In this paper, we proposed a new machine learning based fast power integrity classifier that quickly flags the EM/IR hotspots. We discussed the features to extract to describe the power grid, cell power density, routing impact and controlled collapse chip connection (C4) bumps, etc. The continuous and discontinuous cases are identified and treated using different machine learning models. Nearest neighbors, random forest and neural network models are compared to select the best performance candidates. Experiments are run on open source benchmark, and result is showing promising prediction accuracy.
@article{arxiv.1711.03406,
title = {Machine Learning Based Fast Power Integrity Classifier},
author = {HuaChun Zhang and Lynden Kagan and Chen Zheng},
journal= {arXiv preprint arXiv:1711.03406},
year = {2017}
}