You Only Need Non-Hotspot: An Unsupervised Training-Free Method for Layout Hotspot Detection

Recent advances in deep learning-based layout hotspot detection have made remarkable progress in identifying potential defect patterns at early design stages. However, most existing methods rely on supervised learning, which requires manual identification of pre-defined hotspots and leads to considerable labeling effort. Moreover, design houses often struggle to obtain a sufficient number of labeled hotspot samples, limiting the applicability and scalability of such methods. In this article, we introduce a novel approach, termed you only need non-hotspot (YONN), which to the best of our knowledge, is the first unsupervised and training-free framework for layout hotspot detection. The proposed method mitigates the dependence on labeled hotspot data by leveraging memorized prototypes and a query-based inference mechanism. Specifically, YONN employs a CNN-based prototype generation network to extract multi-scale, fine-grained representations of layouts. During inference, a combination of shape-aware and topology-aware query mechanisms facilitates precise pixel-wise matching between test layout and memorized prototypes. To further enhance YONN’s efficiency and scalability, we propose a prototype sampling strategy that integrates density-based clustering techniques, significantly reducing the scale of the prototypes. Experimental results indicate that YONN achieves performance within 10% of leading state-of-the-art supervised learning methods, despite operating in a fully unsupervised setting without access to hotspot data. As an optional extension, YONN surpasses existing state-of-the-art approaches using only 30% hotspot labels. Notably, YONN is a training-free framework that enables on-the-fly adaptation by directly incorporating novel samples into the prototype bank, thereby supporting efficient and scalable learning within design for manufacturability workflows.