X-ray photoelectron spectroscopy processing methods and X-ray photoelectron spectroscopy systems

Abstract

This disclosure relates to the field of X-ray photoelectron spectroscopy detection, and discloses a method for processing X-ray photoelectron spectroscopy and an X-ray photoelectron spectroscopy system. The method for processing X-ray photoelectron spectroscopy includes: receiving X-ray photoelectron spectral data; and filtering the X-ray photoelectron spectral data through a finite impulse response (FIR) filter to obtain filtered X-ray photoelectron spectral data.

Description

Technical Field

[0001] This disclosure relates to the field of X-ray photoelectron spectroscopy detection, and particularly to a method for processing X-ray photoelectron spectra and an X-ray photoelectron spectroscopy system. Background Technology

[0002] X-ray photoelectron spectrometers are widely used in industrial measurement. Even with identical instrument parameter settings, different measurements of the same sample will yield different photoelectron spectra. The main reasons for this difference are twofold: first, the number of photoelectrons received by the detector follows a Poisson distribution; and second, there are uncertainties in the output of hardware such as the power supply. To improve the operational stability of X-ray photoelectron spectrometers, two common measures are taken: one is to increase the intensity of the X-rays, but this will reduce the instrument's lifespan; the other is to extend the spectral acquisition time, but this will reduce operational efficiency. Summary of the Invention

[0003] This disclosure provides a method for processing X-ray photoelectron spectroscopy, including:

[0004] Receive X-ray photoelectron spectroscopy data; and

[0005] X-ray photoelectron spectroscopy data are filtered using a finite impulse response (FIR) filter to obtain filtered X-ray photoelectron spectroscopy data.

[0006] This disclosure also provides an X-ray photoelectron spectroscopy system, comprising:

[0007] X-ray photoelectron spectrometer, used to acquire X-ray photoelectron spectroscopy data; and

[0008] The processor is communicatively connected to an X-ray photoelectron spectrometer and is configured to execute an X-ray photoelectron spectroscopy processing method according to any embodiment of the present disclosure to filter X-ray photoelectron spectroscopy data acquired by the X-ray photoelectron spectrometer to obtain filtered X-ray photoelectron spectroscopy data.

[0009] This disclosure also provides a computer device, including:

[0010] Memory for storing at least one instruction; and

[0011] A processor, coupled to a memory, is configured to execute at least one instruction to perform a processing method for X-ray photoelectron spectroscopy according to any embodiment of the present disclosure.

[0012] This disclosure also provides a computer-readable storage medium for storing at least one instruction, which, when executed by a computer device, causes the computer to perform a processing method for X-ray photoelectron spectroscopy according to any embodiment of this disclosure. Attached Figure Description

[0013] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only one embodiment of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 An example flowchart of a method for processing X-ray photoelectron spectroscopy according to some embodiments of the present disclosure is shown;

[0015] Figure 2 A logic block diagram of a design filter according to some embodiments of this disclosure is shown;

[0016] Figure 3A The X-ray photoelectron spectroscopy data before filtering obtained from SiON measurement are shown;

[0017] Figure 3B The present disclosure illustrates X-ray photoelectron spectroscopy processing methods and systems employing some embodiments thereof. Figure 3A The X-ray photoelectron spectroscopy data shown is the filtered X-ray photoelectron spectroscopy data.

[0018] Figure 4 A schematic diagram of an X-ray photoelectron spectroscopy system according to some embodiments of the present disclosure is shown. Detailed Implementation

[0019] Some embodiments of this disclosure will now be described with reference to the accompanying drawings. Obviously, the described embodiments are merely exemplary embodiments of this disclosure, and not all embodiments.

