Timing analysis is a key step in the digital design process. By modeling device delay variations statistical static timing analysis (SSTA) reduces pessimism compared to traditional static timing analysis (STA). However, it ignores the circuit's logic which causes some timing paths to never, or only rarely, be sensitized. We introduce a general timing analysis approach and tool to calculate the probability that individual timing paths are sensitized, enabling the calculation of bounding delay distributions over all input combinations. We show how this analysis is related to the well-known #SAT problem and present approaches to improve scalability, achieving, on average, results 75% to 37% less pessimistic than STA while running 569 to 16 times faster than Monte-Carlo timing simulation.

Timing analysis is a cornerstone of the digital design process. Statistical Static Timing Analysis was introduced to reduce pessimism by modelling device delay variations. However it ignores circuit logic, which may cause some timing paths to never or only rarely be sensitized. We introduce a general timing analysis approach and tool to calculate the probability that individual timing paths are sensitized, enabling the calculation of bounding delay distributions over all input combinations. We show the connection to the well-known #SAT problem and present approaches to improve scalability, achieving average results 46 to 32% less pessimistic than Static Timing Analysis while running 14.6 to 44.0 times faster than Monte-Carlo timing simulation.