Faster Region-based Hotspot Detection
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員
替代計量分析
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其它資訊
摘要As the circuit feature size continuously shrinks down, hotspot detection has become a more challenging problem in modern DFM flows. Developed deep learning techniques have recently shown their advantages on hotspot detection tasks. However, existing hotspot detectors only accept small layout clips as input with potential defects occurring at a center region of each clip, which will be time consuming and waste lots of computational resources when dealing with large full-chip layouts. In this paper, we develop a new end-to-end framework that can detect multiple hotspots in a large region at a time and promise a better hotspot detection performance. We design a joint auto-encoder and inception module for efficient feature extraction. A two-stage classification and regression flow is proposed to efficiently locate hotspot regions roughly and conduct final prediction with better accuracy and false alarm penalty. Experimental results show that our framework enables a significant speed improvement over existing methods with higher accuracy and fewer false alarms.
著者Ran Chen, Wei Zhong, Haoyu Yang, Hao Geng, Xuan Zeng, Bei Yu
會議名稱56th ACM/EDAC/IEEE Design Automation Conference (DAC)
會議開始日02.06.2019
會議完結日06.06.2019
會議地點Las Vegas, NV
會議國家/地區美國
會議論文集題名DAC '19 Proceedings of the 56th Annual Design Automation Conference 2019
出版年份2019
國際標準書號978-1-4503-6725-7
語言美式英語

上次更新時間 2020-18-01 於 03:16