A probabilistic model for predicting the uncertainties of the humid stiction phenomenon on hard materials

Stiction is a major failure in microelectromechanical system (MEMS) devices in which two contacting surfaces can remain stuck together because of the adhesive forces. Due to the difference between the surfaces roughness and the adhesive force range, the real contact areas are usually smaller than the apparent one, resulting in a scatter in the adhesive forces. Consequently, the stiction is an uncertain phenomenon. In this work, we develop a probabilistic model to predict the uncertainties of stiction due to the capillary forces acting on stiff materials. This model contains two levels: at the deterministic level, the model can predict the pull-out adhesive contact forces for a given surface topology; at the probabilistic level, the model generates independent identically distributed surfaces on which the deterministic solution can be applied to evaluate the uncertainties related to the stiction phenomenon. An alternative to the statistical method is proposed to evaluate the adhesive forces.Numerical surfaces are generated from a psd function defined from AFM measurements.An asperity approach is applied on these surfaces to compute the adhesive forces.The method is able to capture uncertainties and size effects in the adhesive forces.The method can be used to predict the stiction in MEMS devices.