Sign-Off Timing Considerations via Concurrent Routing Topology Optimization

Abstract

Timing closure is considered across the circuit design flow. Generally, the early stage timing optimization can only focus on improving early timing metrics, e.g., rough timing estimation using linear RC model or prerouting path length, since obtaining sign-off performance needs a time-consuming routing flow. However, there is no consistency guarantee between early stage metrics and sign-off timing performance. Therefore, we utilize the power of deep learning techniques to bridge the gap between the early stage analysis and the sign-off analysis. A well-designed deep learning framework guides the adjustment of Steiner points to enable explicit early stage timing optimization. Cooperating with deep Steiner point adjustment, we propose the routing topology reconstruction to accelerate the convergence and hold a reasonable routing topology. Further, we also introduce Steiner point simplification as a post-processing technique to avoid unnecessary routing constraints. This article demonstrates the ability of the learning-assist framework to perform robust and efficient timing optimization in the early stage with comprehensive and convincing experimental results on real-world designs. With Steiner point adjustment alone, TSteinerPt, can help the state-of-the-art open-source router to obtain 11.2% and 7.1% improvement for the sign-off worst-negative slack and total negative slack, respectively. Under the additional joint optimization with routing topology reconstruction and simplification, TSteinerRec can further save 25.9% optimization duration with a better-sign-off performance.