Parameterized macromodeling for analog system-level design exploration

Authors:
Jian Wang;Xin Li;Lawrence T. Pileggi
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
Proceedings of the 44th annual Design Automation Conference
Year:
2007

Citing 12
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Abstract

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.