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