Statistical design and optimization for adaptive post-silicon tuning of MEMS filters

Authors:
Fa Wang;Gokce Keskin;Andrew Phelps;Jonathan Rotner;Xin Li;Gary K. Fedder;Tamal Mukherjee;Lawrence T. Pileggi
Affiliations:
Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA
Venue:
Proceedings of the 49th Annual Design Automation Conference
Year:
2012

Citing 3
Cited 0

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RF CMOS transceivers for cellular telephony

IEEE Communications Magazine

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Abstract

Large-scale process variations can significantly limit the practical utility of microelectro-mechanical systems (MEMS) for RF (radio frequency) applications. In this paper we describe a novel technique of adaptive post-silicon tuning to reliably design MEMS filters that are robust to process variations. Our key idea is to implement a number of redundant MEMS resonators to form an array and then optimally select a subset of these resonators to achieve the desired frequency response. Several new CAD algorithms and methodologies are proposed to optimize and configure the design variables of the proposed MEMS resonator array. A MEMS design example demonstrates that the proposed post-silicon tuning is able to reduce the ripple of the channel filter gain by 7x over other traditional approaches.