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
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
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
Transistor sizing of custom high-performance digital circuits with parametric yield considerations
Proceedings of the 47th Design Automation Conference
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Advances in Computation of the Maximum of a Set of Gaussian Random Variables
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
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The increasing significance of variability in modern sub-micron manufacturing process has led to the development and use of statistical techniques for chip timing analysis and optimization. Statistical timing involves fundamental operations like statistical-add, sub, max and min to propagate timing information (modeled as random variables with known probability distributions) through a timing graph model of a chip design. Although incremental timing during optimization updates timing information of only certain parts of the timing-graph, lack of established reversible statistical max or min techniques forces more-than-required computations. This paper describes the concept of reversible statistical max and min for correlated Gaussian random variables, and suggests potential applications to statistical timing. A formal proof is presented to establish the uniqueness of reversible statistical max. Experimental results show run-time savings when using the presented technique in the context of chipslack computation during incremental timing optimization.