SD-SSTA: Statistical Static Time Analysis Algorithm Considering Skewed Distribution

Static Timing Analysis (STA) is one of the most widely used and successful analysis engines in digital circuit design in recent years. However, the Deterministic Static Timing Analysis (DSTA) does not take into account the effect of process parameter variability on circuit performance, which arouses people's attention to the ability of STA to effectively simulate statistical changes. Therefore, Statistical Static Timing Analysis (SSTA) has been proposed and extensively studied. Traditional SSTA algorithms, such as probabilistic propagation based on Gaussian distribution and Monte Carlo simulation, cannot achieve a high accuracy and good performance. In this paper, a SSTA algorithm considering skew distribution, SD-SSTA, is proposed, which successfully realizes accurate calculation of arrival time and timing margin, and has excellent performance. The paper makes three contributions. (1) We convert the non-Gaussian distribution into a Gaussian Mixture Model (GMM), which fits the real result better than the traditional SSTA algorithms. (2) We consider the influence of skew and introduce Skew Adjustment Factor (SAF) into the calculation of timing margin to ensure that the results are more realistic. (3) We use the name mapping method to reduce the memory consumption of the algorithm, which further improves the algorithm memory performance. Compared with SSTA algorithm based on Gaussian distribution, SD-SSTA algorithm has excellent performance in both accuracy and performance.