[0020] In the description of this disclosure, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," "top," "bottom," "horizontal," and "longitudinal," etc., indicating the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, are only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance. In the description of this disclosure, it should be noted that unless otherwise expressly specified and limited, the terms "installation," "connection," "linking," and "coupling" should be interpreted broadly, for example, they can refer to fixed connections or detachable connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two elements. In the description of this disclosure, "distal" or "farside" refers to one end or side that extends into a vacuum environment (e.g., a vacuum chamber), while "proximal" or "proximal" is the end or side opposite to "distal" or "farside" (e.g., one end or side away from the vacuum chamber, or one end or side within the vacuum chamber that is closer to the vacuum chamber wall, etc.). Alternatively, the end or side closer to the drive device is called the proximal or proximal end, and the end or side farther from the drive device is called the distal or distal end. Those skilled in the art will understand the specific meaning of the above terms in this disclosure according to the specific circumstances.

[0021] Figure 1 An example flowchart of an X-ray photoelectron spectroscopy (XPS) processing method 100 according to some embodiments of the present disclosure is shown. The X-ray photoelectron spectroscopy processing method 100 may be implemented or executed at least partially by hardware, software, or firmware. In some embodiments, the X-ray photoelectron spectroscopy processing method 100 may be at least partially implemented by an X-ray photoelectron spectroscopy system (e.g., Figure 4 The X-ray photoelectron spectroscopy system 300 shown is used for execution. In some embodiments, the X-ray photoelectron spectroscopy processing method 100 can be implemented as computer-readable instructions. These instructions can be executed by a general-purpose processor or a special-purpose processor (e.g., [processor name missing]). Figure 4 The processor 320 of the X-ray photoelectron spectroscopy system 300 shown reads and executes these instructions. In some embodiments, these instructions may be stored on a computer-readable medium.

[0022] like Figure 1As shown, in some embodiments of this disclosure, the X-ray photoelectron spectroscopy processing method may include, in step 110, receiving X-ray photoelectron spectroscopy data. For example, an X-ray photoelectron spectrometer can measure the sample and acquire X-ray photoelectron spectroscopy data. The X-ray photoelectron spectroscopy data acquired by the X-ray photoelectron spectrometer can be received. Those skilled in the art will understand that the sample may include various suitable embodiments, such as silicon oxynitride (SiON) samples, aluminum oxide (Al2O3) samples, titanium nitride (TiN) samples, etc.

[0023] like Figure 1 As shown, in some embodiments of this disclosure, the X-ray photoelectron spectroscopy processing method may include, in step 120, filtering the X-ray photoelectron spectroscopy data through a finite impulse response (FIR) filter to obtain filtered X-ray photoelectron spectroscopy data (step 130).

[0024] In some embodiments of this disclosure, filtering X-ray photoelectron spectroscopy data using an FIR filter can balance the measurement efficiency and stability of X-ray photoelectron spectroscopy, improving the operational stability of the X-ray photoelectron spectrometer without affecting its lifespan or extending the spectral acquisition time.

[0025] In some embodiments of this disclosure, the FIR filter may include N filter coefficients, where N is an integer greater than 1. Filtering X-ray photoelectron spectroscopy data using the FIR filter (step 120) may include: performing a convolution operation on the N filter coefficients and the X-ray photoelectron spectroscopy data to obtain filtered X-ray photoelectron spectroscopy data.

[0026] In some embodiments of this disclosure, the X-ray photoelectron spectroscopy data may include M values, where M is an integer greater than N. Convolving the N filter coefficients with the X-ray photoelectron spectroscopy data may include: calculating the convolution of the N filter coefficients with the N consecutive values ​​preceding the k-th value in the X-ray photoelectron spectroscopy data to obtain the k-th filtered value, where k is between 0 and M-1. Those skilled in the art will understand that in this disclosure, "previous" refers to a range of values ​​including the current value. For example, in some embodiments of this disclosure, the k-th filtered value y(k) can be calculated using formula (1):

[0027] y(k)=h(0)x(k-0)+h(1)x(k-1)+h(2)x(k-2)…+h(N-1)x(k-(N-1)) (1)

[0029] In formula (1), h(i) represents the filter coefficient, x(ki) represents the received ki-th X-ray photoelectron spectrum value, and y(k) represents the k-th filtered X-ray photoelectron spectrum data value. In formula (1), 0≤i≤N-1, 0≤k≤M-1, and i and k are both integers.

