IR Drop Prediction of ECO-Revised Circuits Using Machine Learning

• Main Authors: Shih-Yao Lin, 林士堯
• Other Authors: Chien-Mo Li
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/2rsx8s

碩士 === 國立臺灣大學 === 電子工程學研究所 === 105 === Excessive power supply noise (PSN), such as IR drop, can cause timing violation in VLSI chips. However, simulation PSN takes a very long time, especially when multiple iterations is needed in IR drop signoff. In this thesis, we propose a machine learning technique to build an IR drop prediction model based on circuits before ECO (engineer change order) revision. After revision, we can re-use this model to predict the IR drop of the revised circuit. Because the previous circuit(s) and the revised circuit are very similar, the model can be applied with small error. Since there are many cells on a die, after each IR drop analysis, we can easily obtain many IR drop data to train the machine learning model. We proposed seven feature extractions, which are simple and scalable for large designs. Our experiment results show that prediction accuracy (average error 3.7mV) and correlation (0.55) are very high for a three million-gate real design. The run time speedup is up to 30X. The proposed method is very useful for designers to save the simulation time when fixing the IR drop problem.