Detecting Multi-Layer Layout Hotspots with Adaptive Squish Patterns
Refereed conference paper presented and published in conference proceedings

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AbstractLayout 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.
All Author(s) ListHaoyu Yang, Piyush Pathak, Frank Gennari, Ya-Chieh Lai, Bei Yu
Name of Conference24th Asia and South Pacific Design Automation Conference, ASPDAC 2019
Start Date of Conference21/01/2019
End Date of Conference24/01/2019
Place of ConferenceTokyo
Country/Region of ConferenceJapan
Proceedings TitleProceedings of the Asia and South Pacific Design Automation Conference
Pages299 - 304
LanguagesEnglish-United States

Last updated on 2021-13-05 at 01:58