[0030] For the M values ​​in the X-ray photoelectron spectroscopy data, convolution operations can be performed using formula (1) to obtain the corresponding filtered values. For example, y(0) is the 0th filtered value obtained by formula (1) (the first value in the filtered sequence); y(1) is the first filtered value obtained by formula (1), ..., y(M-1) is the (M-1)th filtered value obtained by formula (1). M filtered values ​​can be obtained by formula (1): y(0), y(1), ..., y(M-1). Those skilled in the art will understand that, for example, padding is required when calculating y(0).

[0031] In some embodiments of this disclosure, centralized filtering processing can be performed on X-ray photoelectron spectroscopy (XPS) data. For example, receiving XPS data may include receiving all values ​​from the XPS data. The XPS data may also be stored. Convolving N filter coefficients with the XPS data may include reading the XPS data and calculating the convolution of the N filter coefficients with the N consecutive values ​​preceding the k-th value in the XPS data to obtain the k-th filtered value. This centralized filtering processing can be performed in parallel, significantly reducing the overall filtering time.

[0032] In some embodiments of this disclosure, X-ray photoelectron spectroscopy (XPS) data can be filtered in real time. For example, receiving XPS data may include continuously receiving values ​​from the XPS data. Convolving N filter coefficients with the XPS data may include calculating in real time the convolution of the N filter coefficients with the N consecutive values ​​preceding the k-th value in the XPS data to obtain the k-th filtered value. Real-time filtering can be performed during the acquisition of XPS data, enabling rapid filtering of the XPS data for subsequent use.

[0033] In some embodiments, formula (1) can be used to centrally or in real-time calculate the convolution of N filter coefficients with the N consecutive values ​​preceding the k-th value in the continuously received X-ray photoelectron spectroscopy data, thereby obtaining the k-th filtered value and thus all filtered X-ray photoelectron spectroscopy data. Since the filtered value depends only on the values ​​in the continuously received X-ray photoelectron spectroscopy data at previous times, the processing of X-ray photoelectron spectroscopy data can be performed in real-time, facilitating subsequent data processing.

[0034] Figure 2 A logic block diagram 400 of a design filter according to some embodiments of the present disclosure is shown.

[0035] like Figure 2 As shown, in some embodiments of this disclosure, the N filter coefficients can be determined based on the cutoff frequency of the FIR filter and a predetermined order N (box 450).

[0036] In some embodiments of this disclosure, the N filter coefficients can be determined based on the cutoff frequency and sinc function of the FIR filter.

[0037] An inverse Fourier transform of the frequency response of an ideal filter can produce its time-domain impulse response. To accommodate computer processing, in some embodiments of this disclosure, the time-domain impulse response of the ideal filter is shifted to the right and truncated with the main lobe as the axis of symmetry and the shift distance as the radius, to obtain a finite-length time-domain impulse response with a positive index, as shown in formula (2):

[0038] h(i)=sin(2πf c i) / (πi) (2)

[0039] In formula (2), h(i) represents the filter coefficients, and f c The cutoff frequency is represented by 0 ≤ i ≤ N-1, where i is an integer and N represents the order of the filter. The order N can be preset or determined.

[0040] For example, when the predetermined order N of the filter is odd, the time-domain impulse response of finite length with a positive index is as shown in Equation (3):

[0041]

[0042] In formula (3), h(i) are filter coefficients, and f c It is the cutoff frequency, j = i - (N-1) / 2, 0 ≤ i ≤ N-1, and i is an integer.

