Batch information processing and imaging method for electron energy loss spectrum

Abstract

The invention relates to a batch information processing and imaging method for an electron energy loss spectrum, and belongs to the field of electron energy loss spectrum information processing. The method relates to feature extraction and analysis of electron energy loss spectrums and is mainly used for feature information analysis of nanometer materials. By integrating batch processing algorithms such as a power law background deduction algorithm, a Fourier ratio deconvolution multiple scattering removal algorithm, a multi-order Gaussian peak background fitting algorithm and a peak value integration algorithm, the problems of high processing difficulty and low processing efficiency of the electron energy loss spectrum can be effectively solved, and support is provided for wider technical expansion.

Description

Technical Field

[0001] The invention belongs to the field of electron energy loss spectrum information processing, and in particular relates to a batch information processing and imaging method of electron energy loss spectrum. Background Art

[0002] Electron Energy Loss Spectroscopy (EELS) is an important method to characterize the electronic structure and valence bond information of materials, and is widely used in energy, catalysis, semiconductors and other fields. Through spectrum analysis, the energy loss after the interaction between electrons with known kinetic energy and atoms in the sample to be tested can be obtained. By using the distribution characteristics of energy loss, the spatial environment information of the electrons in the sample to be tested can be obtained, and then the physical and chemical properties of the material can be analyzed.

[0003] Since the information extraction of electron energy loss spectrum involves complex mathematical operations and requires a professional theoretical knowledge base. The current conventional spectrum processing and information extraction methods are mainly to extract single spectrum information or image a single two-dimensional array through software. Many documents also use this method for processing, such as the processing methods reported in J.Appl.Phys.108,063704 (2010) and Nat.Commun.15,378 (2024). However, with the increase in the number of materials and the increase in the dimension of material analysis, researchers need to perform similar processing on a large number of electron energy loss spectra, which requires a batch processing technology to analyze a certain type of nanomaterial at the same time and realize batch information extraction and imaging of multiple two-dimensional electron energy loss spectra. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the existing batch processing of two-dimensional electron energy loss spectra in the structure and energy characterization of nanomaterials, fully consider the theoretical knowledge such as energy loss theory, electronic structure characteristics, spectrum peak characteristic absorption in the spectrum information, integrate the power law background subtraction algorithm, Fourier ratio deconvolution demultiple scattering algorithm, multi-order Gaussian peak background fitting algorithm, peak integration algorithm and other batch processing algorithms, and provide a batch information processing and imaging method of electron energy loss spectrum.

[0005] To achieve the above-mentioned purpose, the technical solution of the present invention is: a method for batch information processing and imaging of electron energy loss spectrum, including batch information processing and two-dimensional imaging of electron energy loss spectrum; wherein, the batch information processing process integrates a batch processing algorithm to realize batch information processing of electron energy loss spectrum.

[0006] In one embodiment of the present invention, the electron energy loss spectrum is stored in a matrix structure before batch information processing, and the storage structure is [x, y, z, n], where x and y are the acquisition length x and width y of the electron energy loss spectrum, z is the energy acquisition length of the electron energy loss spectrum, and n is the number of batches; the same batch contains a zero-loss Low-loss spectrum [x, y, z, n]0, an energy range m0, and a high-energy loss High-loss spectrum [x, y, z, n]1, an energy range m1; in multi-batch processing, there are different batches k.

[0007] In one embodiment of the present invention, the batch information processing process includes energy value correction, power law background subtraction, logarithmic ratio thickness calculation, Fourier ratio deconvolution to remove multiple scattering, multi-order Gaussian peak background fitting, and peak integration.

[0008] In one embodiment of the present invention, the energy value correction is specifically implemented as follows:

[0009] Extract the peak signal at each [x,y]0 point in the Low-loss spectrum, and calculate the difference Δx between the energy value at the strongest peak at each point and the 0 value energy , shift the energy value of each point in the high-loss spectrum [x, y]1 by the corresponding Δx energy .

[0010] In one embodiment of the present invention, the power-law background subtraction is specifically implemented as follows:

[0011] In the electron energy loss spectrum, the background shows an exponential decay feature. Based on this feature, the power law algorithm is used to subtract the background. The calculation formula is:

[0012] I=a·x b energy (1)

[0013] Among them, a and b are fitting parameters, x energy is the energy value of the fitting area; to avoid measurement errors, the fitting signal is defined as the average value of a spectral image; in the batch processing process, the energy range of 50eV to 100eV before the peak is selected as the background fitting area, and the fitting signal is defined as the average value of all points in each spectral image [x, y]. At the same time, considering the signal difference of each point, the magnification parameter c is used i To optimize:

[0014] I=I acquire -c i ·a·x b energy (2)

[0015] Among them, I acquireis the measured value, and I is the signal after background subtraction.

