinates to energy physical quantities.

[0060] Step S1152: The photoelectron kinetic energies corresponding to the pixel positions of each pixel in the light emission imaging are superimposed, and the data points with the same photoelectron kinetic energy are integrated to obtain the ultraviolet photoelectron spectrum of the sample to be tested.

[0061] All converted photoelectron kinetic energy data are superimposed and integrated. Then, the intensity of data points with the same photoelectron kinetic energy value in the superimposed data is integrated, and the photoelectron signal intensity corresponding to all pixels with the same kinetic energy value is statistically analyzed. For each data point with photoelectron kinetic energy, the total photoelectron intensity of all pixels with the same kinetic energy value is accumulated. Traversing the entire kinetic energy range, a relationship curve is plotted with photoelectron kinetic energy as the x-axis and the integrated photoelectron intensity as the y-axis to obtain the ultraviolet photoelectron spectrum of the sample under test.

[0062] By using the energy calibration coefficient, the spatial position of each pixel (i.e., its position on the fluorescent screen) can be mapped to the corresponding photoelectron kinetic energy value, ensuring that each data point in the final energy spectrum corresponds to the actual photoelectron kinetic energy, thus avoiding errors caused by inaccurate mapping between spatial position and energy.

[0063] Step S12: Determine the Fermi edge region from the ultraviolet photoelectron spectrum of the sample to be tested, and extract the shape information of the Fermi edge region, wherein the Fermi edge region is a preset width range centered on the position of the Fermi edge.

[0064] The Fermi edge region is extracted from the ultraviolet photoelectron spectrum of the sample under test, and its shape information is obtained. The Fermi edge refers to the electron energy range near the Fermi level in the ultraviolet photoelectron spectrum; typically, at zero degrees Celsius, the Fermi level is at the highest point of the electron spectrum. The electron intensity information near the Fermi level can provide crucial information about the electronic structure and surface state of the sample under test. First, the Fermi edge region in the ultraviolet photoelectron spectrum is identified by determining its location. Next, a preset width range centered on the Fermi edge is selected as the Fermi edge region. Finally, the photoelectron intensity within this Fermi edge region is processed and mapped to a standard intensity range to extract the shape information of the region.

[0065] In one optional implementation, step S12 specifically includes steps S121 to S123: Step S121: Calculate the first derivative of the ultraviolet photoelectron spectrum and determine the position of the Fermi edge based on the first derivative.

[0066] The Fermi edge in ultraviolet (UV) photoelectron spectroscopy typically appears as a region of abrupt or rapid change. To accurately pinpoint this change, the location of the abrupt change can be identified by calculating the first derivative of the UV photoelectron spectrum. The first derivative reflects the rate of intensity change in the UV photoelectron spectrum and can clearly indicate the region of fastest intensity change, corresponding to the location of the Fermi edge.

[0067] Step S122: Using the position of the Fermi edge as the center, select an interval of the preset width range as the Fermi edge region.

[0068] A preset width range centered on the Fermi edge is selected as the Fermi edge region. The selection of the preset width range is based on experience or theoretical models, aiming to ensure that all important information near the Fermi edge is covered. For example, a region of ±1 eV centered on the Fermi edge can be selected. The Fermi edge region covers the electron energy range near the Fermi edge location, effectively capturing the characteristics of the Fermi edge and avoiding the omission of key information that may affect subsequent analysis.

[0069] Step S123: Based on the maximum and minimum values ​​of photoelectron intensity within the Fermi edge region, map the photoelectron intensity within the Fermi edge region to a standard intensity range to obtain the shape information.

[0070] Maximum photoelectron intensity ( The photoelectron intensity is the highest intensity value among all data points within the Fermi edge region, corresponding to the saturation signal of electron-occupied states below the Fermi level. The minimum photoelectron intensity ( The value is the lowest intensity among all data points within the Fermi edge region, corresponding to the background noise of unoccupied states above the Fermi level. The standard intensity range is a normalized intensity range fixed at [0,1], where 0 represents... ,1 represents The normalized spectral curve retains key features such as the slope and broadening of the Fermi edge, while eliminating intensity deviations introduced by instrument gain or light source fluctuations.

