Artifical neural networks in RF MEMS switch modelling

Abstract

The increased growth of the applications of RF MEMS switches in modern communication systems has created an increased need for their accurate and efficient models. Artificial neural networks have appeared as a fast and efficient modelling tool providing similar accuracy as standard commercial simulation packages. This paper gives an overview of the applications of artificial neural networks in modelling of RF MEMS switches, in particular of the capacitive shunt switches, proposed by the authors of the paper. Models for the most important switch characteristics in electrical and mechanical domains are considered, as well as the inverse models aimed to determine the switch bridge dimensions for specified requirements for the switch characteristics.