广东工业大学学报2024，Vol.41Issue(6)：45-51,7.DOI:10.12052/gdutxb.240114
基于主动学习和视觉状态空间模型的热点检测器
A Hotspot Detector Based on Active Learning and Visual State Space Models
摘要
Abstract
Physical verification is a critical concern in chip manufacturing,ensuring chip yield.Detecting potential hotspots in the chip layout before actual manufacturing is a critical step,which ensures manufacturing feasibility and enhances production efficiency.Traditional hotspot detection techniques suffer from long detection cycles and high computational resource consumption,resulting in increased time costs throughout the production cycle and limited detection of hotspot patterns.Based on active learning techniques and visual state space models,this paper proposes a new hotspot detection model.A memory-based sampling strategy is employed for query evaluation to mitigate the impact of the imbalance between hotspot and non-hotspot data on the model.Furthermore,the resolution constraints of the CNN structure and the secondary complexity of the ViT network architecture are optimized,leading to linear complexity for the hotspot detector.Testing results on the ICCAD-2012 competition dataset show that the proposed hotspot detector significantly reduces the false positive rate,achieving a rate of only 1.47%,while the recall reaches an impressive 98.89%.关键词
热点检测/深度学习/视觉状态空间模型/主动学习Key words
hotspot detection/deep learning/visual state space model/active learning分类
信息技术与安全科学引用本文复制引用
王盈,蔡述庭,熊晓明..基于主动学习和视觉状态空间模型的热点检测器[J].广东工业大学学报,2024,41(6):45-51,7.基金项目
广东省重点领域研发计划资助项目(2022B0701180001) （2022B0701180001）