[0016] In one embodiment of the present invention, the logarithmic ratio thickness calculation is specifically implemented as follows:

[0017] According to the integrated intensity of the signal in the low loss spectrum, the thickness is calculated using the logarithmic ratio method. The thickness is a multiple of the inelastic mean free path. The calculation method is as follows:

[0018] t / μ=ln(I t / I0) (3)

[0019] Where I0 is the total zero-loss peak count, I t is the sum of the total spectral counts, i.e., the zero-loss peak plus the extrapolated 50 eV loss portion, μ is the mean free path of inelastic scattering, and t is the sample thickness; using the relative thickness, the thickness-corrected intensity is calculated as follows:

[0020] I high-loss =I·e^(-t / μ) (4)

[0021] Among them, I high-loss is the corrected high energy loss spectrum.

[0022] In one embodiment of the present invention, the Fourier ratio deconvolution to remove multiple scattering is specifically implemented as follows:

[0023] The energy loss function ELF of all points in each spectral image [x, y] is calculated by two-dimensional Fourier transform; the high-energy loss spectrum is converted from the energy domain to the frequency domain by Fourier transform. In the frequency domain, the Fourier transform of the high-energy loss spectrum is divided by the energy loss function for deconvolution, and the deconvolved frequency domain data is inversely Fourier transformed to convert it back to the energy domain to eliminate the influence of multiple scattering effects on the high-energy loss spectrum. The calculation formula is as follows:

[0024] I corr =IFT((FT(I high-loss )) / FT(I low-loss )) (5)

[0025] Where FT represents Fourier transform, I low-loss is the zero-loss spectrum, IFT stands for inverse Fourier transform, I corr is the corrected signal intensity.

[0026] In one embodiment of the present invention, the multi-order Gaussian peak background fitting is specifically implemented as follows:

[0027] The baseline of the element peak in the material is fitted according to the correction signal. The fitting area is selected as the extended area of ​​the pre-peak energy value x1 and the post-peak energy value x2. The baseline fitting adopts the third-order Gaussian model:

[0028] I g =a1·e^((-((x energy -b1) / c1)^2))+a2·e^((-((x energy -b2) / c2)^2))…

[0029] +a3·e^((-((x energy -b3) / c3)^2))(6)

[0030] Among them, I g is the baseline value, a i ,b i ,c i are fitting parameters, i=1,2,3.

[0031] In one embodiment of the present invention, the peak integration is specifically implemented as follows:

[0032] Calculate the peak integral of each point in each spectral image [x, y]. This value is the characteristic value of the material information at the corresponding position. Image the characteristic value of each image to obtain a batch of two-dimensional images; the integral value is:

[0033] Intensity = ∑(I corr -I g ) (7).

[0034] The present invention also provides a batch information processing and imaging system for electron energy loss spectrum, including a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0035] Compared with the prior art, the present invention has the following beneficial effects: the method of the present invention fully considers theoretical knowledge such as energy loss theory, electronic structure characteristics, and spectrum peak characteristic absorption in the spectrum information, and integrates batch processing algorithms such as power-law background subtraction algorithm, Fourier ratio deconvolution and multiple scattering removal algorithm, multi-order Gaussian peak background fitting algorithm, and peak integration algorithm. The method of the present invention can realize batch information processing and imaging of electron energy loss spectra, and can effectively solve the problems of difficulty and low processing efficiency in electron energy loss spectrum processing, and provide support for more extensive technology expansion. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is the batch processing flow of the electron energy loss spectrum of the present invention.

[0037] Figure 2 Schematic diagram of baseline fitting.

[0038] Figure 3 This is a schematic diagram of the spectrum after correction.

[0039] Figure 4 To batch extract and image copper information in nanomaterials.

[0040] Figure 5 To extract and image selenium information in nanomaterials in batches. DETAILED DESCRIPTION

[0041] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings.