[0071] Searching for the global extremum within the Fermi edge region:

[0072]

[0073] The position of the first derivative peak in step S121 is determined (e.g., for gold samples). ).

[0074] For each energy point within the region E original strength Normalization is performed to obtain shape information. That is, the normalized ultraviolet photoelectron spectrum:

[0075] This invention characterizes the formation mechanism of the Fermi edge in ultraviolet photoelectron spectroscopy by establishing a physics-driven convolution model. The convolution model simultaneously considers the combined distribution of the intrinsic electronic state density of the material and the thermal broadening effect (i.e., a state density-weighted Fermi-Dirac distribution) and the broadening introduced by instrument resolution (Gaussian function). Its convolution result matches the measured ultraviolet photoelectron spectrum. Specifically, considering the thermal broadening effect of the state density distribution reflects the influence of the material's electronic state density and Fermi-Dirac statistical distribution on the photoelectron intensity distribution at finite temperatures. In metals, the probability of electron occupancy follows a Fermi-Dirac distribution; combined with the state density distribution, the Fermi edge exhibits a smooth transition rather than an ideal step. Instrument broadening (Gaussian function) originates from hardware limitations such as the monochromaticity of the light source and the resolution of the energy analyzer, manifesting as a Gaussian broadening of the true energy level. The convolution model decouples these two factors, avoiding the misinterpretation of thermal broadening as an instrument performance defect by traditional methods, such as the 20%–80% intensity method.

[0076] Figure 2 This is a schematic diagram illustrating the fitting effect of ultraviolet photoelectron spectroscopy in Fermi level determination in one embodiment of the present invention. Figure 2 Plotting photoelectron binding energy (eV) on the horizontal axis and normalized photoelectron intensity on the vertical axis, this figure displays the ultraviolet photoelectron spectrum of a standard gold (Au) sample (i.e., the standard sample) as the test sample under specific testing conditions. Solid circles in the figure represent the measured photoelectron energy distribution data (experimental data). These experimental data points cover the Fermi level (…). The region near 300K temperature and 7.7eV ultraviolet light represents the area under these conditions. hν The photoelectron kinetic energy distribution on the surface of a gold sample under excitation at 7.7 eV. The experimental data show the intensity from the high-energy region ( The value of ) is approximately constant, and it rapidly decreases to the low-energy region ( The near-zero value of the electron state density (Fermi-Dirac distribution) forms a typical steep drop at the Fermi edge. However, the measured point is not an ideal step, but exhibits a smooth transition, reflecting the combined effects of electron thermal motion (density-weighted Fermi-Dirac distribution) and instrument noise. The theoretical fitting curve represents the output of the convolution model, i.e., the convolution result of the Fermi-Dirac distribution function (density-weighted) considering the density of states distribution and the Gaussian function. This curve closely fits the experimental data, with only a slight deviation near the Fermi edge (the residual is extremely small). The thermal broadening part considering the density of states distribution describes the influence of the combined effect of the material's electronic state density and Fermi-Dirac statistical distribution on the photoelectron intensity distribution at 300K (T=300K). The smooth slope (rather than a vertical drop) reflects the broadening caused by the combined effect of temperature and density of states. The Gaussian fitting portion is used to simulate the instrument resolution broadening.

[0077] In one alternative implementation, the convolutional model is constructed according to the following steps: Step S31: Construct the Fermi-Dirac distribution function, which is used to describe the thermal broadening effect of photoelectrons in the material of the sample to be tested.

[0078] The Fermi-Dirac distribution function is constructed to describe the thermal broadening effect of photoelectrons in the material of the sample under test. It reflects the occupation of electrons under thermal equilibrium, especially at low temperatures, where the energy distribution of electrons follows this distribution law.

[0079] A Fermi-Dirac distribution function considering the density of states distribution is constructed. This function describes the combined effect of the electronic density of states distribution and thermal broadening effect on the photoelectron intensity distribution in the sample material. In actual photoelectron spectra, the photoelectron intensity is proportional to the product of the material's electronic density of states and the Fermi-Dirac occupancy probability; therefore, both must be combined.

