  In this paper, we discussed limitation of current
electronic-design-automoation (EDA) tool and proposed a machine learning
framework to overcome the limitations and achieve better design quality. We
explored how to efficiently extract relevant features and leverage gradient
boost regressor (GBR) model to predict underestimated risky net (URN).
Customized routing optimizations are applied to the URNs and results show clear
timing improvement and trend to converge toward timing closure.
