Generalizable Lithographic Hotspot Detection Using Asynchronous Meta-Learning with Only One Shot

With integrated circuits shrinking in feature size, layout printability has become increasingly challenging, making lithographic hotspot detection ever-crucial in computer-aided design (CAD) flows. In recent years, numerous studies have explored deep learning to detect lithographic hotspots, offering promising results. However, neural networks can easily be biased and overfit when lacking sufficient training data, especially in the CAD domain. A generalizable DL-based hotspot detector should learn the genuine lithography principle and ensure consistent accuracy across layouts from various designs at the same technology node, regardless of their varying design styles. However, we find that existing convolutional neural network (CNN)-based hotspot detectors fail to generalize to different circuit layouts other than the design it has been trained for. To this end, we propose a few-shot learning-based framework for generalizable CNN-based hotspot detection. We develop a meta-learning scheme that asynchronously updates the CNN feature extraction and classification component to obtain a metainitialized model that can quickly adapt to new designs using as few as one training layout clip. We propose a layout topologybased sampling strategy for few-shot adaptation to enhance generalization stability. Experimental results on ICCAD 2012 and 2019 datasets show that our framework enables superior generalization capabilities than prior arts on unseen new designs.