A statistical static timing analysis considering correlations between delays
Proceedings of the 2001 Asia and South Pacific Design Automation Conference
A general probabilistic framework for worst case timing analysis
Proceedings of the 39th annual Design Automation Conference
Proceedings of the 39th annual Design Automation Conference
Block-based Static Timing Analysis with Uncertainty
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
"AU: Timing Analysis Under Uncertainty
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
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Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
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Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Statistical delay computation considering spatial correlations
ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
Statistical timing analysis using bounds and selective enumeration
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Static timing: back to our roots
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Incremental criticality and yield gradients
Proceedings of the conference on Design, automation and test in Europe
Non-Gaussian statistical timing analysis using second-order polynomial fitting
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Proceedings of the 2009 International Conference on Computer-Aided Design
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Block based statistical timing analysis (STA) tools often yield less accurate results when timing variables become correlated due to global source of variations and path reconvergence. To the best of our knowledge, no good solution is available handling both types of correlations simultaneously.In this paper, we present a novel statistical timing algorithm, AMECT (Asymptotic MAX/MIN approximation & Extended Canonical Timing model), that produces accurate timing estimation by handling both types of correlations simultaneously. An extended canonical timing model is developed to evaluate and decompose correlations between arbitrary timing variables. And an intelligent pruning method is designed enabling trade-off runtime with accuracy.Tested with ISCAS benchmark suites, AMECT shows both high accuracy and high performance compared with Monte Carlo simulation results: with distribution estimation error