[0043] In some embodiments, because the impulse response of the FIR filter in the X-ray photoelectron spectroscopy processing method is of finite length and there is no feedback, the data processing operation is more stable.

[0044] Because ringing and ripples appear in the frequency response of a filter, especially near the edges of the frequency band, the performance of a directly truncated filter differs from that of an ideal filter. To approximate the characteristics of an ideal filter—undistorted passband and full stopband rejection—in some embodiments, the truncated low-pass filter is modified using a Blackman window function. The Blackman window function is defined as shown in equation (4):

[0045] ω(i)=0.42-0.5cos(2πi / (N-1))+0.08cos(4πi / (N-1)) (4)

[0047] In formula (4), ω(i) represents the Blackman window function, N represents the filter length (0≤i≤N-1), and i is an integer. In this disclosure, the filter length is equal to the filter order, and both are represented by the letter N.

[0048] In some embodiments of this disclosure, the FIR filter is designed to have a linear phase response, and the phase delay is linear when different frequency components pass through the FIR filter, so signal distortion can be largely avoided.

[0049] like Figure 2 As shown, in some embodiments of this disclosure, the N filter coefficients are determined based on the cutoff frequency of the FIR filter and the product of the sinc function and the Blackman window function (box 460). For example, the optimized filter coefficients can be obtained by multiplying the finite-length time-domain impulse response with a positive index shown in Equation (2) with the Blackman window function shown in Equation (4), as shown in Equation (5):

[0050] h(i)=sin(2πf c i) / (πi)·[0.42-0.5cos(2πi / (N-1))+0.08cos(4πi / (N-1))] (5)

[0052] In formula (5), h(i) represents the optimized filter coefficients, and f c Where i is the cutoff frequency, N is the order of the filter, 0≤i≤N-1, and i is an integer.

[0053] In some embodiments of this disclosure, the cutoff frequency of the FIR filter is predetermined based on prior X-ray photoelectron spectroscopy data.

[0054] like Figure 2 As shown, in some embodiments of this disclosure, the cutoff frequency of the FIR filter is determined based on the difference between the relative standard deviation of the filtered X-ray photoelectron spectral area and the relative standard deviation of the X-ray photoelectron spectral area, as well as the simulated standard deviation (box 440).

[0055] In some embodiments, the cutoff frequency of the FIR filter can be determined based on the fact that the difference between the relative standard deviation of the filtered X-ray photoelectron spectral area and the relative standard deviation of the X-ray photoelectron spectral area before filtering is approximately equal to the simulated standard deviation. For example, the cutoff frequency of the FIR filter can be determined based on the fact that the difference between the relative standard deviation of the filtered X-ray photoelectron spectral area and the relative standard deviation of the X-ray photoelectron spectral area before filtering, relative to the simulated standard deviation, is within an acceptable range (e.g., equal to, slightly greater than, or slightly less than).

[0056] like Figure 2 As shown, in some embodiments of this disclosure, the simulated standard deviation is determined based on the relative standard deviation of the X-ray photoelectron spectral area of ​​multiple sets of simulated X-ray photoelectron spectral data generated from previous X-ray photoelectron spectral data (box 430).

[0057] In some embodiments, the simulated standard deviation is the standard deviation of the relative standard deviations of the X-ray photoelectron spectral areas of multiple sets of simulated X-ray photoelectron spectral data generated from previous X-ray photoelectron spectral data. For example, the simulated standard deviation can be calculated using the following formula (6):

[0058]

[0059] In formula (6), σ RSD denoted by , μ represents the mean of the relative standard deviations of the X-ray photoelectron spectral areas of multiple sets of simulated X-ray photoelectron spectral data generated from previous X-ray photoelectron spectroscopy data. y represents the number of sets of simulated X-ray photoelectron spectroscopy data generated from previous X-ray photoelectron spectroscopy data, RSD(sv). y The relative standard deviation of the X-ray photoelectron spectral area for each set of simulated X-ray photoelectron spectral data generated from previous X-ray photoelectron spectral data is represented by x, which is an integer from 1 to y.

