On the Improvement of Statistical Timing Analysis

As the minimum feature sizes of VLSI fabrication processes continue to shrink, the impact of process variations is becoming increasingly significant. This has prompted research into extending traditional static timing analysis so that it can be performed statistically. However, statistical static timing analysis (SSTA) tends to be quite pessimistic. In this paper we present a sensitizable statistical timing analysis (StatSense) technique to overcome the pessimism of SSTA. Our StatSense approach implicitly eliminates false paths, and also uses different delay distributions for different input transitions for any gate. These features enable our StatSense approach to perform less conservative timing analysis than the SSTA approach. Our results show that on average, the worst case (mu + 3sigma) circuit delay reported by StatSense is about 20% lower than that reported by SSTA.