[0080] Specifically, the thermal broadening effect is caused by the material's temperature. Temperature changes lead to a smooth change in the electron occupancy probability near the Fermi level, while the density of states distribution determines the number of electron states that can be occupied at each energy level. The combined effect of these two factors results in a smooth transition at the Fermi edge rather than an ideal step. The Fermi-Dirac distribution function, which considers the density of states distribution, can accurately describe the influence of thermal effects on the photoelectron spectrum based on the temperature information of the sample and the material's density of states characteristics.

[0081] In the idealized simplified model, if the gradual variation of the density of states with energy is ignored, the Fermi-Dirac distribution function can be used as an approximation: The Fermi-Dirac distribution function has the following functional form:

[0082] In the formula, It is the kinetic energy of electrons; It is the Fermi level; Boltzmann constant ( eV / K); The experimental temperature can be obtained by monitoring the sample stage in real time using a temperature sensor.

[0083] Step S32: Based on the spectral broadening corresponding to the pre-measured instrument response, a Gaussian function is constructed. The Gaussian function is used to describe the energy broadening effect introduced by the resolution of the instrument.

[0084] Based on the spectral broadening corresponding to the instrument response, a Gaussian function is constructed to describe the energy broadening effect caused by instrument resolution. In actual measurements, due to the inherent resolution limitations of the instrument, the measured photoelectron spectrum exhibits a certain degree of broadening, which typically displays a Gaussian shape. To simulate this effect, a Gaussian function is constructed; the Gaussian distribution is a mathematical function widely used to describe random and measurement errors. The standard form of the Gaussian function describes the distribution of photoelectron energy, and its width is proportional to the instrument resolution. By measuring the actual instrument response (using known spectral lines), the broadening information introduced by the instrument can be obtained, and this effect can be quantified using the Gaussian function.

[0085] The Gaussian function has the following functional form:

[0086] In the formula, Let be the standard deviation of the Gaussian function to be fitted.

[0087] Step S33: Perform a convolution operation between the Fermi-Dirac distribution function and the Gaussian function to obtain the convolution model, which includes the Fermi level parameters to be fitted.

[0088] By convolving the previously constructed Fermi-Dirac distribution function with a Gaussian function, a comprehensive convolution model is obtained. Convolution is a common mathematical method for combining two signals or functions. This operation integrates thermal broadening and instrument broadening effects, resulting in a comprehensive energy spectrum broadening model. Through convolution, the effects of thermal and instrument effects are combined, producing a new function that represents the final broadening of the photoelectron spectrum under these two effects. The convolution model accurately describes the photoelectron spectrum of the sample under test, taking into account the combined effects of temperature and instrument resolution.

[0089] Considering the Fermi-Dirac distribution function of the density of states. With Gaussian expansion Convolution is performed to obtain the convolution model. :

[0090] in, This reflects the combined contribution of the material's intrinsic electronic structure (density of states) and thermal occupancy probability.

[0091] Step S13: Based on a pre-constructed convolution model using the Gaussian broadening parameters of the UV photoelectron spectrometer to be tested as fitting parameters, the shape information of the Fermi edge region is fitted using the particle swarm optimization algorithm, and the energy resolution of the UV photoelectron spectrometer is determined according to the fitting results; the convolution model is used to describe thermal broadening and instrument broadening.

[0092] By constructing a convolutional model of the Fermi-Dirac distribution and Gaussian broadening, and combining it with particle swarm optimization (PSO) to fit the spectral data of the Fermi edge region, the instrument's energy resolution parameters are finally extracted. It can be understood that this invention uses a standard sample with a known Fermi level as an energy scale to inversely calibrate the instrument's own broadening characteristics. Specifically, a convolutional model is constructed that simultaneously describes electron thermal broadening and instrument broadening. In the convolutional model, the known Fermi level value of the standard sample (i.e., the standard gold sample mentioned earlier) is used as a constant input to fix the influence of the material itself on the shape of the energy spectrum; while the energy broadening of the instrument to be measured is characterized by the broadening parameter in the Gaussian function and serves as the only unknown parameter to be fitted in the model. By obtaining the ultraviolet photoelectron spectrum of this standard sample, extracting the shape information of the Fermi edge region, and using PSO to fit the convolutional model, the Gaussian broadening parameter introduced by the instrument itself can be accurately retrieved from the measured spectral lines, thereby calculating the instrument's energy resolution. By using known materials as a reference, the effects of thermal broadening and instrument broadening can be separated, enabling objective and automated measurement of the energy resolution of ultraviolet photoelectron spectrometers.

