Fast statistical timing analysis by probabilistic event propagation
Proceedings of the 38th annual Design Automation Conference
Uncertainty-aware circuit optimization
Proceedings of the 39th annual 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
Statistical timing for parametric yield prediction of digital integrated circuits
Proceedings of the 40th annual Design Automation Conference
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
Block-based Static Timing Analysis with 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 using bounds and selective enumeration
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Criticality computation in parameterized statistical timing
Proceedings of the 43rd annual Design Automation Conference
Proceedings of the 2007 ACM/SIGDA 15th international symposium on Field programmable gate arrays
Use of statistical timing analysis on real designs
Proceedings of the conference on Design, automation and test in Europe
A novel criticality computation method in statistical timing analysis
Proceedings of the conference on Design, automation and test in Europe
Proceedings of the 44th annual Design Automation Conference
Statistical performance modeling and optimization
Foundations and Trends in Electronic Design Automation
Clustering based pruning for statistical criticality computation under process variations
Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design
Utilizing redundancy for timing critical interconnect
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Incremental criticality and yield gradients
Proceedings of the conference on Design, automation and test in Europe
An Evaluation Method of the Number of Monte Carlo STA Trials for Statistical Path Delay Analysis
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Statistical ordering of correlated timing quantities and its application for path ranking
Proceedings of the 46th Annual Design Automation Conference
Using randomization to cope with circuit uncertainty
Proceedings of the Conference on Design, Automation and Test in Europe
Fast statistical timing analysis for circuits with post-silicon tunable clock buffers
Proceedings of the International Conference on Computer-Aided Design
NBTI-aware circuit node criticality computation
ACM Journal on Emerging Technologies in Computing Systems (JETC)
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The large-scale process and environmental variations for today's nanoscale ICs are requiring statistical approaches for timing analysis and optimization. Significant research has been recently focused on developing new statistical timing analysis algorithms, but often without consideration for how one should interpret the statistical timing results for optimization. In this paper (Li et al., 2005) we demonstrate why the traditional concepts of slack and critical path become ineffective under large-scale variations, and we propose a novel sensitivity-based metric to assess the "criticality" of each path and/or arc in the statistical timing graph. We define the statistical sensitivities for both paths and arcs, and theoretically prove that our path sensitivity is equivalent to the probability that a path is critical, and our arc sensitivity is equivalent to the probability that an arc sits on the critical path. An efficient algorithm with incremental analysis capability is described for fast sensitivity computation that has a linear runtime complexity in circuit size. The efficacy of the proposed sensitivity analysis is demonstrated on both standard benchmark circuits and large industry examples.