Toward a Reliable Power Ground Network

• Main Authors: Zeng, Yu-Kai, 曾郁凱
• Other Authors: Jiang, Hui-Ru
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/9uq8ny

碩士 === 國立交通大學 === 電子研究所 === 106 === At advanced technology nodes, there exists a variety of power/ground network (P/G network) structures. Due to the various structures, finding a robust P/G becomes a time-consuming task. A robust P/G means a P/G which satisfies the IR-drop constraint and occupies small routing resource. In order to shorten the time of finding a robust P/G, we improve the existing industrial flow by using machine-learning (ML) techniques. At first, we intend to insert an ML-predictor in the industrial flow to narrow down the search space by removing failed P/Gs which do not satisfy the IR-drop constraint. However, because of training data issues, in this thesis, we propose a producing guidance data method to collect the proper training data and use early stop to limit the number of produced training data. Based on our techniques, we overcome the training data issues and expedite the industrial flow. Experimental results show that we can reduce the runtime up to 48%. Furthermore, by analyzing the correlation between IR-drop and P/G structures, we find that IR-drop is sensitive to the structure of middle and bottom layers for advanced processes.