广东工业大学学报2025，Vol.42Issue(6)：34-43,10.DOI:10.12052/gdutxb.250111
基于图族建模与图神经网络的热点检测算法
A Hotspot Detection Algorithm Based on Graph Family Modelling and Graph Neural Networks
摘要
Abstract
The rapid scaling of semiconductor technologies has led to increasingly complex integrated circuit layouts,which pose severe challenges to maintaining manufacturing yield and highlight the urgent need for accurate lithographic hotspot detection.Existing detection methods are often constrained by limited layout modelling capacity,insufficient separability of augmented samples,and suboptimal classification performance.To overcome these challenges,a hotspot detection algorithm was developed that combined a multi-scale graph family modelling strategy with a hierarchical graph neural network.The proposed graph family model constructed both local subgraphs and a global graph to represent geometric details and contextual semantics simultaneously,thereby improving structural representation and preserving data separability under various augmentation operations.On this basis,a hierarchical GNN was designed to extract multi-level features and strengthen the model's focus on critical core regions.Experimental evaluations on the ICCAD'19 TNSB benchmark demonstrate that the proposed method substantially improves data distribution balance,achieving a 206 percentage point increase in MNND and a 16 percentage point gain in Shannon entropy.Moreover,the detection model attains a recall of 99.91%with only a 1.12%false alarm rate,outperforming state-of-the-art alternatives.These results confirm that the proposed graph-based approach offers both strong structural expressiveness and robust detection capability,providing a promising solution for addressing the challenges of lithographic hotspot detection in advanced technology nodes.关键词
光刻热点检测/图神经网络/多尺度图族建模/图族结构/数据增强表达能力Key words
lithographic hotspot detection/graph neural network/multi-scale graph family modelling/graph family structure/data augmentation expressiveness分类
电子信息工程引用本文复制引用
梁珑曦,蔡述庭,张慧..基于图族建模与图神经网络的热点检测算法[J].广东工业大学学报,2025,42(6):34-43,10.基金项目
广东省重点领域研发计划项目(2022B0701180001) （2022B0701180001）
