RF MEMS switches have been efficiently exploited in various applications in
communication systems. As the dimensions of the switch bridge influence the switch
behaviour, during the design of a switch it is necessary to perform inverse
modeling, i.e. to determine the bridge dimensions to ensure the desired
switch characteristics, such as the resonant frequency. In this paper a
novel inverse modeling approach based on combination of artificial neural
networks and a lumped element circuit model has been considered. This
approach allows determination of the bridge fingered part length for the
given resonant frequency and the bridge solid part length, generating at the
same time values of the elements of the switch lumped element model.
Validity of the model is demonstrated by appropriate numerical examples.
Hybrid neural lumped element approach in inverse modeling of RF MEMS switches