Open AccessHybrid neural lumped element approach in inverse modeling of RF MEMS switches
Author(s) -
Tomislav Ćirić,
Zlatica Marinković,
Rohan Dhuri,
Olivera PronićRančić,
Vera Marković
Publication year - 2020
Publication title -
facta universitatis. series electronics and energetics/facta universitatis. series: electronics and energetics
Language(s) - English
Resource type - Journals
eISSN - 2217-5997
pISSN - 0353-3670
DOI - 10.2298/fuee2001027c
Subject(s) - bridge (graph theory) , inverse , artificial neural network , microelectromechanical systems , electronic engineering , computer science , finite element method , element (criminal law) , topology (electrical circuits) , engineering , electrical engineering , physics , mathematics , structural engineering , artificial intelligence , optoelectronics , medicine , geometry , law , political science
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.