System-level characterization of bias noise effects on electrostatic RF MEMS tunable filters

Xiaoguang Liu, Birck Nanotechnology Center, Purdue University
Kenle Chen, Birck Nanotechnology Center, Purdue University
Linda P.B. Katehi, Birck Nanotechnology Center, Purdue University
William J. Chappell, Birck Nanotechnology Center, Purdue University
Dimitrios Peroulis, Birck Nanotechnology Center, Purdue University

Date of this Version

1-23-2011

Citation

System-level characterization of bias noise effects on electrostatic RF MEMS tunable filters Xiaoguang Liu; Kenle Chen; Linda P. B. Katehi; William J. Chappell; Dimitrios Peroulis 2011 IEEE 24th International Conference on Micro Electro Mechanical Systems Year: 2011 Pages: 797 - 800

Abstract

This paper presents the first system-level characterization of the effects of bias noise on the performance of high-Q electrostatic RF MEMS tunable filters. By looking at the system-level performance of such a tunable filter, this paper shows that bias noise, if not well controlled, can degrade the RF performance of the tunable filter. Quantified by error vector magnitude measurement, such system level degradation due to bias noise is found to be dependent on the frequency and amplitude of the noise signals.

Discipline(s)

Nanoscience and Nanotechnology

 

Share