[0093] In an optional implementation, step S13 specifically includes steps S131 to S133: Step S131: Using the particle swarm optimization algorithm, with the goal of minimizing the sum of squared residuals between the fitted function and the shape information, the Gaussian broadening parameter in the convolution model is iteratively adjusted.

[0094] Figure 3 This is a schematic diagram of the fitting and iterative process of the particle swarm optimization algorithm in one embodiment of the present invention. Please refer to [link / reference]. Figure 3 To minimize the output of the convolutional model With shape information The residual sum of squares (RSS) is the objective function, with respect to the Gaussian broadening parameter. Perform global optimization. The particle swarm optimization algorithm is configured as follows: Each particle position A candidate solution representing a Gaussian broadening parameter The objective function is:

[0095] In the formula, The number of data points in the Fermi edge region. For convolutional models in terms of energy E k The theoretical strength is derived.

[0096] The particle update rules are as follows:

[0097] In the formula, For particle velocity (search direction); This represents the optimal position in the particle's history. It is the optimal position for the entire group. Inertial weights; For learning factors; A random number that is uniformly distributed in the interval 0 and 1.

[0098] In an optional implementation, step S131 specifically includes steps S1311 to S1312: Step S1311: Set up a particle swarm containing multiple particles; wherein each particle represents a candidate value of a Gaussian broadening parameter, the position of each particle represents the current solution of the corresponding Gaussian broadening parameter, and the velocity of each particle represents the search direction of the corresponding Gaussian broadening parameter.

[0099] First, a particle swarm is constructed, consisting of multiple particles, each representing a candidate value for a Gaussian broadening parameter. The particle swarm is the basic unit of optimization using the Particle Swarm Optimization (PSO) algorithm. The position of each particle represents the current solution for a specific Gaussian broadening parameter; the particle's current position reflects the current estimate of that parameter. This position is actually a point in the parameter space. In this algorithm, the position of each particle corresponds to a specific value of the Gaussian broadening parameter. In other words, the particle's current solution is the value of the Gaussian broadening parameter that the particle is currently guessing.

[0100] In addition, each particle has a corresponding velocity, representing its movement rate and step size along that direction during the search. The particle's velocity affects the direction and step size of its search process, that is, how the particle adjusts its position to find a better Gaussian broadening parameter. By searching together with all the particles in the swarm, the optimal Gaussian broadening parameter value can be found more comprehensively and quickly, thus obtaining the best energy resolution of the ultraviolet photoelectron spectrometer.

[0101] Step S1312: In each iteration, the position of each particle in the particle swarm is updated according to the current residual sum of squares until the minimum value of the residual sum of squares reaches a preset convergence threshold.

[0102] The particle swarm optimization algorithm performs iterative updates. In each iteration, the particle swarm updates the position of each particle based on the current sum of squared residuals (i.e., the difference between the fitted function and the shape information). The sum of squared residuals is used to measure the goodness of fit; the smaller the residuals, the better the fit.

[0103] The particle swarm optimization technique can progressively approximate the optimal solution of the Gaussian broadening parameters, ensuring that the energy resolution of the ultraviolet photoelectron spectrometer reaches its highest accuracy. The automation of this process eliminates the need for human intervention, significantly improving the efficiency and accuracy of data fitting.

[0104] In one optional implementation, step S1312 specifically includes: Step S13121: In each iteration, the Gaussian broadening parameter corresponding to the minimum residual sum of squares reached by each particle in the particle swarm is taken as the historical best position of that particle.

[0105] In each iteration, the historical best position of each particle in the particle swarm is updated. Specifically, the minimum sum of squared residuals reached by the current particle during the search process is recorded, i.e., the best fit, and the corresponding Gaussian broadening parameter value is saved. If the sum of squared residuals in the current iteration is lower than the historical minimum, the current position of the particle is updated to its new historical best position; otherwise, the original historical best position is retained.

[0106] Each particle learns and memorizes the local optimal solutions it finds during the search process, thus avoiding losing the high-quality parameter regions it has discovered.

[0107] Step S13122: Select the Gaussian broadening parameter with the smallest sum of squared residuals from the historical best positions of all particles in the particle swarm as the current global best position of the particle swarm.

