ANN Model of RF MEMS Lateral SPDT Switches for Millimeter Wave Applications

S. Suganthi, K. Murugesan, S. Raghavan


This paper presents Artificial Neural Network (ANN) implementation for the Radio Frequency (RF) and Mechanical modeling of lateral RF Micro Electro Mechanical System (MEMS) series micro machined Single pole double through (SPDT) switch. We propose an efficient approach based on ANN for analyzing the losses in ON and OFF state of lateral RF MEMS series switch by calculating the S-parameters. The double beam structure has been analyzed in terms of its return, isolation 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 and isolation losses. ANN model were trained with five different training algorithms namely Levenberg-Marquart (LM), Bayesian Regularization (BR), Quasi - Newton (QN), Scaled Conjugate Gradient (SCG) and Conjugate Gradient of Fletcher - Powell (CGF) to obtain better performance and fast convergence. The results from the neural model trained by Levenberg-Marquardt back propagation algorithm are highly agreed with the theoretical results available in the literature. The neural networks shows the better results with the highest correlation coefficient which measures the strength and direction of linear relation between two variables (actual and predicted values) (0.9998) along with lowest root mean square error (MSE) of (0.0039).

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