LithoGDV: General Visual Understanding Meets Domain-Specific Visual Priors for Robust Lithography Hotspot Detection

Lithography hotspot detection (LHD) identifies local integrated circuit layout patterns susceptible to manufacturing defects. As technology nodes shrink, denser layouts and narrower process windows make detection increasingly sensitive to subtle geometric variations. To address this challenge, mainstream LHD methods rasterize polygonal layout clips into images and apply vision models for hotspot detection. These detectors, however, are trained on limited domain data and rely on learned parametric decision boundaries without explicitly reusing validated layout cases. Consequently, they may generalize poorly to unseen designs and miss hotspot patterns that differ only subtly from non-hotspots. We propose LithoGDV, built on the insight that robust LHD requires complementary knowledge: general visual understanding of layout geometry and domain-specific visual priors from comparable cases. LithoGDV fine-tunes a pretrained large vision model to learn fine-grained layout representations and constructs a knowledge base of process-verified cases. Retrieved hotspot and non-hotspot cases provide explicit evidence that complements the model's parametric prediction. On OpenHSDBench, LithoGDV achieves state-of-the-art detection accuracy against the leading open-source and non-open-source baselines across full-data, low-resource, and cross-design settings. We will publicly release the complete LithoGDV codebase, including all training and evaluation configurations.