[0042] Characterization of the microscopic properties of nanomaterials is an important means to study and optimize the functional properties of materials. Electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy has the unique ability to characterize the electronic structure and valence bond information of nanomaterials. Its energy resolution is close to 0.1eV, and it can record the fine structure of the spectrum with high precision. It is an important cutting-edge analytical method. However, with the demand for high-throughput and high-dimensional characterization, the analysis method of single spectrum and single sheet has become an important constraint on the development of this technology. The information analysis of electron energy loss spectra requires a lot of time from professionals, large-scale data processing, and expensive labor costs. The development of batch processing technology can effectively solve this defect, improve the analysis efficiency of spectra, and further improve the research progress of materials.

[0043] like Figure 1 As shown, the present invention provides a batch information processing and imaging method of electron energy loss spectrum, including batch information processing and two-dimensional imaging of electron energy loss spectrum; wherein the batch information processing process integrates a batch processing algorithm to realize batch information processing of electron energy loss spectrum. Specifically, the batch information processing process includes energy value correction, power law background subtraction, logarithmic ratio thickness calculation, Fourier ratio deconvolution to remove multiple scattering, multi-order Gaussian peak background fitting, and peak integration.

[0044] First, the electron energy loss spectrum is stored in a matrix structure before batch information processing. The storage structure is [x, y, z, n], where x and y are the acquisition length x and width y of the electron energy loss spectrum, z is the energy acquisition length of the electron energy loss spectrum, and n is the number of batches. The same batch contains a zero-loss Low-loss spectrum [x, y, z, n]0 with an energy range of m0, and a high-energy loss High-loss spectrum [x, y, z, n]1 with an energy range of m1. In multi-batch processing, there are different batches k.

[0045] Secondly, the energy value correction is batch processed. The peak signal at each [x, y]0 point in the Low-loss spectrum is extracted, and the difference Δx between the energy value at the strongest peak of each point and the 0 value is calculated. energy , shift the energy value of each point in the high-loss spectrum [x, y]1 by the corresponding Δxenergy .

[0046] Next, the background subtraction batch processing is performed. In the electron energy loss spectrum, the electrons are inelastically scattered, and the lost part of the energy undergoes a random small deflection. In addition, due to the influence of multiple scattering and thickness effect, the background shows an exponential decay feature. Based on this feature, the power law algorithm is used to subtract the background, and the calculation formula is:

[0047] I=a·x b energy (1)

[0048] Among them, a and b are fitting parameters, x energy is the energy value of the fitting area; to avoid measurement errors, the fitting signal is defined as the average value of a spectral image; in the batch processing process, the energy range of 50eV to 100eV before the peak is selected as the background fitting area, and the fitting signal is defined as the average value of all points in each spectral image [x, y]. At the same time, considering the signal difference of each point, the magnification parameter c is used i To optimize:

[0049] I=I acquire -c i ·a·x b energy (2)

[0050] Among them, I acquire is the measured value, and I is the signal after background subtraction.

[0051] Furthermore, logarithmic ratio thickness correction is used. Based on the integrated intensity of the signal in the low loss spectrum, the thickness is calculated using the logarithmic ratio method. The thickness is a multiple of the inelastic mean free path. The calculation method is as follows:

[0052] t / μ=ln(I t / I0) (3)

[0053] Where I0 is the total zero-loss peak count, I t is the sum of the total spectral counts, i.e., the zero-loss peak plus the extrapolated 50 eV loss portion, μ is the mean free path of inelastic scattering, and t is the sample thickness; using the relative thickness, the thickness-corrected intensity is calculated as follows:

[0054] I high-loss =I·e^(-t / μ) (4)

[0055] Among them, I high-loss is the corrected high energy loss spectrum.

[0056] Then, Fourier ratio deconvolution is used to remove multiple scattering. The energy loss function ELF of all points in each spectral image [x, y] is calculated through two-dimensional Fourier transform; the high-energy loss spectrum is converted from the energy domain to the frequency domain through Fourier transform. In the frequency domain, the Fourier transform of the high-energy loss spectrum is divided by the energy loss function for deconvolution, and the deconvolved frequency domain data is inversely Fourier transformed to convert it back to the energy domain to eliminate the influence of multiple scattering effects on the high-energy loss spectrum. The calculation formula is as follows:

[0057] I corr =IFT((FT(I high-loss )) / FT(I low-loss )) (5)

[0058] Where FT represents Fourier transform, I low-loss is the zero-loss spectrum, IFT stands for inverse Fourier transform, I corr is the corrected signal intensity.

