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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
- 106 - 109 (4page)
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초록· 키워드
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This research proposes an innovative method for optimizing plasma etching processes in semiconductor manufacturing using machine learning (ML). Plasma etching is a critical process in defining precise patterns on semiconductor materials, requiring accurate process control. In this study, we employ the ML model based on big data to develop a predictive model that can capture complex relationships between process variables and plasma etching outcomes as the thickness of MoS₂. The ML model demonstrated high accuracy, closely aligning with actual experimental results. The experiments confirmed uniform etching across the entire 4-inch wafer, with a precision of approximately 1 ㎚. Based on this research, we aim to apply ML prediction models to various process conditions of plasma etching and gain deeper insight into the ML’s capabilities for two-dimensional materials in semiconductor manufacturing.
목차
ABSTRACT
1. Introduction
2. Experimental details
3. Results and discussion
4. Conclusions
References
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