Efficient Linear Macromodeling via Discrete-Time Time-Domain Vector Fitting

Abstract

We present a discrete-time time-domain vector fitting algorithm, called TD-VFz, for rational function macromodeling of port-to-port responses with discrete time-sampled data. The core routine involves a two-step pole refinement process based on a linear least-squares solve and an eigenvalue problem. Applications in the macromodeling of practical circuits demonstrate that TD-VFz exhibits fast computation, excellent accuracy, and robustness against noisy data. We also utilize an quasi-error bound unique to the discrete-time setting to facilitate the determination of approximant model order.