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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] [ABSTRACT FROM PUBLISHER]

This work aims at developing a computational stochastic multiscale methodology to quantify the uncertainties of the adhesive contact problems due to capillary effects and van der Waals forces in MEMS. Because the magnitudes of the adhesive forces strongly depend on the surface interaction distances, which in turn evolve with the roughness of the contacting surfaces, the involved structural behaviors suffer from a scatter. To numerically predict the probabilistic behaviors of structures involving adhesion, the proposed method introduces stochastic meso-scale random apparent contact forces which can be integrated into a stochastic finite element model. Because the evaluation of their realizations is expensive, a generator for the random apparent contact force using the polynomial chaos expansion is constructed in an efficient way. [ABSTRACT FROM AUTHOR]