Why we need statistical static timing analysis

Abstract

As technology continues to advance deeper into the nanometer regime, a tight control on the process parameters is increasingly difficult. As a consequence, variability has turned out to be a dominant factor in the design of complex ICs. Traditional static timing analysis (STA) is becoming insufficient to accurately evaluate the process variation impact on the design performance considering the increasing number of process, power supply voltage, and temperature (PVT) corners. In contrast, statistical static timing analysis (SSTA) is a promising innovative technique to handle increasingly larger environmental and process fluctuations, especially on-chip parameter variations. However, the statistical approach needs a set of costly additional data such as an accurate process variation description, and a statistical standard cell library characterization. In this paper, STA and SSTA are applied on a real industrial design to compare the two techniques, in terms of both accuracy and cost. From our analysis, we have concluded that the potential advantages offered by SSTA exceed the additional library characterization cost and process data assembly effort.