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.
|Number of pages||14|
|Journal||Journal of Applied and Computational Mechanics|
|Publication status||Published - 2022|
- Artificial neural network.
- Pull-in instability
ASJC Scopus subject areas
- Computational Mechanics
- Mechanical Engineering