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


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)
會議地點Las Vegas, NV
會議論文集題名DAC '19 Proceedings of the 56th Annual Design Automation Conference 2019

上次更新時間 2020-18-01 於 03:16