Efficient Hotspot Detection via Graph Neural Network
Shuyuan Sun1, Yiyang Jiang1, Fan Yang1, Bei Yu2 and Xuan Zeng1
1State Key Lab of ASIC & System, School of Microelectronics, Fudan University, China
2Department of Computer Science and Engineering,The Chinese University of Hong Kong, China
ABSTRACT
Lithography hotspot detection is of great importance in chip manufacturing. It aims to find patterns that may incur defects in the early design stage. Inspired by the success of deep learning in computer vision, many works convert layouts into images, turn the hotspot detection problem into an image classification task. Traditional graph-based methods consume fewer computer resources and less detection time compared to imagebased methods, but they have too many false alarms. In this paper, a hotspot detection approach via the graph neural network (GNN) is proposed. We also propose a novel representation model to map a layout to one graph, in which we introduce multi-dimensional features to encode components of the layout. Then we use a modified GNN to further process the extracted layout features and get an embedding of the local geometric relationship. Experimental results on the ICCAD2012 Contest benchmarks show our proposed approach can achieve over 10× speedup and fewer false alarms without loss of accuracy. On the ICCAD2022 benchmark, our model can achieve 2.10% higher accuracy compared with the previous approach.