[0060] In some embodiments of this disclosure, the simulated standard deviation is the standard deviation of the relative standard deviation of the X-ray photoelectron spectral area of ​​multiple sets of simulated X-ray photoelectron spectral data. It can effectively measure the fluctuation level of X-ray photoelectron spectroscopy. Using the simulated standard deviation to limit the filtering effect can not only effectively suppress noise, but also fully maintain the original characteristics of the data.

[0061] like Figure 2 As shown, in some embodiments of this disclosure, the relative standard deviation of the X-ray photoelectron spectral area of ​​each set of simulated X-ray photoelectron spectral data is determined based on the X-ray photoelectron spectral area of ​​each simulated X-ray photoelectron spectral data in each set of simulated X-ray photoelectron spectral data (box 420).

[0062] In some embodiments, the relative standard deviation of the X-ray photoelectron spectral area of ​​each set of simulated X-ray photoelectron spectral data can be calculated using formula (7):

[0063] RSD(sv) y =σ(sv) / μ(sv)×100% (7)

[0064] In formula (7), RSD(sv) y represents the relative standard deviation of the X-ray photoelectron spectral area for each set of simulated X-ray photoelectron spectral data; μ(sv) represents the mean area for each set of simulated X-ray photoelectron spectral data. z represents the number of simulated X-ray photoelectron spectroscopy data in each group of simulated X-ray photoelectron spectroscopy data, A r Let r represent the spectral area of ​​each simulated X-ray photoelectron spectroscopy data set in each set of simulated X-ray photoelectron spectroscopy data, where r is an integer from 1 to z; σ(sv) is the standard deviation of the area of ​​each set of simulated X-ray photoelectron spectroscopy data.

[0065] In some embodiments of this disclosure, multiple sets of simulated X-ray photoelectron spectroscopy data are generated by generating multiple simulated X-ray photoelectron spectroscopy data by distributing any one of the count spectra in the previous X-ray photoelectron spectroscopy data according to a Poisson distribution and then grouping the multiple simulated X-ray photoelectron spectroscopy data into an average group.

[0066] In some embodiments, any count spectrum in the previous X-ray photoelectron spectroscopy data can be regarded as a p*1 matrix. If each element of this p*1 matrix is ​​used as the parameter λ of the Poisson distribution to generate random numbers following a Poisson distribution in the column direction, then a specified number q simulated X-ray photoelectron spectra can be generated using any count spectrum in the previous X-ray photoelectron spectroscopy data. At this time, the p*1 matrix becomes a p*q matrix. Then, the generated q simulated X-ray photoelectron spectroscopy data are evenly grouped, for example, into y groups, to obtain y groups of simulated X-ray photoelectron spectroscopy data.

[0067] like Figure 2 As shown, in some embodiments of this disclosure, the X-ray photoelectron spectral area of ​​each simulated X-ray photoelectron spectral data is calculated after subtracting the spectral background from each simulated X-ray photoelectron spectral data (box 410).

[0068] In some embodiments of this disclosure, the energy spectrum background may include an energy spectrum background based on polynomial fitting, wherein the X-ray photoelectron energy spectrum area is calculated based on multiple energy spectrum parameters determined by a summation function of a Gaussian function and a Lorentz function.

[0069] In some embodiments, a segment can be selected on each side of the target peak, and the background of the energy spectrum can be fitted using a polynomial. Then, the original energy spectrum is subtracted from the background to obtain the signal portion. Using the sum of the Gaussian function and the Lorentz function as the fitting function, various parameters of the signal can be given. The sum of the Gaussian function and the Lorentz function is defined as shown in formula (8):

[0070] F(x)=a·m·Lx+a·(1-m)·Gx (8)

[0071] In formula (8), Lx represents the Lorentz function. Gx is a Gaussian function. m, a, b, and c represent parameters, where m can adjust the ratio of the Gaussian function to the Lorentz function, a represents the peak height, b represents the peak position, and c represents the full width at half maximum (FWHM).

