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Probabilistic standard cell modeling considering non-Gaussian parameters and correlations

: Lange, A.; Sohrmann, C.; Jancke, R.; Haase, J.; Lorenz, I.; Schlichtmann, U.


Preas, K. ; European Design Automation Association -EDAA-; IEEE Computer Society, Test Technology Technical Council -TTTC-:
Design, Automation and Test in Europe Conference and Exhibition, DATE 2014. Vol.2 : Dresden, Germany, 24 - 28 March 2014; Proceedings
Piscataway, NJ: IEEE, 2014
ISBN: 978-1-4799-3297-9
ISBN: 978-3-9815370-2-4
Design, Automation and Test in Europe Conference & Exhibition (DATE) <17, 2014, Dresden>
Fraunhofer IIS, Institutsteil Entwurfsautomatisierung (EAS) ()

Variability continues to pose challenges to integrated circuit design. With statistical static timing analysis and high-yield estimation methods, solutions to particular problems exist, but they do not allow a common view on performance variability including potentially correlated and non-Gaussian parameter distributions. In this paper, we present a probabilistic approach for variability modeling as an alternative: model parameters are treated as multi-dimensional random variables. Such a fully mul-tivariate statistical description formally accounts for correlations and non-Gaussian random components. Statistical characterization and model application are introduced for standard cells and gate-level digital circuits. Example analyses of circuitry in a 28 nm industrial technology illustrate the capabilities of our modeling approach.