[0108] The algorithm iterates through the historical best positions of all particles in the swarm (i.e., the best parameters each particle has ever found). From these historical best positions, it selects the one with the smallest sum of squared residuals and uses its corresponding Gaussian broadening parameters as the global best position for the entire swarm. By sharing swarm experience, the algorithm guides particles towards the current best solution in the entire search space, accelerating convergence.

[0109] Step S13123: Calculate the direction and step size of the particle's movement based on the current position, historical best position, and global best position of each particle, as well as the preset inertia weight and random perturbation factor.

[0110] The movement direction and step size of each particle are dynamically calculated based on three aspects of information: the particle's current position, which is the current candidate value of the Gaussian broadening parameter; the particle's historical best position, which is the individual experience; and the global best position of the particle swarm, which is the group experience.

[0111] An inertia weight and a random perturbation factor are introduced to control the movement behavior of each particle. The inertia weight controls the strength of the particle's original movement trend, avoiding violent oscillations; the random perturbation factor introduces randomness into the particle's movement direction, preventing it from getting trapped in local optima. In other words, the particle's movement direction is guided by both individual and collective experience, while retaining some of the original motion inertia. The random perturbation ensures that the search process has the ability to explore unknown regions.

[0112] Step S13124: Based on the direction and step size of each particle's movement, update the particle's velocity, and update the particle's position according to the updated velocity.

[0113] For each particle in the particle swarm, a new velocity vector is generated based on the movement direction and step size calculated in step S1313. The particle position is updated based on the new velocity vector, i.e., new candidate values ​​for the Gaussian broadening parameter are generated. If the particle moves toward a better solution, its search step size in that direction is increased; if the particle gets stuck in a suboptimal region, a random perturbation will cause it to jump out of its current position.

[0114] Step S13125: Repeat the above steps until the sum of squared residuals converges to a preset threshold.

[0115] Repeat steps S13121 to S13124 until either of the following conditions is met: the change in the sum of squared residuals is less than a preset threshold, or the maximum number of iterations is reached. Use the Gaussian broadening parameter corresponding to the global optimum position as the final solution for energy resolution calculation.

[0116] Step S132: When the residual converges to a preset threshold, a fitting result including the optimal Gaussian broadening parameter is obtained.

[0117] As described in step S13125, when the sum of squared residuals converges to a preset threshold, the Gaussian broadening parameter corresponding to the global optimal position is taken as the final solution.

[0118] Step S133: Calculate the energy resolution of the ultraviolet photoelectron spectrometer based on the optimal Gaussian broadening parameter in the fitting results.

[0119] Optimal Gaussian broadening parameters Converted to full width at half maximum (FWHM), it serves as a quantitative indicator of the instrument's energy resolution.

[0120] Energy resolution (unit: eV) is the smallest energy difference that an instrument can distinguish, and the coefficient is... The conversion relationship between the full width at half maximum (FWHM) and standard deviation of a Gaussian distribution.

[0121] Figure 4 This is a structural block diagram of a continuously adjustable ultraviolet photoelectron spectrometer energy resolution measurement device according to an embodiment of the present invention, applied to perform the steps of a continuously adjustable ultraviolet photoelectron spectrometer energy resolution measurement method as described above; as Figure 4 As shown, the device includes: The first construction module 41 is used to irradiate the sample to be tested with a photon beam of the target wavelength, and construct the ultraviolet photoelectron spectrum of the sample to be tested based on the distribution number of photoelectrons with different kinetic energies excited by the sample to be tested. The photon beam belongs to the vacuum ultraviolet band, and the horizontal axis of the ultraviolet photoelectron spectrum is the photoelectron kinetic energy, and the vertical axis is the photoelectron intensity that characterizes the distribution number of photoelectrons. The second construction module 42 is used to determine the Fermi edge region from the ultraviolet photoelectron spectrum of the sample to be tested, and extract the shape information of the Fermi edge region, wherein the Fermi edge region is a preset width range centered on the position of the Fermi edge. The fitting module 43 is used to fit the shape information of the Fermi edge region based on a pre-constructed convolution model with the Gaussian broadening parameters of the ultraviolet photoelectron spectrometer to be tested as the fitting parameters, and to determine the energy resolution of the ultraviolet photoelectron spectrometer based on the fitting results; the convolution model is used to describe thermal broadening and instrument broadening.

