Accurate modeling of low actuation voltage RFMEMS switches using artificial neural networks

Pak, Amin, Mafinejad, Yasser, Kouzani, Abbas, Nabovati, Hooman and Mafinezhad, Khalil 2012, Accurate modeling of low actuation voltage RFMEMS switches using artificial neural networks, in ISCAS 2012 : Proceedings of the 2012 IEEE International Symposium on Circuits and Systems, IEEE, Piscataway, N. J., pp. 3282-3284.

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Title Accurate modeling of low actuation voltage RFMEMS switches using artificial neural networks
Author(s) Pak, Amin
Mafinejad, Yasser
Kouzani, Abbas
Nabovati, Hooman
Mafinezhad, Khalil
Conference name IEEE International Symposium on Circuits and Systems. Conference (2012 : Seoul, Korea)
Conference location Seoul, Korea
Conference dates 20-23 May. 2012
Title of proceedings ISCAS 2012 : Proceedings of the 2012 IEEE International Symposium on Circuits and Systems
Editor(s) [Unknown]
Publication date 2012
Conference series IEEE International Symposium on Circuits and Systems. Conference
Start page 3282
End page 3284
Total pages 3
Publisher IEEE
Place of publication Piscataway, N. J.
Keyword(s) accurate modeling
electromagnetic simulation
low actuation voltage
network prediction
parametric modeling
return loss
RF characteristics
RF-MEMS switches
Summary This paper presents a fast and accurate method for extracting the scattering parameters of a RF MEMS switch by using its essential parameters. A neural network is developed for parametric modeling of the switch. The essential parameters of the switch are analyzed in terms of its return loss and isolation with variation of its geometrical component values. Simulation results show that the proposed approach can be used to accurately model the RF characteristics of RF-MEMS switches. The results show good agreement between the neural network prediction and electromagnetic simulations.
ISBN 1467302198
9781467302197
Language eng
Field of Research 091306 Microelectromechanical Systems (MEMS)
Socio Economic Objective 861701 Network Infrastructure Equipment
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2012, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049584

Document type: Conference Paper
Collection: School of Engineering
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