Problem
Research questions and friction points this paper is trying to address.
wafer-level
spatial profiling
plasma etching
process monitoring
etch depth distribution
Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM
📝 Abstract
Understanding wafer-level spatial variations from in-situ process signals is essential for advanced plasma etching process monitoring. While most data-driven approaches focus on scalar indicators such as average etch rate, actual process quality is determined by complex two-dimensional spatial distributions across the wafer. This paper presents a spatial regression model that predicts wafer-level etch depth distributions directly from multichannel in-situ process time series. We propose a Time-LLM-based spatial regression model that extends LLM reprogramming from conventional time-series forecasting to wafer-level spatial estimation by redesigning the input embedding and output projection. Using the BOSCH plasma-etching dataset, we demonstrate stable performance under data-limited conditions, supporting the feasibility of LLM-based reprogramming for wafer-level spatial monitoring.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Time-LLM
spatial regression
wafer-level etch profiling
in-situ process monitoring
plasma etching
🔎 Similar Papers
No similar papers found.
