Assessment of dielectric charging in micro-electro-mechanical system capacitive switches

Koutsoureli, M.; Michalas, Loukas; Papaioannou, G.

doi:10.2298/fuee1303239k

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…MEMS devices have the ability to sense, control and actuate on micro scale, and generate effects on macro scale. According to variety and diversity of RF MEMS technology functionalities, they have wide applicability for the new generation of communication system components, like switches and varactors (variable capacitors), resonators, complex networks, reconfigurable filters, phase shifters, impedance matching tuners and programmable step attenuators [1][2][3][4][5][6][7][8][9]. In the recent time, the RF MEMS technology has found applications for Internet of Things (IoT), Internet of Everything (IoE), Tactile Internet and 5G telecommunications [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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

Ćirić

Marinković

Dhuri³

et al. 2020

FACTA U EE

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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.

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Section: Introductionmentioning

confidence: 99%

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

Ćirić

Marinković

Dhuri³

et al. 2020

FACTA U EE

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“…Current research of RF MEMS switches is mostly concentrated on various new structures, new materials or processes in devices [13]- [16], while optimization analysis of MEMS devices lacks enough study. A standard approach to obtain RF MEMS switch electrical characteristics is to use full-wave numerical methods in electromagnetic (EM) simulators.…”

Section: Introductionmentioning

confidence: 99%

Artifical neural networks in RF MEMS switch modelling

et al. 2016

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The increased growth of the applications of RF MEMS switches in modern communication systems has created an increased need for their accurate and efficient models. Artificial neural networks have appeared as a fast and efficient modelling tool providing similar accuracy as standard commercial simulation packages. This paper gives an overview of the applications of artificial neural networks in modelling of RF MEMS switches, in particular of the capacitive shunt switches, proposed by the authors of the paper. Models for the most important switch characteristics in electrical and mechanical domains are considered, as well as the inverse models aimed to determine the switch bridge dimensions for specified requirements for the switch characteristics.

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Assessment of dielectric charging in micro-electro-mechanical system capacitive switches

Cited by 2 publications

References 14 publications

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

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

Artifical neural networks in RF MEMS switch modelling

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