[0072] The integral of the sum of the Gaussian and Lorentz functions over the real interval is shown in formula (9):

[0073]

[0074] In formula (9), S represents the X-ray photoelectron spectral area.

[0075] In some embodiments, a count spectrum (vertical axis unit: counts) from previous X-ray photoelectron spectroscopy data is taken, and 6000 spectra are generated according to a Poisson distribution. These 6000 spectra are then divided into 200 groups of simulated X-ray photoelectron spectral data in groups of 30. The X-ray photoelectron spectral area Ar of each simulated X-ray photoelectron spectral data in each group can be calculated using formula (9). The relative standard deviation RSD(sv) of the X-ray photoelectron spectral area of ​​each group of simulated X-ray photoelectron spectral data can be calculated according to formula (7). y Then, according to formula (6), the simulated standard deviation σ of the X-ray photoelectron spectral area of ​​the 200 sets of simulated X-ray photoelectron spectral data generated from the previous X-ray photoelectron spectral data can be calculated. RSDThe simulated standard deviation of the X-ray photoelectron spectral area from 200 sets of simulated X-ray photoelectron spectral data can effectively measure the fluctuation level of the X-ray photoelectron spectrum. In some embodiments, when the difference between the relative standard deviation of the filtered X-ray photoelectron spectral area and the relative standard deviation of the X-ray photoelectron spectral area before filtering is approximately equal to the simulated standard deviation, the cutoff frequency of the FIR filter can be determined. Then, based on the cutoff frequency of the FIR filter and the predetermined order of the filter, the filter coefficients can be determined according to formula (5). For example, in some embodiments, the order can be set to 9th or 11th order to calculate the filter coefficients.

[0076] Those skilled in the art will understand that taking a count spectrum (vertical axis unit: counts) from previous X-ray photoelectron spectroscopy data, generating 6000 spectra according to a Poisson distribution, and dividing these 6000 spectra into 200 groups of 30 each to simulate X-ray photoelectron spectroscopy data is merely an example. Other suitable numbers of spectra can also be generated according to a Poisson distribution, such as 4000, 5000, 7000, or 10000 spectra, etc., and divided into multiple groups of 20, 40, 50, etc., to simulate X-ray photoelectron spectroscopy data. For example, they can be divided into 80 groups, 100 groups, 175 groups, 250 groups, 350 groups, 500 groups, etc., to simulate X-ray photoelectron spectroscopy data.

[0077] Those skilled in the art will understand that the 9th and 11th order filter coefficients are merely exemplary, and other order filter coefficients may be selected as needed.

[0078] The X-ray photoelectron spectroscopy processing method according to any embodiment of this disclosure can be applied to existing X-ray photoelectron spectroscopy systems.

[0079] Figure 4 A schematic diagram of an X-ray photoelectron spectroscopy system 300 according to some embodiments of the present disclosure is shown.

[0080] like Figure 4 As shown, in some embodiments of this disclosure, the X-ray photoelectron spectroscopy system 300 may include an X-ray photoelectron spectrometer 310 and a processor 320. The X-ray photoelectron spectrometer 310 is used to acquire X-ray photoelectron spectroscopy data. The processor 320 is communicatively connected to the X-ray photoelectron spectrometer. The processor is configured to execute an X-ray photoelectron spectroscopy processing method according to any embodiment of this disclosure to filter the X-ray photoelectron spectroscopy data acquired by the X-ray photoelectron spectrometer 310 to obtain filtered X-ray photoelectron spectroscopy data.

