Parametric Test Metrics Estimation Using Non-gaussian Copulas

The evaluation of parametric test metrics for analog/RF test techniques requires an accurate multivariate statistical model of output parameters of the device under test, namely performances and test measurements. In this paper, we will use Copulas theory for deriving such a model. A copulas-based model separates the dependencies between these output parameters from their marginal distributions, providing a complete and scale-free description of dependence that is more suitable to be modeled using well known multivariate parametric laws. Previous works have used Gaussian copulas for modeling the dependencies between the output parameters for some types of devices (e.g RF LNA). This paper will illustrate the use of Archimedean copulas for modeling non-Gaussian dependencies. In particular, a Clayton copula will be used to model the dependencies between the output parameters of a case-study test technique for CMOS imagers. Parametric test metrics such as pixel false acceptance and false rejection will be estimated using the derived model.