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Research Paper

Applied Science and Convergence Technology 2023; 32(5): 106-109

Published online September 30, 2023


Copyright © The Korean Vacuum Society.

Machine Learning-Based Prediction of Atomic Layer Control for MoS2 via Reactive Ion Etcher

Changmin Kima , † , Seunghwan Leeb , † , Muyoung Kima , Min Sup Choic , Taesung Kimb , d , and Hyeong-U Kima , *

aDepartment of Plasma Engineering, Korea Institute of Machinery and Materials, Daejeon 34103, Republic of Korea
bSchool of Mechanical Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea
cDepartment of Materials Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea
dSKKU Advanced Institute of Nanotechnology, Sungkyunkwan University, Suwon 16419, Republic of Korea

Correspondence to:guddn418@kimm.re.kr

Received: July 27, 2023; Revised: August 10, 2023; Accepted: August 11, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, distribution and reproduction in any medium without alteration, provided that the original work is properly cited.


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 nm. 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.

Keywords: Plasma, Machine learning, Layer control, MoS2, Reactive ion etching

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