ANN based inverse modeling of RF MEMS capacitive switches

Abstract

RF MEMS switches have been efficiently applied in various applications in communication systems. Therefore, there is a need for reliable and accurate models of RF MEMS switches. Artificial neural networks (ANNs) have been appeared as very efficient alternative to time consuming full-wave and/or mechanical simulations of RF MEMS devices. However, to optimize the switch geometry it is usually necessary to perform certain optimization procedures. In this paper the development of ANN based procedures to be used as a feed-forward tool for determination of the switch geometrical parameters avoiding optimizations is proposed. The proposed procedure is developed for determination of the length of the bridge fingered part of a capacitive switch to achieve the desired electrical resonance frequency or the necessary actuation voltage.