Mortaza Aliasghary - Electrical Engineering Department, Faculty of Industrial Technologies, Urmia University of Technology (UUT), Urmia, Postal Code: ۵۷۱۶۶-۱۷۱۶۵ Iran
Hamed Mobki - Department of Engineering, German University of Technology in Oman, Athaibah, Muscat, Postal Code: ۱۸۱۶, Oman‎
Hassen M. OUAKAD - Mechanical and Industrial Engineering Department, Sultan Qaboos University, Al Khoudh, Muscat, Postal Code:۱۲۳, Oman

Artificial Neural Networks (ANN) are designed to evaluate the pull-in voltage of MEMS switches. The mathematical model of a micro-switch subjected to electrostatic force is preliminarily illustrated to get the relevant equations providing static deflection and pull-in voltage. Adopting the Step-by-Step Linearization Method together with a Galerkin-based reduced order model, numerical results in terms of pull-in voltage are obtained to be employed in the training process of ANN. Then, feed forward back propagation ANNs are designed and a learning process based on the Levenberg-Marquardt method is performed. The ability of designed neural networks to determine pull-in voltage have been compared with previous results presented in experimental and theoretical studies and it has been shown that the presented method has a good ability to approximate the threshold voltage of micro switch. Furthermore, the geometric and physical effect of the micro-switch on the pull-in voltage was also examined using these designed networks and relevant findings were provided.

MEMS, Pull-in instability, Electrostatic, Artificial Neural Network‎