[0122] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, electronic devices, and media. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of a computer program product embodied on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0123] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods and apparatus according to embodiments of the present invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0124] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the present invention.

[0125] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element. The above provides a detailed description of the energy resolution measurement method and apparatus for an ultraviolet photoelectron spectrometer provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention; at the same time, for those skilled in the art, based on the ideas of the present invention, there will be changes in specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for testing the energy resolution of a continuously adjustable ultraviolet photoelectron spectrometer, characterized in that, include: A Fermi-Dirac distribution function considering the density of states distribution is constructed. This function describes the combined effect of the electronic density of states distribution and thermal broadening effect on the photoelectron intensity distribution in the sample material. The Fermi level parameter in the Fermi-Dirac distribution function considering the density of states distribution is a pre-calibrated known value. The probability of electron occupancy in metals follows a Fermi-Dirac distribution. After combining the density of states distribution, the Fermi edge exhibits a smooth transition rather than an ideal step. The Fermi-Dirac distribution function considering the density of states distribution can accurately describe the influence of thermal effects on the photoelectron spectrum based on the temperature information and material density of states characteristics of the sample. A Gaussian function is constructed to describe the energy broadening effect introduced by the energy resolution of the ultraviolet photoelectron spectrometer; the Gaussian broadening parameters contained in the Gaussian function are used as unknown parameters to be fitted. The Fermi-Dirac distribution function and the Gaussian function are convolved to obtain a convolution model, which includes the Gaussian broadening parameter to be fitted. The convolution model simultaneously considers the joint distribution of the intrinsic electronic state density distribution and the thermal broadening effect of the material with the Gaussian function, and its convolution result matches the measured ultraviolet photoelectron spectrum. The measured point is not an ideal step, but exhibits a smooth transition, reflecting the combined influence of the state density-weighted Fermi-Dirac distribution and instrument noise. The output of the matching convolution model is the theoretical fitting curve, that is, the convolution result of the Fermi-Dirac distribution function considering the state density distribution and the Gaussian function. The sample to be tested is irradiated with a photon beam of the target wavelength. The ultraviolet photoelectron spectrum of the sample to be tested is constructed based on the distribution of the number of photoelectrons with different kinetic energies excited by the sample. The photon beam belongs to the vacuum ultraviolet band. The horizontal axis of the ultraviolet photoelectron spectrum is the photoelectron kinetic energy, and the vertical axis is the photoelectron intensity, which represents the distribution of the number of photoelectrons. The Fermi edge region is determined from the ultraviolet photoelectron spectrum of the sample to be tested, and the shape information of the Fermi edge region is extracted. The Fermi edge region is a preset width range centered on the position of the Fermi edge. Based on a pre-constructed convolution model using the Gaussian broadening parameters of the UV photoelectron spectrometer to be tested as fitting parameters, the shape information of the Fermi edge region is fitted using the particle swarm optimization algorithm, and the energy resolution of the UV photoelectron spectrometer is determined based on the fitting results; the convolution model is used to describe thermal broadening and instrument broadening. Specifically, the Fermi edge region is determined from the ultraviolet photoelectron spectrum of the sample to be tested, and the shape information of the Fermi edge region is extracted, including: Calculate the first derivative of the ultraviolet photoelectron spectrum, and determine the position of the Fermi edge based on the first derivative; Centered on the position of the Fermi edge, a range of the preset width is selected as the Fermi edge region; Based on the maximum and minimum values ​​of photoelectron intensity within the Fermi edge region, the photoelectron intensity within the Fermi edge region is mapped to a standard intensity range to obtain the shape information; The process involves irradiating the sample under test with a photon beam of the target wavelength, and constructing the ultraviolet photoelectron spectrum of the sample under test based on the distribution and number of photoelectrons with different kinetic energies excited by the sample. This includes: A reference sample is irradiated with an electron beam of target energy emitted by an electron gun to obtain photoelectrons excited in the reference sample; Photoelectrons with different kinetic energies were screened out using a hemispherical energy analyzer; The signals of photoelectrons with different kinetic energies are amplified by a microchannel plate, and the corresponding luminescent images are obtained on a fluorescent screen. The luminescent image is captured using an industrial camera; The energy calibration coefficient is calculated based on the correspondence between the pixel position and photoelectron kinetic energy of each pixel in the light emission imaging. Based on the energy calibration coefficient, the pixel position of each pixel in the light emission imaging is converted into the corresponding photoelectron kinetic energy; The photoelectron kinetic energies corresponding to the pixel positions of each pixel in the light emission imaging are superimposed, and the data points with the same photoelectron kinetic energy are integrated to obtain the ultraviolet photoelectron spectrum of the sample under test.

