Parameterized Macromodeling for Analog System-Level Design Exploration

In this paper we propose a novel parameterized macromodeling technique for analog circuits. Unlike traditional macromodels that are only extracted for a small variation space, our proposed approach captures a significantly larger analog design space to facilitate system-level design exploration. Combining a novel piece-wise approximation algorithm and a new multi-point model- order-reduction approach, the proposed method generates compact macromodels covering the entire feasible design space. Our experiments demonstrate that using such models can achieve more than 60 x speed-up while incurring less than 4 % overall error when varying design parameters by an order of magnitude.