SegSEM: Enabling and Enhancing SAM2 for SEM Contour Extraction
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866911602551291904 |
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| author | Chen, Da Hu, Guangyu Xu, Kaihong Liang, Kaichao Li, Songjiang Yang, Wei Wen, XiangYu Yuan, Mingxuan |
| author_facet | Chen, Da Hu, Guangyu Xu, Kaihong Liang, Kaichao Li, Songjiang Yang, Wei Wen, XiangYu Yuan, Mingxuan |
| contents | Extracting high-fidelity 2D contours from Scanning Electron Microscope (SEM) images is critical for calibrating Optical Proximity Correction (OPC) models. While foundation models like Segment Anything 2 (SAM2) are promising, adapting them to specialized domains with scarce annotated data is a major challenge. This paper presents a case study on adapting SAM2 for SEM contour extraction in a few-shot setting. We propose SegSEM, a framework built on two principles: a data-efficient fine-tuning strategy that adapts by selectively training only the model's encoders, and a robust hybrid architecture integrating a traditional algorithm as a confidence-aware fallback. Using a small dataset of 60 production images, our experiments demonstrate this methodology's viability. The primary contribution is a methodology for leveraging foundation models in data-constrained industrial applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20471 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SegSEM: Enabling and Enhancing SAM2 for SEM Contour Extraction Chen, Da Hu, Guangyu Xu, Kaihong Liang, Kaichao Li, Songjiang Yang, Wei Wen, XiangYu Yuan, Mingxuan Hardware Architecture Extracting high-fidelity 2D contours from Scanning Electron Microscope (SEM) images is critical for calibrating Optical Proximity Correction (OPC) models. While foundation models like Segment Anything 2 (SAM2) are promising, adapting them to specialized domains with scarce annotated data is a major challenge. This paper presents a case study on adapting SAM2 for SEM contour extraction in a few-shot setting. We propose SegSEM, a framework built on two principles: a data-efficient fine-tuning strategy that adapts by selectively training only the model's encoders, and a robust hybrid architecture integrating a traditional algorithm as a confidence-aware fallback. Using a small dataset of 60 production images, our experiments demonstrate this methodology's viability. The primary contribution is a methodology for leveraging foundation models in data-constrained industrial applications. |
| title | SegSEM: Enabling and Enhancing SAM2 for SEM Contour Extraction |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2602.20471 |