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

Ano: 2012 | Volume: 11 | Número: 1

Autores: S.Suganthi, K.Murugesan, S.Raghavan

Palavras-chave: Artificial neural networks, Micro electro mechanical systems, Training algorithms, correlation coefficient.

Resumo Inglês:

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).