FACT: Fast and Accurate Multi-Corner Predictor for Timing Closure in Commercial EDA Flows

Abstract

With technology scaling progressing well into deep nanometer region, the number of technology corners surges from dozens to hundreds. This dramatic increase in corners significantly complicates timing closure during Engineering Change Orders (ECO) stages, as performing full-corner static timing analysis (STA) becomes increasingly time-consuming and challenging. Existing methodologies often leverage machine learning (ML) techniques to predict unknown corners based on a subset of known corners. However, as the total number of corners expands, these methods not only require a larger set of known corners but also become highly sensitive to known corner selection. Additionally, as designers push designs into near-threshold voltage regions to enhance energy efficiency, the number of corners increases and the nonlinearity between corners becomes more pronounced. This intensifies the difficulty for current ML-based methods to accurately predict full-corner timing metrics. In this work, we propose FACT, a fast and accurate multi-corner predictor for timing closure optimization. Our approach simplifies the process by necessitating timing analysis under only one known corner. By effectively capturing correlations across diverse technology files, FACT robustly infers full-corner timing metrics, even under challenging near-threshold conditions. Moreover, our framework seamlessly integrates with commercial EDA design flows, making it practical in industrial environments. Experimental results on open-source designs indicate the superior stability of our method, coupled with a significant runtime speed-up over both traditional and prior ML-based timing ECO flows.