From Global Route to Detailed Route: ML for Fast and Accurate Wire Parasitics and Timing Prediction

Abstract

Timing prediction and optimization are challenging in design stages prior to detailed routing (DR) due to the unavailability of routing information. Inaccurate timing prediction wastes design effort, hurts circuit performance, and may lead to design failure. This work focuses on timing prediction after clock tree synthesis and placement legalization, which is the earliest opportunity to time and optimize a "complete" netlist. The paper first documents that having "oracle knowledge" of the final post-DR parasitics enables post-global routing (GR) optimization to produce improved final timing outcomes. Machine learning (ML)-based models are proposed to bridge the gap between GR-based parasitic and timing estimation and post-DR results during post-GR optimization. These models show higher accuracy than GR-based timing estimation and, when used during post-GR optimization, show demonstrable improvements in post-DR circuit performance. Results on open 45nm and 130nm enablements using OpenROAD show efficient improvements in post-DR WNS and TNS metrics without increasing congestion.