Proceedings of the 39th annual Design Automation Conference
Finding a Small Set of Longest Testable Paths that Cover Every Gate
ITC '02 Proceedings of the 2002 IEEE International Test Conference
First-order incremental block-based statistical timing analysis
Proceedings of the 41st annual Design Automation Conference
Correlation-preserved non-gaussian statistical timing analysis with quadratic timing model
Proceedings of the 42nd annual Design Automation Conference
Variation-aware performance verification using at-speed structural test and statistical timing
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Statistical path selection for at-speed test
Proceedings of the 2008 IEEE/ACM International Conference on Computer-Aided Design
Critical path selection for delay fault testing based upon a statistical timing model
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Pre-ATPG path selection for near optimal post-ATPG process space coverage
Proceedings of the 2009 International Conference on Computer-Aided Design
Statistical path selection for at-speed test
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
What is the statistical method for at-speed testing?
ACM SIGDA Newsletter
On confidence in characterization and application of variation models
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Testability driven statistical path selection
Proceedings of the 48th Design Automation Conference
Order statistics for correlated random variables and its application to at-speed testing
ACM Transactions on Design Automation of Electronic Systems (TODAES)
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Increasingly large process variations make selection of a set of critical paths for at-speed testing essential yet challenging. This paper proposes a novel multilayer process space coverage metric to quantitatively gauge the quality of path selection. To overcome the exponential complexity in computing such a metric, this paper reveals its relationship to a concept called order statistics for a set of correlated random variables, efficient computation of which is a hitherto open problem in the literature. This paper then develops an elegant recursive algorithm to compute the order statistics (or the metric) in provable linear time and space. With a novel data structure, the order statistics can also be incrementally updated. By employing a branch-and-bound path selection algorithm with above techniques, this paper shows that selecting an optimal set of paths for a multi-million-gate design can be performed efficiently. Compared to the state-of-the-art, experimental results show both the efficiency of our algorithms and better quality of our path selection.