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Multiphysics Simulation of RF-MEMS With Quantified Uncertainties

[+] Author Affiliations
Lin Sun, Hojin Kim, Alejandro Strachan

Purdue University, West Lafayette, IN

Sanjay R. Mathur, Jayathi Y. Murthy

University of Texas, Austin, Austin, TX

Paper No. IPACK2013-73175, pp. V001T05A007; 8 pages
doi:10.1115/IPACK2013-73175
From:
  • ASME 2013 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems
  • Volume 1: Advanced Packaging; Emerging Technologies; Modeling and Simulation; Multi-Physics Based Reliability; MEMS and NEMS; Materials and Processes
  • Burlingame, California, USA, July 16–18, 2013
  • Conference Sponsors: Electronic and Photonic Packaging Division
  • ISBN: 978-0-7918-5575-1
  • Copyright © 2013 by ASME

abstract

RF-MEMS devices area complex systems governed by the interaction of a variety of forces, including electrostatics, solid deformation, fluid damping and contact. The performance and reliability of these devices is strongly dependent on device geometry and composition, and also on material microstructure and related properties. In this paper, we consider multiscale simulation of RF MEMS switched. At the device level, we introduce a comprehensive integrated numerical framework to simulate the major governing physics and their interactions. At the micron scale, we develop a mesoscale contact model to describe the history-dependent force-displacement relationships in terms of the surface roughness, the long-range attractive interaction between the two surfaces, and the repulsive interaction between contacting asperities (including elastic and plastic deformation). The inputs to this model are obtained from atomic level simulations and nanoscale surface topography characterization. The mesoscale contact model is integrated in the device-level simulation to predict the pull-in and pull-out behavior of these switches. The uncertainties associated with the simulation are quantified and propagated using a non-intrusive collocation method based on generalized Polynomial Chaos (gPC) expansions. With such a framework, we are able to predict the PDFs of pull-in and pull-out voltage, identify the critical factors that have the most influence on the quantities of interest, and therefore guide resource allocation and risk-informed decision-making.

Copyright © 2013 by ASME

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