Stochastic Multiscale Model of MEMS Stiction Accounting for High-Order Statistical Moments of Non-Gaussian Contacting Surfaces

Stiction is a failure mode of microelectromechanical systems (MEMS) involving permanent adhesion of moving surfaces. Models of stiction typically describe the adhesion as a multiple asperity adhesive contact between random rough surfaces, and they thus require a sufficiently accurate statistical rep...

Full description

Saved in:
Bibliographic Details
Published in:Journal of microelectromechanical systems 2018-04, Vol.27 (2), p.137-155
Main Authors: Hoang, T. V., Wu, L., Golinval, J.-C., Arnst, M., Noels, L.
Format: Article
Language:English
Subjects:
adhesion
Adhesives
capillary
Engineering, computing & technology
Generators
Ingénierie mécanique
Ingénierie, informatique & technologie
maximum entropy principle
Mechanical engineering
Micromechanical devices
Multiscale contact
Rough surfaces
stochastic
Stochastic processes
Surface roughness
Uncertainty
van der Waals
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Stiction is a failure mode of microelectromechanical systems (MEMS) involving permanent adhesion of moving surfaces. Models of stiction typically describe the adhesion as a multiple asperity adhesive contact between random rough surfaces, and they thus require a sufficiently accurate statistical representation of the surface, which may be non-Gaussian. If the stiction is caused primarily by multiple asperity adhesive contact in only a small portion of the apparent area of the contacting surfaces, the number of adhesive contacts between asperities may not be sufficiently statistically significant for a homogenized model to be representative. We have proposed a probabilistic multiscale model of multiple asperity adhesive contact that can capture the uncertainty in a stiction behavior. Whereas the previous paper considered Gaussian random rough surfaces, the aim of this paper is to extend this probabilistic multiscale model to non-Gaussian random rough surfaces whose probabilistic representation accounts for the high-order statistical moments of the surface height. The probabilistic multiscale model thus obtained is validated by means of a comparison with experimental data of stiction tests of cantilever beams reported in the literature. [2017-0215]
ISSN:1057-7157
1941-0158
1941-0158
DOI:10.1109/JMEMS.2018.2797133