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 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.
In [Hoang et al., A computational stochastic multiscale methodology for MEMS structures involving adhesive contact, Tribology International, 110:401-425, 2017], the authors have proposed a probabilistic multiscale model of multiple asperity adhesive contact that can capture the uncertainty in stiction behavior. Whereas the previous paper considered Gaussian random rough surfaces, the aim of the present 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.