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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2603.23576 |
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| _version_ | 1866918407420510208 |
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| author | Kim, Hyunwoo Lee, Munyoung Jeon, Seung Hyub Lee, Kyu Sung |
| author_facet | Kim, Hyunwoo Lee, Munyoung Jeon, Seung Hyub Lee, Kyu Sung |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_23576 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM Kim, Hyunwoo Lee, Munyoung Jeon, Seung Hyub Lee, Kyu Sung Applications Artificial Intelligence Machine Learning 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. |
| title | Wafer-Level Etch Spatial Profiling for Process Monitoring from Time-Series with Time-LLM |
| topic | Applications Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2603.23576 |