Sparse Macromodeling for Parametric Nonlinear Networks

Model-order reduction has proven to be an effective tool for addressing the simulation complexities of the modern microsystem such as the ones arising due to large interconnect networks. Traditional model-order reduction methods are frequency-domain methods and are, therefore, limited to linear networks. Recently, time-domain model-order reduction was developed extending this concept to nonlinear macromodels. However, the resulting reduced nonlinear macromodel is dense, which reduces the efficiency of the simulation. In this paper, a nonlinear parametric formulation suitable for sparsification is presented. This results in an efficient reduced-order nonlinear macromodel, which is sparse, and is valid over a range of parameter values, and is thus suitable for optimization and design space exploration. Numerical examples are shown to illustrate the accuracy and efficiency of the proposed method