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

Hyunwoo Kim, Munyoung Lee, Seung Hyub Jeon, Kyu Sung Lee

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.

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.
Paper Structure (28 sections, 9 equations, 8 figures, 3 tables)

This paper contains 28 sections, 9 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Virtual Metrology for Process Monitoring
  4. Data-Driven Spatial Etch Profiling
  5. Time-Series-to-Spatial Regression via LLMs
  6. Background
  7. Baseline model: Time-LLM
  8. Target Domain: Silicon Etching Process
  9. Dataset Preparation
  10. Dataset Overview
  11. Channel Selection
  12. Dataset Conditioning
  13. Proposed Model Architecture
  14. Input Embedding
  15. Normalization
  16. ...and 13 more sections

Figures (8)

  • Figure 1: Overall architecture of the Time-LLM framework, reproduced from jin2023time.
  • Figure 2:
  • Figure 3:
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  • ...and 3 more figures