2. The method according to claim 1, characterized in that, Based on a pre-constructed convolution model using the Gaussian broadening parameters of the UV photoelectron spectrometer to be measured as the fitting parameters, the shape information of the Fermi edge region is fitted using a particle swarm optimization algorithm, and the energy resolution of the UV photoelectron spectrometer is determined based on the fitting results, including: Using the particle swarm optimization algorithm, with the goal of minimizing the sum of squared residuals between the fitted function and the shape information, the Gaussian broadening parameter in the convolution model is iteratively adjusted. When the residual converges to a preset threshold, a fitting result including the optimal Gaussian broadening parameter is obtained; The energy resolution of the ultraviolet photoelectron spectrometer is calculated based on the optimal Gaussian broadening parameter in the fitting results.

3. The method according to claim 2, characterized in that, Using the particle swarm optimization algorithm, with the objective of minimizing the sum of squared residuals between the fitted function and the shape information, the Gaussian broadening parameter in the convolutional model is iteratively adjusted, including: Set up a particle swarm containing multiple particles; where each particle represents a candidate value of a Gaussian broadening parameter, the position of each particle represents the current solution of the corresponding Gaussian broadening parameter, and the velocity of each particle represents the search direction of the corresponding Gaussian broadening parameter. In each iteration, the position of each particle in the particle swarm is updated based on the current residual sum of squares until the minimum value of the residual sum of squares reaches a preset convergence threshold.

4. The method according to claim 3, characterized in that, In each iteration, the position of each particle in the particle swarm is updated based on the current residual sum of squares until the minimum residual sum of squares reaches a preset convergence threshold, including: In each iteration, the Gaussian broadening parameter corresponding to the minimum residual sum of squares reached by each particle in the particle swarm is taken as the historical best position of that particle. From the historical best positions of all particles in the particle swarm, select the Gaussian broadening parameter with the smallest sum of squared residuals as the current global best position of the particle swarm. Based on each particle's current position, historical best position, and global best position, as well as preset inertia weights and random perturbation factors, calculate the particle's movement direction and step size. Based on the direction and step size of each particle's movement, update the particle's velocity, and then update the particle's position based on the updated velocity; Repeat the above steps until the sum of squared residuals converges to a preset threshold.

5. The method according to claim 1, characterized in that, The sample to be tested is irradiated with a photon beam of the target wavelength. Based on the distribution of photoelectrons with different kinetic energies excited by the sample, the ultraviolet photoelectron spectrum of the sample is constructed, including: The sample to be tested is irradiated with a photon beam of the target wavelength to obtain photoelectrons with different kinetic energies excited by the sample to be tested. Using a hemispherical energy analyzer, photoelectrons with different kinetic energies are screened from the photoelectrons with different kinetic energies excited by the sample under test. The signals of photoelectrons with different kinetic energies are amplified by a microchannel plate, and the corresponding light emission images are obtained on a fluorescent screen. The light emission images include pixels at multiple different positions. The luminescent image is captured using an industrial camera; All the light-emitting images captured by the industrial camera are superimposed, and the pixels with the same electron kinetic energy are integrated to obtain the ultraviolet photoelectron spectrum of the sample under test.

6. The method according to claim 1, characterized in that, Irradiating the sample under test with a photon beam of the target wavelength, including: Metal films with a purity higher than the preset value were used as the test samples; The surface of the sample to be tested is etched using an argon ion beam to remove the oxide layer and contaminants from the surface of the sample to be tested. The ultraviolet spectrum of the continuous band generated by the deuterium lamp is processed using a vacuum ultraviolet monochromator to obtain a photon beam of the target wavelength. In a vacuum sample chamber, the sample to be tested is irradiated with a photon beam of the target wavelength.

