Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM
This addresses wafer-level spatial monitoring for plasma etching processes, which is incremental as it extends LLM reprogramming to spatial estimation.
The paper tackles the problem of predicting wafer-level etch depth distributions from in-situ process time series, achieving stable performance under data-limited conditions using a Time-LLM-based spatial regression model.
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.