Sparse macromodels for parametric networks

Model order reduction (MOR) has proven to be an effective tool in combatting the computational complexities that arise from the simulation of large interconnect networks. Furthermore, parametric model order reduction (PMR) was recently developed for extending this concept to optimization and design space exploration of interconnect networks. The difficulty with current PMR methods is that the reduced macromodel is dense which reduces the efficiency of the simulation. In this paper a new formulation is proposed which allows for the sparsification of the reduced parametric macromodel, thus resulting in significant CPU cost saving as is demonstrated in the examples