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.

翻译：从扫描电子显微镜（SEM）图像中提取高保真二维轮廓，对于校准光学邻近效应校正（OPC）模型至关重要。虽然像Segment Anything 2（SAM2）这样的基础模型颇具前景，但如何将其适应到标注数据稀缺的专业领域仍是一大挑战。本文呈现了一项关于在少样本场景下适配SAM2进行SEM轮廓提取的案例研究。我们提出SegSEM框架，该框架基于两个原则构建：一是数据高效的微调策略，通过选择性仅训练模型编码器实现适应；二是鲁棒的混合架构，将传统算法作为置信度感知的备用方案。基于包含60张生产图像的小型数据集，我们的实验验证了该方法的可行性。本研究的主要贡献在于，为数据受限的工业应用场景中利用基础模型提供了一套方法论。