Accurate passivity-enforced macromodeling for RF circuits via iterative zero/pole update based on measurement data
Passive macromodeling for RF circuit blocks is a critical task to facilitate efficient system-level simulation for large-scale RF systems (e.g., wireless transceivers). In this paper we propose a novel algorithm to find the optimal macromodel that minimizes the modeling error based on measurement data, while simultaneously guaranteeing passivity. The key idea is to attack the passive macromodeling problem by solving a sequence of convex semi-definite programming (SDP) problems. As such, the proposed method can iteratively find the optimal poles and zeros for macromodeling. Our experimental results with several commercial RF circuit examples demonstrate that the proposed macromodeling method reduces the modeling error by 1.31-2.74× over other conventional approaches.
