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
Dragon2000: standard-cell placement tool for large industry circuits
Proceedings of the 2000 IEEE/ACM international conference on Computer-aided design
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
Statistical Timing Analysis Considering Spatial Correlations using a Single Pert-Like Traversal
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Statistical Timing Analysis for Intra-Die Process Variations with Spatial Correlations
Proceedings of the 2003 IEEE/ACM international conference on Computer-aided design
Statistical Timing Analysis with Extended Pseudo-Canonical Timing Model
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
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
Timing margin evaluation with a simple statistical timing analysis flow
Journal of Embedded Computing - PATMOS 2007 selected papers on low power electronics
Proceedings of the 2009 International Conference on Computer-Aided Design
On confidence in characterization and application of variation models
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
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State of the art statistical timing analysis (STA) tools often yield less accurate results when timing variables become correlated. Spatial correlation and correlation caused by path reconvergence are among those which are most difficult to deal with. Existing methods treating these correlations will either suffer from high computational complexity or significant errors.In this paper, we present a new sensitivity pruning method which will significantly reduce the computational cost to consider path reconvergence correlation. We also develop an accurate and efficient model to deal with the spatial correlation.