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


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AbstractAs 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.
All Author(s) ListRan Chen, Wei Zhong, Haoyu Yang, Hao Geng, Xuan Zeng, Bei Yu
Name of Conference56th ACM/EDAC/IEEE Design Automation Conference (DAC)
Start Date of Conference02/06/2019
End Date of Conference06/06/2019
Place of ConferenceLas Vegas, NV
Country/Region of ConferenceUnited States of America
Proceedings TitleDAC '19 Proceedings of the 56th Annual Design Automation Conference 2019
Year2019
ISBN978-1-4503-6725-7
LanguagesEnglish-United States

Last updated on 2020-19-05 at 02:26