First-order incremental block-based statistical timing analysis
Proceedings of the 41st annual Design Automation Conference
STAC: statistical timing analysis with correlation
Proceedings of the 41st annual Design Automation Conference
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
Proceedings of the 42nd annual Design Automation Conference
Correlation-aware statistical timing analysis with non-gaussian delay distributions
Proceedings of the 42nd annual Design Automation Conference
Correlation-preserved non-gaussian statistical timing analysis with quadratic timing model
Proceedings of the 42nd annual Design Automation Conference
A framework for statistical timing analysis using non-linear delay and slew models
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
Non-Gaussian statistical timing analysis using second-order polynomial fitting
Proceedings of the 2008 Asia and South Pacific Design Automation Conference
Non-Gaussian statistical timing analysis using second-order polynomial fitting
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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Most of the existing statistical static timing analysis (SSTA) algorithms assume that the process parameters of have been given with 100% confidence level or zero errors and are preferable Gaussian distributions. These assumptions are actually quite questionable and require careful attention.In this paper, we aim at providing solid statistical analysis methods to analyze the measurement data on testing chips and extract the statistical distribution, either Gaussian or non-Gaussian which could be used in advanced SSTA algorithms for confidence interval or error bound information.Two contributions are achieved by this paper. First, we develop a moment matching based quadratic function modeling method to fit the first three moments of given measurement data in plain form which may not follow Gaussian distributions. Second, we provide a systematic way to analyze the confident intervals on our modeling strategies. The confidence intervals analysis gives the solid guidelines for testing chip data collections. Extensive experimental results demonstrate the accuracy of our algorithm.