[0081] like Figure 4As shown, in some embodiments of this disclosure, the X-ray photoelectron spectroscopy system 300 may further include a vacuum chamber 330, and the X-ray photoelectron spectrometer 310 is at least partially connected to the vacuum chamber 330 in a vacuum seal. The sample 400 is placed inside the vacuum chamber 330 so that the sample 400 can be analyzed by the X-ray photoelectron spectrometer 310.

[0082] Those skilled in the art will understand that Figure 4 As an example only, the processor 320 may also be disposed in an X-ray photoelectron spectrometer to form an integrated X-ray photoelectron spectroscopy system.

[0083] The X-ray photoelectron spectroscopy system 300 according to any embodiment of this disclosure can be used in a variety of suitable analyses, including, but not limited to, elemental composition analysis, chemical state analysis, surface composition depth analysis, chemical bonding state analysis, etc.

[0084] In some embodiments, the X-ray photoelectron spectroscopy system 300 can be used, for example, for the measurement of silicon oxynitride (SiON).

[0085] Figure 3A The X-ray photoelectron spectroscopy data (N1s peak) obtained from the measurement of SiON before filtering is shown. Figure 3B The present disclosure illustrates X-ray photoelectron spectroscopy processing methods and systems employing some embodiments thereof. Figure 3A The X-ray photoelectron spectroscopy data shown is the filtered X-ray photoelectron spectroscopy data (N1s peak).

[0086] from Figure 3A and 3B It can be seen that the X-ray photoelectron spectroscopy processing method according to any embodiment of the present disclosure, or the X-ray photoelectron spectroscopy system configured with a processor to execute the X-ray photoelectron spectroscopy processing method according to any embodiment of the present disclosure, can effectively suppress noise in the X-ray photoelectron spectroscopy of SiON and improve measurement stability. Furthermore, the X-ray photoelectron spectroscopy processing method or X-ray photoelectron spectroscopy system according to some embodiments of the present disclosure has the advantages of high efficiency, accuracy, and real-time response.

[0087] In some embodiments of this disclosure, the computer device may include a memory and a processor. The memory is used to store at least one instruction. The processor is coupled to the memory and is used to execute at least one instruction to perform an X-ray photoelectron spectroscopy processing method according to any embodiment of this disclosure, for example, such as... Figure 1 The steps of the X-ray photoelectron spectroscopy processing method shown are some or all of the steps.

[0088] In some embodiments of this disclosure, a computer-readable storage medium may be used to store at least one instruction. When executed by a computer device, the at least one instruction causes the computer to perform an X-ray photoelectron spectroscopy processing method according to any embodiment of this disclosure, for example, such as... Figure 1 The illustrated X-ray photoelectron spectroscopy processing method includes some or all of the steps. Examples of computer-readable storage media include memory for computer programs (instructions), such as read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0089] It should be noted that the above are merely exemplary embodiments of this disclosure and are not intended to limit this disclosure. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A processing method of X-ray photoelectron spectroscopy, characterized by, include: Receive X-ray photoelectron spectroscopy data; as well as The X-ray photoelectron spectroscopy data is filtered using a finite impulse response (FIR) filter to obtain filtered X-ray photoelectron spectroscopy data. The FIR filter comprises N filter coefficients, where N is an integer greater than 1. These N coefficients are determined based on the cutoff frequency of the FIR filter and a predetermined order N. The cutoff frequency of the FIR filter is pre-determined based on previous X-ray photoelectron spectroscopy data. The filtering of the X-ray photoelectron spectroscopy data using an FIR filter includes: performing a convolution operation between the N filter coefficients and the X-ray photoelectron spectroscopy data to obtain filtered X-ray photoelectron spectroscopy data.

2. The X-ray photoelectron spectroscopy processing method according to claim 1, characterized in that, The X-ray photoelectron spectroscopy data includes M values, where M is an integer greater than N. The convolution operation between the N filter coefficients and the X-ray photoelectron spectroscopy data includes: calculating the convolution of the N filter coefficients with the N consecutive values ​​before the k-th value in the X-ray photoelectron spectroscopy data to obtain the k-th filtered value, where k is between 0 and M-1.