[0059] Secondly, multi-order Gaussian peak background fitting. The baseline of the element peak in the material is fitted according to the calibration signal, and the fitting area is selected as the extended area of ​​the pre-peak energy value x1 and the post-peak energy value x2. The baseline fitting adopts the third-order Gaussian model:

[0060] I g =a1·e^((-((x energy -b1) / c1)^2))+a2·e^((-((x energy -b2) / c2)^2))…

[0061] +a3·e^((-((x energy -b3) / c3)^2))(6)

[0062] Among them, I g is the baseline value, a i ,b i ,c i are fitting parameters, i=1,2,3.

[0063] Figure 2 Schematic diagram of baseline fitting. Figure 3 This is a schematic diagram of the spectrum after correction.

[0064] Finally, peak integration and two-dimensional imaging. Calculate the peak integral of each point in each spectral image [x, y]. This value is the characteristic value of the material information at the corresponding position. Image the characteristic value of each image to obtain a batch of two-dimensional images; the integral value is:

[0065] Intensity = ∑(I corr -I g ) (7).

[0066] The present invention also provides a batch information processing and imaging system for electron energy loss spectrum, including a memory, a processor, and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps described above can be implemented.

[0067] Example 1

[0068] Batch extraction and imaging of copper information in nanomaterials.

[0069] (1) Using the electron energy loss tester of a transmission electron microscope, five electron energy loss spectra of copper materials were collected and stored in a matrix. The matrix of the zero-loss spectrum is A[42,43,2020,5], with an energy range of -150eV to 1365eV, and the matrix of the high-loss spectrum is B[42,43,2020,5], with an energy range of -650eV to 2165eV.

[0070] (2) Extract the peak signal at each point in the low-loss spectrum, calculate the difference Δxenergy between the energy value at the strongest peak of each point and the value of 0, and store the difference value in the matrix C[42,43,5]. Shift the energy value of each point in the high-loss spectrum by the corresponding Δxenergy to obtain the matrix D[42,43,2020,5].

[0071] (3) According to the integrated intensity of the signal in the low-loss spectrum, the relative thickness t / μ of each spectrum is calculated. The relative thickness is stored in the matrix E[42,43,5]. The high-loss spectrum is corrected using the relative thickness to obtain the matrix E[42,43,2020,5].

[0072] (4) Using the matrix A of the low-loss spectrum and the matrix E of the high-loss spectrum, multiple scattering is removed by Fourier ratio deconvolution to obtain the matrix F[42,43,2020,5].

[0073] (5) According to the signal matrix F[42,43,2020,5] of the high-loss spectrum, the signal peak background is fitted in the extended area with energy value 100 eV before the copper peak (931 eV) and energy value 100 eV after the peak (981 eV).

[0074] (6) In the cumulative energy range of 931 to 981 eV, the energy value of the signal peak background is deducted to obtain the element signal intensity at each point, and an image is formed based on this. The resulting image is as follows: Figure 4 shown.

[0075] Example 2

[0076] Batch extraction and imaging of selenium information in nanomaterials.

[0077] Steps (1) to (4) are the same as those described in Example 1, except that 5 electron energy loss spectra of the selenium material are collected and stored in a matrix.

[0078] (5) According to the signal matrix G[42,43,2020,5] of the high-loss spectrum of selenium material, the signal peak background is fitted in the extended area with energy value 50 eV before the selenium peak (1436 eV) and energy value 50 eV after the peak (1486 eV).

[0079] (6) In the cumulative energy range of 1436 to 1486 eV, the energy value of the signal peak background is deducted to obtain the element signal intensity at each point, and an image is formed based on this. The resulting image is as follows: Figure 5 shown.

[0080] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions do not exceed the scope of the technical solution of the present invention, belong to the protection scope of the present invention.

Claims

1. A method for batch information processing and imaging of electron energy loss spectrum, characterized in that: It includes batch information processing and two-dimensional imaging of electron energy loss spectra; wherein, the batch information processing process integrates a batch processing algorithm to realize batch information processing of electron energy loss spectra.

2. The method for batch information processing and imaging of electron energy loss spectrum according to claim 1, characterized in that: Before batch information processing, the electron energy loss spectrum is stored in a matrix structure with the storage structure of [x, y, z, n], where x and y are the acquisition length x and width y of the electron energy loss spectrum, z is the energy acquisition length of the electron energy loss spectrum, and n is the number of batches; the same batch contains a zero-loss Low-loss spectrum [x, y, z, n]0 with an energy range of m0, and a high-energy loss High-loss spectrum [x, y, z, n]1 with an energy range of m1; in multi-batch processing, there are different batches k.

