Lange, A.A.LangeSohrmann, C.C.SohrmannJancke, R.R.JanckeHaase, J.J.HaaseLorenz, I.I.LorenzSchlichtmann, U.U.Schlichtmann2022-03-122022-03-122014https://publica.fraunhofer.de/handle/publica/38522510.7873/DATE.2014.243Variability 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.en621conference paper