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In this paper, we discussed limitation of currentelectronic-design-automoation (EDA) tool and proposed a machine learningframework to overcome the limitations and achieve better design quality. Weexplored how to efficiently extract relevant features and leverage gradientboost regressor (GBR) model to predict underestimated risky net (URN).Customized routing optimizations are applied to the URNs and results show cleartiming improvement and trend to converge toward timing closure.

Customized Routing Optimization Based on Gradient Boost Regressor Model