[2603.23576] Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM

Statistics > Applications arXiv:2603.23576 (stat) [Submitted on 24 Mar 2026] Title:Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM Authors:Hyunwoo Kim, Munyoung Lee, Seung Hyub Jeon, Kyu Sung Lee View a PDF of the paper titled Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM, by Hyunwoo Kim and 3 other authors View PDF HTML (experimental) 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. Comments: Subjects: Applications (stat.AP); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2603.23576 [stat.AP]   (or arXiv:2603.23576v1 [stat.AP]...