Improving the efficiency of passivity compensation schemes via adaptive sampling

A new algorithm for the enforcement of passivity for large lumped macromodels is presented. An accuracy-controlled adaptive frequency sampling combined with an iterative multishift Arnoldi scheme leads to a fast computation of the imaginary Hamiltonian eigenvalues, which are perturbed until passivity is reached. Up to two orders of magnitude speedup factors are achieved with respect to previous formulations.