A compression strategy for rational macromodeling of large interconnect structures

Rational macromodeling via Vector Fitting algorithms is a standard practice in Signal and Power Integrity analysis and design flows. However, despite the robustness and reliability of the Vector Fitting scheme, some challenges remain for those applications requiring models with a very large port count. Fully coupled signal and/or power distribution networks may require concurrent modeling of hundreds of simultaneously coupled ports over extended frequency bands. Direct rational fitting is impractical for such structures due to a large computational cost. In this work, we present a compression strategy aimed at representing the dynamic behavior of the structure through few carefully selected "basis functions". We show that model accuracy can be traded for complexity, with full control over approximation errors. Application of standard Vector Fitting to the obtained low-dimensional compressed system leads to the construction of a global state-space macromodel with significantly reduced runtime and memory consumption. Several benchmarks demonstrate the effectiveness of the approach.