Determining RF MEMS switch parameter by neural networks

Author

Mafinejad, Yasser ; Kouzani, Abbas Z. ; Mafinezhad, Khalil

Author_Institution

Sch. of Eng., Deakin Univ., Geelong, VIC, Australia

fYear

2009

fDate

23-26 Jan. 2009

Firstpage

1

Lastpage

5

Abstract

A challenge in designing a RF MEMS switch is the determination of its parameters to satisfy the application requirements. Often this is done through a set of comprehensive time consuming simulations. This paper employs neural networks and develops a supervised learner that is capable of determining S11 parameter for a RF MEMS shunt switch. The inputs are the length its L and the height of its gap. The outputs are S11s for eight different frequency points from 0 to V band. The developed learner helps prevent repetitive simulations when designing the specified switch. Simulation results are presented.

Keywords

electronic engineering computing; learning (artificial intelligence); microswitches; neural nets; RF MEMS shunt switch; S11 parameter determination; neural networks; supervised learner; Contacts; Electrodes; Electrostatics; Fabrication; Neural networks; Radio frequency; Radiofrequency microelectromechanical systems; Springs; Switches; Voltage; MEMS; RF; neural networks; switch;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON 2009 - 2009 IEEE Region 10 Conference

Conference_Location

Singapore

Print_ISBN

978-1-4244-4546-2

Electronic_ISBN

978-1-4244-4547-9

Type

conf

DOI

10.1109/TENCON.2009.5396083

Filename

5396083