Optical emission spectroscopy (OES) is a highly valuable tool for plasma characterization due to its nonintrusive and versatile nature. The intensities of the emission lines contain information about the parameters of the underlying plasma–electron density and temperature or, more generally, the electron energy distribution function (EEDF). This study aims to obtain the EEDF and from the OES data of argon plasma with machine learning (ML) techniques. Two different models, i.e., the Kernel Regression for Functional Data (KRFD) and an artificial neural network (ANN), are used to predict the normalized EEDF and Random Forest (RF) regression is used to predict . The ML models are trained with computed plasma data obtained from Particle-in-Cell/Monte Carlo Collision simulations coupled with a collisional–radiative model. All three ML models developed in this study are found to predict with high accuracy what they are trained to predict when the simulated test OES data are used as the input data. When the experimentally measured OES data are used as the input data, the ANN-based model predicts the normalized EEDF with reasonable accuracy under the discharge conditions where the simulation data are known to agree well with the corresponding experimental data. However, the capabilities of the KRFD and RF models to predict the EEDF and from experimental OES data are found to be rather limited, reflecting the need for further improvement of the robustness of these models.
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Machine learning-based prediction of the electron energy distribution function and electron density of argon plasma from the optical emission spectra
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September 2024
Research Article|
July 11 2024
Machine learning-based prediction of the electron energy distribution function and electron density of argon plasma from the optical emission spectra
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Fatima Jenina Arellano
;
Fatima Jenina Arellano
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing)
1
Graduate School of Engineering, Osaka University
, Osaka 565-0871, Japan
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Minoru Kusaba
;
Minoru Kusaba
(Formal analysis, Methodology, Software, Supervision, Validation, Writing – review & editing)
2
The Institute of Statistical Mathematics, Research Organization of Information and Systems
, Tachikawa 190-8562, Japan
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Stephen Wu
;
Stephen Wu
(Formal analysis, Methodology, Project administration, Software, Supervision)
2
The Institute of Statistical Mathematics, Research Organization of Information and Systems
, Tachikawa 190-8562, Japan
3
Department of Statistical Science, The Graduate University for Advanced Studies
, Tachikawa 190-8562, Japan
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Ryo Yoshida
;
Ryo Yoshida
(Formal analysis, Methodology, Project administration, Software, Supervision, Writing – review & editing)
2
The Institute of Statistical Mathematics, Research Organization of Information and Systems
, Tachikawa 190-8562, Japan
3
Department of Statistical Science, The Graduate University for Advanced Studies
, Tachikawa 190-8562, Japan
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Zoltán Donkó
;
Zoltán Donkó
(Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Writing – review & editing)
1
Graduate School of Engineering, Osaka University
, Osaka 565-0871, Japan
4
Institute for Solid State Physics and Optics, HUN-REN Wigner Research Centre for Physics
, Budapest 1121, Hungary
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Peter Hartmann
;
Peter Hartmann
(Data curation, Methodology, Resources, Software)
4
Institute for Solid State Physics and Optics, HUN-REN Wigner Research Centre for Physics
, Budapest 1121, Hungary
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Tsanko V. Tsankov
;
Tsanko V. Tsankov
(Data curation, Formal analysis, Software, Supervision, Writing – review & editing)
5
Faculty of Physics and Astronomy, Experimental Physics V, Ruhr University Bochum
, Bochum, 44801, Germany
6
LPP, CNRS, Sorbonne Université, École Polytechnique, Institut Polytechnique de Paris
, Palaiseau 91128, France
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Satoshi Hamaguchi
Satoshi Hamaguchi
a)
(Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Resources, Supervision, Visualization, Writing – review & editing)
1
Graduate School of Engineering, Osaka University
, Osaka 565-0871, Japan
a)Author to whom correspondence should be addressed: [email protected]
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Fatima Jenina Arellano
1
Minoru Kusaba
2
Stephen Wu
2,3
Ryo Yoshida
2,3
Zoltán Donkó
1,4
Peter Hartmann
4
Tsanko V. Tsankov
5,6
Satoshi Hamaguchi
1,a)
1
Graduate School of Engineering, Osaka University
, Osaka 565-0871, Japan
2
The Institute of Statistical Mathematics, Research Organization of Information and Systems
, Tachikawa 190-8562, Japan
3
Department of Statistical Science, The Graduate University for Advanced Studies
, Tachikawa 190-8562, Japan
4
Institute for Solid State Physics and Optics, HUN-REN Wigner Research Centre for Physics
, Budapest 1121, Hungary
5
Faculty of Physics and Astronomy, Experimental Physics V, Ruhr University Bochum
, Bochum, 44801, Germany
6
LPP, CNRS, Sorbonne Université, École Polytechnique, Institut Polytechnique de Paris
, Palaiseau 91128, France
a)Author to whom correspondence should be addressed: [email protected]
J. Vac. Sci. Technol. A 42, 053001 (2024)
Article history
Received:
May 02 2024
Accepted:
June 05 2024
Citation
Fatima Jenina Arellano, Minoru Kusaba, Stephen Wu, Ryo Yoshida, Zoltán Donkó, Peter Hartmann, Tsanko V. Tsankov, Satoshi Hamaguchi; Machine learning-based prediction of the electron energy distribution function and electron density of argon plasma from the optical emission spectra. J. Vac. Sci. Technol. A 1 September 2024; 42 (5): 053001. https://doi.org/10.1116/6.0003731
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