Accurate modeling of low actuation voltage RFMEMS switches using artificial neural networks

Abstract

This paper presents a fast and accurate method for extracting the scattering parameters of a RF MEMS switch by using its essential parameters. A neural network is developed for parametric modeling of the switch. The essential parameters of the switch are analyzed in terms of its return loss and isolation with variation of its geometrical component values. Simulation results show that the proposed approach can be used to accurately model the RF characteristics of RF-MEMS switches. The results show good agreement between the neural network prediction and electromagnetic simulations.I.

Uncertainty analysis of sensitivity of MEMS microphone based on artificial neural network

“…Recently, ANN models have been widely used to simulate electrical or mechanical characteristics of different MEMS devices. They have been applied mostly to the models of pull-in voltage [20,21,22] and S parameter [23,24,25,26,27,28] of RF-MEMS (radio frequency MEMS) switches, to resonant frequency [29] and spurious modes [30] of RF-MEMS resonator, and to S parameter [31,32,33] of RF-MEMS phase shifter. Uncertainty analysis is the key technique and research focus in the MEMS optimization.…”