Accurate prediction of detailed routing congestion using supervised data learning

Routing congestion model is of great importance in design stages of modern physical synthesis, e.g. global routing and routability estimation during placement. As the technology node becomes smaller, routing congestion is more difficult to estimate during design stages ahead of detailed routing. In this paper, we propose a framework using nonparametric regression technique in machine learning to construct routing congestion model. The constructed model can capture multiple factors and enables direct prediction of detailed routing congestion with high accuracy. By using this model in global routing, significant reduction of design rule violations and detailed routing runtime can be achieved compared with the model in previous work, with small overhead in global routing runtime and memory usage.