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

Abstract

Abstract-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.

Toward stochastic multiscale methods in continuum solid mechanics

“…Because the surface roughness is of random nature, the apparent force evolution at the meso-scale exhibits uncertainties. Hoang et al (2017Hoang et al ( , 2018 have generated random surfaces, standing for micro-scale realisations w k ∈ W, from which the apparent contact force evolution F M (d M ; X, w k ) could be predicted from a micromechanics model standing as high-fidelity model. Clearly the evolution is non-linear with the separation distance d M , and, in order to perform stochastic analyses of the MEMS behaviour, a stochastic low fidelity model of the adhesive contact force was developed.…”

“…The vectors q k M correspond to observations of the random vector Q M : W → 4 . Hoang et al (2017Hoang et al ( , 2018 have shown that two neighbouring SVEs ω(X; w k ) and ω(X ; w k ) yield spatially uncorrelated apparent properties Q M . Following the discussion in Section 5.1.4, the apparent properties were thus considered as random vectors and not random vector fields.…”

Convolutional Neural Networks Combined with Machine Vision for Mechanical Compressor Defect Detection

“…Through the wavelet packet decomposition and reconstruction of compressor vibration signal, and the compressor fault diagnosis method under the combination of neural network, the diagnosis accuracy can reach 97% [3]. The combination of adaptive bistable random justice and multi-scale noise demodulation can effectively identify the weak vibration caused by the blade crack of centrifugal compressor [4]. The compressor fault diagnosis based on deep learning and local mean decomposition can learn the fault features adaptively, and the corresponding classification accuracy can reach 92.72% under the condition of low signal-to-noise ratio (SNR) [5].…”

Influence of surface roughness on the adhesion hysteresis of nano thin film

“…The phenomenon of adhesive contact widely existing in the micro/nano-electro-mechanical system (MEMS/ NEMS) is an important factor to determine the performance and reliability of MEMS/NEMS [1,2]. It has a serious impact on the precision and stability of the system, and even directly causes irreversible damage.…”