Modeling of actuation voltage of RF MEMS capacitive switches based on RBF ANNs

Abstract

Artificial neural networks (ANNs) have been exploited as an efficient tool in modeling of many electronic devices, among them RF MEMS devices. The design of RF MEMS devices requires determination of their electrical and mechanical characteristics according to the application requirements. ANNs have been proposed to be used for modeling RF MEMS devices and can be used further as an alternative and efficient simulation and optimization tool replacing time consuming simulations in standard electrical and mechanical simulators. The aim of this paper is to investigate possibilities of the radial basis function (RBF) ANNs to be applied for modeling of mechanical characteristics of RF MEMS capacitive switches, relating the switch geometry parameters and the actuation voltage. The achieved results obtained by the developed RBF neural model are compared with the results from the earlier developed multilayer perceptron (MLP) neural model. Moreover, effectiveness and accuracy of these two ANN models are analysed. The results confirm the efficiency of the both modelling approaches.