An Efficient Pruning Method to Guide the Search of Precision Tests in Statistical Timing Space

As feature sizes continue to decrease, sensitivities of design to process variations have become harder to analyze. Traditional worst-case and nominal timing analyses are not sufficient to accurately characterize these sensitivities. Timing sensitivities can be classified into timing variability, a direct result of process variations, and timing uncertainty, caused by the interaction between some timing-dependent effects and timing variability. Statistical timing analysis is an emerging approach that promises to better quantify timing variability. However, there has been little work focusing on timing uncertainty. Searching for precision tests that bound this uncertainty in design and test is inherently a statistical problem. Using cross-coupling as an example, this work describes a non-statistical framework that utilizes a Boolean satisfiability solver (SAT), ordered binary decision diagrams (OBDD), and timing window based filtering to efficiently prune the search space. Experimental results are presented to explain that such a non-statistical solution is desired and can be effective for practical use