SegSEM: Enabling and Enhancing SAM2 for SEM Contour Extraction
This work addresses the challenge of adapting foundation models to specialized industrial domains with scarce annotated data, offering a practical methodology for SEM contour extraction in OPC calibration.
SegSEM adapts SAM2 for SEM contour extraction using a data-efficient fine-tuning strategy and a hybrid architecture with a traditional algorithm as fallback, achieving viable results with only 60 production images.
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.