用时间大模型直接预测晶圆刻蚀深度分布，提升工艺监控精度。
Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM
- 基于时间大模型重构输入与输出，实现从时序信号到二维空间分布的映射。
- 在数据有限条件下仍保持稳定性能，验证了方法可行性。
- 适合半导体制造中需要高精度空间监控的工艺优化场景。
从原位工艺信号中理解晶圆级空间变异对先进等离子体刻蚀工艺监控至关重要。尽管多数数据驱动方法聚焦于平均刻蚀速率等标量指标，但实际工艺质量由晶圆上的复杂二维空间分布决定。本文提出一种空间回归模型，可直接从多通道原位工艺时序数据中预测晶圆级刻蚀深度分布。我们设计了一种基于Time-LLM的空间回归模型，通过重新设计输入嵌入与输出投影，将大语言模型重编程从传统时序预测拓展至晶圆级空间估计。基于BOSCH等离子刻蚀数据集的实验表明，该方法在数据受限条件下表现稳定，证实了基于大模型重编程实现晶圆级空间监控的可行性。
原文摘要 · Abstract (English)
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.
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