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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국진공학회(ASCT) Applied Science and Convergence Technology Applied Science and Convergence Technology Vol.32 No.5
- 발행연도
- 2023.9
- 수록면
- 122 - 126 (5page)
이용수
초록· 키워드
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This study proposes a model that combines deep learning (DL) techniques with plasma simulations to efficiently investigate optimal process conditions. The DL model was trained using data obtained from an Ar/O₂ inductively coupled plasma discharge simulation. Plasma discharge parameters such as the O₂ ratio, pressure, and power were trained as input data to predict the electron density, electron temperature, potential, and the densities of Ar⁺, O<SUP>2+</SUP>, O⁻, and O⁺. The performance of the DL model was verified by comparing the results of interpolation, which predicted a constant pattern within the range of the trained data, and extrapolation, which predicted a pattern beyond the trained data range, with the ground truth to verify the low error rate. The proposed deep neural network model can significantly reduce the necessity for trial and error when adjusting the process conditions. This model is expected to be an effective tool for narrowing the process window during the early stages of equipment and process development.
목차
ABSTRACT
1. Introduction
2. Material and methods
3. Results and discussion
4. Conclusions
References
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