Customized Routing Optimization Based on Gradient Boost Regressor Model

Abstract

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.

6 pages, 7 tables, 3 figures

Cited by in corpus (4)

• Timing Aware Dummy Metal Fill Methodology
• Machine Learning Based Fast Power Integrity Classifier
• Modeling and Simulation of Electromigration Behavior for Via Array Structure
• Depth First Always On Routing Trace Algorithm