3. The X-ray photoelectron spectroscopy processing method according to claim 2, characterized in that, Receiving X-ray photoelectron spectroscopy data includes: continuously receiving values ​​from the X-ray photoelectron spectroscopy data; and The convolution operation between the N filter coefficients and the X-ray photoelectron spectroscopy data includes: calculating in real time the convolution of the N filter coefficients with the N consecutive values ​​before the k-th value in the X-ray photoelectron spectroscopy data to obtain the k-th filtered value.

4. The X-ray photoelectron spectroscopy processing method according to claim 1, characterized in that, The N filter coefficients are determined based on the cutoff frequency and sinc function of the FIR filter.

5. The X-ray photoelectron spectroscopy processing method according to claim 4, characterized in that, The N filter coefficients are determined based on the cutoff frequency of the FIR filter and the product of the sinc function and the Blackman window function.

6. The method for processing X-ray photoelectron spectroscopy according to claim 1, characterized in that, The cutoff frequency of the FIR filter is determined based on the difference between the relative standard deviation of the filtered X-ray photoelectron spectral area and the relative standard deviation of the X-ray photoelectron spectral area, as well as the simulated standard deviation.

7. The method for processing X-ray photoelectron spectroscopy according to claim 6, characterized in that, The simulated standard deviation is determined based on the relative standard deviation of the X-ray photoelectron spectral area of ​​multiple sets of simulated X-ray photoelectron spectral data generated from the previous X-ray photoelectron spectral data.

8. The method for processing X-ray photoelectron spectroscopy according to claim 7, characterized in that, The relative standard deviation of the X-ray photoelectron spectral area for each set of simulated X-ray photoelectron spectral data is determined based on the X-ray photoelectron spectral area of ​​each simulated X-ray photoelectron spectral data in each set of simulated X-ray photoelectron spectral data.

9. The method for processing X-ray photoelectron spectroscopy according to claim 7, characterized in that, The multiple sets of simulated X-ray photoelectron spectroscopy data are generated by generating multiple simulated X-ray photoelectron spectroscopy data by distributing any one of the count spectra in the previous X-ray photoelectron spectroscopy data according to a Poisson distribution, and then grouping the multiple simulated X-ray photoelectron spectroscopy data into an average group.

10. The method for processing X-ray photoelectron spectroscopy according to claim 8, characterized in that, The X-ray photoelectron spectral area of ​​each simulated X-ray photoelectron spectral data is calculated after subtracting the spectral background from each simulated X-ray photoelectron spectral data.

11. The method for processing X-ray photoelectron spectroscopy according to claim 10, characterized in that, The energy spectrum background includes an energy spectrum background based on polynomial fitting, and the X-ray photoelectron energy spectrum area is calculated based on multiple energy spectrum parameters determined by the sum of Gaussian and Lorentz functions.

12. An X-ray photoelectron spectroscopy system, characterized in that, include: X-ray photoelectron spectrometer is used to collect X-ray photoelectron spectroscopy data; as well as A processor, communicatively connected to the X-ray photoelectron spectrometer, is configured to execute the X-ray photoelectron spectroscopy processing method according to any one of claims 1-11, to filter the X-ray photoelectron spectroscopy data acquired by the X-ray photoelectron spectrometer to obtain filtered X-ray photoelectron spectroscopy data.

13. A computer device, characterized in that, The computer device includes: Memory for storing at least one instruction; and A processor, coupled to the memory and configured to execute the at least one instruction to perform the X-ray photoelectron spectroscopy processing method according to any one of claims 1-11.

14. A computer-readable storage medium for storing at least one instruction, characterized in that, When the at least one instruction is executed by a computer device, it causes the computer to perform the X-ray photoelectron spectroscopy processing method according to any one of claims 1-11.

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