7. A continuously adjustable ultraviolet photoelectron spectrometer energy resolution measurement device, characterized in that, Applied to perform the steps as described in any one of claims 1-6; comprising: The first construction module is used to irradiate the sample to be tested with a photon beam of the target wavelength, and construct the ultraviolet photoelectron spectrum of the sample to be tested based on the distribution of the number of photoelectrons with different kinetic energies excited by the sample to be tested. The photon beam belongs to the vacuum ultraviolet band, and the horizontal axis of the ultraviolet photoelectron spectrum is the photoelectron kinetic energy, and the vertical axis is the photoelectron intensity that characterizes the distribution of the number of photoelectrons. The second construction module is used to determine the Fermi edge region from the ultraviolet photoelectron spectrum of the sample to be tested, and extract the shape information of the Fermi edge region, wherein the Fermi edge region is a preset width range centered on the position of the Fermi edge. The fitting module is used to fit the shape information of the Fermi edge region based on a pre-constructed convolution model with the Gaussian broadening parameters of the UV photoelectron spectrometer to be tested as the fitting parameters, and to determine the energy resolution of the UV photoelectron spectrometer based on the fitting results; the convolution model is used to describe thermal broadening and instrument broadening. The first building module is also used for: A Fermi-Dirac distribution function considering the density of states distribution is constructed. This function describes the combined effect of the electronic density of states distribution and thermal broadening effect on the photoelectron intensity distribution in the sample material. The Fermi level parameter in the Fermi-Dirac distribution function considering the density of states distribution is a pre-calibrated known value. The probability of electron occupancy in metals follows a Fermi-Dirac distribution. After combining the density of states distribution, the Fermi edge exhibits a smooth transition rather than an ideal step. The Fermi-Dirac distribution function considering the density of states distribution can accurately describe the influence of thermal effects on the photoelectron spectrum based on the temperature information and material density of states characteristics of the sample. A Gaussian function is constructed to describe the energy broadening effect introduced by the energy resolution of the ultraviolet photoelectron spectrometer; the Gaussian broadening parameters contained in the Gaussian function are used as unknown parameters to be fitted. The convolution operation is performed between the Fermi-Dirac distribution function and the Gaussian function to obtain the convolution model, which includes the Gaussian broadening parameter to be fitted. The convolution model simultaneously considers the joint distribution of the intrinsic electronic state density distribution and the thermal broadening effect of the material with the Gaussian function, and its convolution result matches the measured ultraviolet photoelectron spectrum. The measured point is not an ideal step, but exhibits a smooth transition, reflecting the combined influence of the state density-weighted Fermi-Dirac distribution and instrument noise. The output of the matching convolution model is the theoretical fitting curve, that is, the convolution result of the Fermi-Dirac distribution function considering the state density distribution and the Gaussian function. A reference sample is irradiated with an electron beam of target energy emitted by an electron gun to obtain photoelectrons excited in the reference sample; Photoelectrons with different kinetic energies were screened out using a hemispherical energy analyzer; The signals of photoelectrons with different kinetic energies are amplified by a microchannel plate, and the corresponding luminescent images are obtained on a fluorescent screen. The luminescent image is captured using an industrial camera; The energy calibration coefficient is calculated based on the correspondence between the pixel position and photoelectron kinetic energy of each pixel in the light emission imaging. Based on the energy calibration coefficient, the pixel position of each pixel in the light emission imaging is converted into the corresponding photoelectron kinetic energy; The photoelectron kinetic energies corresponding to the pixel positions of each pixel in the light emission imaging are superimposed, and the data points with the same photoelectron kinetic energy are integrated to obtain the ultraviolet photoelectron spectrum of the sample under test. The second building module is also used for: Calculate the first derivative of the ultraviolet photoelectron spectrum, and determine the position of the Fermi edge based on the first derivative; Centered on the position of the Fermi edge, a range of the preset width is selected as the Fermi edge region; Based on the maximum and minimum values ​​of photoelectron intensity within the Fermi edge region, the photoelectron intensity within the Fermi edge region is mapped to a standard intensity range to obtain the shape information.

Images