Neural Network based realization and circuit analysis of lateral RF MEMS series switch

Radio Frequency microelectromechanical system (RF MEMS) is a relatively new field which has generated a tremendous amount of excitement because of its performance enhancement and low manufacturing cost. RF MEMS switch is a fundamental device that offers wide applications in defense and telecommunication systems. In this paper we propose an efficient approach based on Artificial Neural Network (ANN) for analyzing the losses in ON and OFF state of lateral RF MEMS series switch by calculating the S-parameters. The single beam structure has been analyzed in terms of its return and insertion losses with the variation of its passive circuit component values. The effect of design parameters has been analyzed and the lateral switch was realized with low insertion loss, high return loss, and high isolation. A stand-alone model for optimization of DMTL phase shift is derived efficiently using ANN. The results from the neural models trained by Levenberg-Marquardt algorithm are in very good agreement with the theoretical results available in the literature.