Detecting Multi-Layer Layout Hotspots with Adaptive Squish Patterns
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Layout hotpot detection is one of the critical steps in modern integrated circuit design flow. It aims to find potential weak points in layouts before feeding them into manufacturing stage. Rapid development of machine learning has made it a preferable alternative of traditional hotspot detection solutions. Recent researches range from layout feature extraction and learning model design. However, only single layer layout hotspots are considered in state-of-the-art hotspot detectors and certain defects such as metal-to-via failures are not naturally supported. In this paper, we propose an adaptive squish representation for multilayer layouts, which is storage efficient, lossless and compatible with deep neural networks. We conduct experiments on 14nm industrial designs with a metal layer and its two adjacent via layers that contain metal-to-via hotspots. Results show that the adaptive squish representation can achieve satisfactory hotspot detection accuracy by incorporating a medium-sized convolutional neural networks.
著者Haoyu Yang, Piyush Pathak, Frank Gennari, Ya-Chieh Lai, Bei Yu
會議名稱24th Asia and South Pacific Design Automation Conference, ASPDAC 2019
會議開始日21.01.2019
會議完結日24.01.2019
會議地點Tokyo
會議國家/地區日本
會議論文集題名Proceedings of the Asia and South Pacific Design Automation Conference
出版年份2019
月份1
頁次299 - 304
國際標準書號978-145036007-4
語言美式英語

上次更新時間 2020-09-07 於 02:55