Hybrid neural lumped element approach in inverse modeling of RF MEMS switches

Abstract

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.

Damping analysis to improve the performance of shunt capacitive RF MEMS switch

“…Among these, electrostatic actuation offers major advantages [8]. However, there are still a few potential research challenges in electrostatically actuated RF MEMS switches, like improving the reliability, reducing the actuation voltage, and improving the switching time [9,10]. The prior iterative analysis obviously helps to improve the performance of the RF MEMS switches.…”

Daugiasluoksnių puslaidininkinių ir dielektrinių mikrobangų įtaisų tyrimas

“…Vienas iš pavyzdžių -dirbtinių neuronų tinklo naudojimas mikrobangų MEMS komponentų charakteristikų analizei. Autoriai pasitelkia hibridinį analizės metodą, kai pritaikomi du dirbtinių neuronų tinklai, turintys vieną paslėptąjį sluoksnį greitam S parametrų apskaičiavimui (Ciric et al, 2020).…”