3. The method for batch information processing and imaging of electron energy loss spectrum according to claim 2, characterized in that: The batch information processing process includes energy value correction, power law background subtraction, logarithmic ratio thickness calculation, Fourier ratio deconvolution to remove multiple scattering, multi-order Gaussian peak background fitting, and peak integration.

4. The method for batch information processing and imaging of electron energy loss spectrum according to claim 3, characterized in that: The energy value correction is specifically implemented as follows: Extract the peak signal at each [x,y]0 point in the Low-loss spectrum, and calculate the difference Δx between the energy value at the strongest peak at each point and the 0 value energy , shift the energy value of each point in the high-loss spectrum [x, y]1 by the corresponding Δx energy .

5. The method for batch information processing and imaging of electron energy loss spectrum according to claim 4, characterized in that: The power-law background subtraction is specifically implemented as follows: In the electron energy loss spectrum, the background shows an exponential decay feature. Based on this feature, the power law algorithm is used to subtract the background. The calculation formula is: I=a·x b energy (1) Among them, a and b are fitting parameters, x energy is the energy value of the fitting area; to avoid measurement errors, the fitting signal is defined as the average value of a spectral image; in the batch processing process, the energy range of 50eV to 100eV before the peak is selected as the background fitting area, and the fitting signal is defined as the average value of all points in each spectral image [x, y]. At the same time, considering the signal difference of each point, the magnification parameter c is used i To optimize: I=I acquire -c i ·a·x b energy (2) Among them, I acquire is the measured value, and I is the signal after background subtraction.

6. The method for batch information processing and imaging of electron energy loss spectrum according to claim 5, characterized in that: The logarithmic ratio thickness calculation is specifically implemented as follows: According to the integrated intensity of the signal in the low loss spectrum, the thickness is calculated using the logarithmic ratio method. The thickness is a multiple of the inelastic mean free path. The calculation method is as follows: t / µ1ln(I t / I0) (3) Where I0 is the total zero-loss peak count, I t is the sum of the total spectral counts, i.e., the zero-loss peak plus the extrapolated 50 eV loss portion, μ is the mean free path of inelastic scattering, and t is the sample thickness; using the relative thickness, the thickness-corrected intensity is calculated as follows: I high-loss =I·e^(-t / μ) (4) Among them, I high-loss is the corrected high energy loss spectrum.

7. The method for batch information processing and imaging of electron energy loss spectrum according to claim 6, characterized in that: The Fourier ratio deconvolution to remove multiple scattering is specifically implemented as follows: The energy loss function ELF of all points in each spectral image [x, y] is calculated by two-dimensional Fourier transform; the high-energy loss spectrum is converted from the energy domain to the frequency domain by Fourier transform. In the frequency domain, the Fourier transform of the high-energy loss spectrum is divided by the energy loss function for deconvolution, and the deconvolved frequency domain data is inversely Fourier transformed to convert it back to the energy domain to eliminate the influence of multiple scattering effects on the high-energy loss spectrum. The calculation formula is as follows: I corr =IFT((FT(I high-loss )) / FT(I low-loss )) (5) Where FT represents Fourier transform, I low-loss is the zero-loss spectrum, IFT stands for inverse Fourier transform, I corr is the corrected signal intensity.

8. The method for batch information processing and imaging of electron energy loss spectrum according to claim 7, characterized in that: The multi-order Gaussian peak background fitting is specifically implemented as follows: The baseline of the element peak in the material is fitted according to the correction signal. The fitting area is selected as the extended area of ​​the pre-peak energy value x1 and the post-peak energy value x2. The baseline fitting adopts the third-order Gaussian model: I g =a1·e^((-((x energy -b1) / c1)^2))+a2·e^((-((x energy -b2) / c2)^2))…+a3·e^((-((x energy -b3) / c3)^2))(6) Among them, I g is the baseline value, a i ,b i ,c i are fitting parameters, i=1,2,3.

9. The method for batch information processing and imaging of electron energy loss spectrum according to claim 8, characterized in that: The peak integration is specifically implemented as follows: Calculate the peak integral of each point in each spectral image [x, y]. This value is the characteristic value of the material information at the corresponding position. Image the characteristic value of each image to obtain a batch of two-dimensional images; the integral value is: Intensity=∑(I corr -I g ) (7)。 10. A batch information processing and imaging system for electron energy loss spectrum, characterized in that: The method comprises a memory, a processor and computer program instructions stored in the memory and capable of being executed by the processor. When the processor executes the computer program instructions, the method steps as claimed in any one of claims 1 to 9 can be implemented.

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