Artificial Neural Network based Design of RF MEMS Capacitive Shunt Switches

Authors

  • Zlatica Marinkovis University of Niš, Faculty of Electronic Engineering Aleksandra Medvedeva 14, 18000 Niš, Serbia
  • Taeyoung Kim Lehrstuhl für Hochfrequenztechnik, TU München Arcisstrasse 21, 80333 München, Germany
  • Vera Markovis University of Niš, Faculty of Electronic Engineering Aleksandra Medvedeva 14, 18000 Niš, Serbia
  • Marija Milijis University of Niš, Faculty of Electronic Engineering Aleksandra Medvedeva 14, 18000 Niš, Serbia
  • Olivera Pronis-Ranšis University of Niš, Faculty of Electronic Engineering Aleksandra Medvedeva 14, 18000 Niš, Serbia
  • Tomislav Siris University of Niš, Faculty of Electronic Engineering Aleksandra Medvedeva 14, 18000 Niš, Serbia
  • Larissa Vietzorreck Lehrstuhl für Hochfrequenztechnik, TU München Arcisstrasse 21, 80333 München, Germany

Keywords:

Artificial neural networks, computer aided design, RF MEMS

Abstract

Artificial neural networks (ANNs) have appeared as a very efficient alternative to time consuming full-wave simulations of electrical characteristics of RF MEMS. In this paper, a new ANN based method to be used in the design of RF MEMS devices is proposed. ANNs are trained to model dependence of the scattering parameters and the resonant frequency of an RF MEMS switch on the switch geometrical parameters, as well as to perform the opposite procedure, i.e., to determine values of the geometrical parameters to achieve the desired electrical resonant frequency. The developed models can be used for fast simulation and optimization of the switch characteristics replacing time consuming procedures in full-wave simulators, which leads to a significant reduction of time needed for the device design.

Downloads

Download data is not yet available.

References

G. M. Rebeiz, RF MEMS Theory, Design, and Technology. New York, USA, Wiley, 2003.

T. Itoh, G. Haddad, and J. Harvey, “Microelectromechanical switches for RF applications,” RF Technologies for Low Power Wireless Communications, pp. 349-382, 2001.

R. Ramadoss, S. Lee, Y. C. Lee, V. M. Bright, and K. C. Gupta, “MEMS capacitive series switch fabricated using PCB technology,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 17, no. 4, pp. 387-397, July 2007.

M. Daneshmand, A. A. Fomani, M. M. Fahmi, J. A. Ruiz-Cruz, and R. R. Mansour, “MEMS multiport switches and switch matrices for satellite applications,” IEEE MTT-S International Microwave Symposium Digest, Montreal, Canada, pp. 1-3, June 2012.

Y. Lee, Y Park, F. Niu, B. Bachman, K. C. Gupta, and D. Filipovic, “Artificial neural network modeling of RF MEMS resonators,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 14, no. 4, pp. 302-316, July 2004.

Y. Lee and D. S. Filipovic, “Combined fullwave/ANN based modeling of MEMS switches for RF and microwave applications,” IEEE Antennas and Propagation Society International Symposium, Washington DC, pp. 85-88, July 2005.

Y. Lee and D. S. Filipovic, “ANN based electromagnetic models for the design of RF MEMS switches,” IEEE Microwave Wireless Component Letters, vol. 15, no. 11, pp. 823-825, Nov. 2005.

Y. Mafinejad, A. Z. Kouzani, and K. Mafinezhad, “Determining RF MEMS switch parameter by neural networks,” IEEE Region 10 Conference TENCON 2009, Singapore, pp. 1-5, Jan. 2009.

Y. Lee, Y Park, F. Niu, and D. Filipovic, “Artificial neural network based macromodeling approach for two-port RF MEMS resonating structures,” IEEE Proceedings of Networking, Sensing and Control, Tucson, pp. 261-266, Mar. 2005.

Y. Lee, Y Park, F. Niu, and D. Filipovic, “Design and optimization of RF ICs with embedded linear macromodels of multiport MEMS devices,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 17, no. 2, pp. 196-209, Mar. 2007.

Y. Gong, F. Zhao, H. Xin, J. Lin, and Q. Bai, “Simulation and optimal design for RF MEMS cantilevered beam switch,” International Conference on Future Computer and Communication (FCC '09), Wuhan, pp. 84-87, June 2009.

S. DiNardo, P. Farinelli, F. Giacomozzi, G. Mannocchi, R. Marcelli, B. Margesin, P. Mezzanotte, V. Mulloni, P. Russer, R. Sorrentino, F. Vitulli, and L. Vietzorreck, “Broadband RFMEMS based SPDT,” European Microwave Conference, Manchester, pp. 1727-1730, Sep. 2006.

Q. J. Zhang and K. C. Gupta, Neural Networks for RF and Microwave Design. Boston, MA, USA, Artech House, 2000.

C. Christodoulou and M. Gerogiopoulos, Applications of Neural Networks in Electromagnetics. Artech House, Inc. Norwood, MA, USA, 2000.

P. Burrascano, S. Fiori, and M. Mongiardo, “A review of artificial neural network applications in microwave computer-aided design,” International Journal of RF and Microwave Computer-Aided Engineering, vol. 9, no. 3, pp. 158-174, May 1999.

S. S. Gultekin, K. Guney, and S. Sagiroglu, “Neural networks for the calculation of bandwidth of rectangular microstrip antennas,” Applied Computational Electromagnetics Society Journal, vol. 18, no. 2, pp. 110-120, July 2003.

R. Ghayoula, N. Fadlallah, A. Gharsallah, and M. Rammal, ”Design, modeling, and synthesis of radiation pattern of intelligent antenna by artificial neural networks,” Applied Computational Electromagnetics Society Journal, vol. 23, no. 4, pp. 336- 344, Dec. 2008.

J. E. Rayas-Sanchez, “EM-based optimization of microwave circuits using artificial neural networks: The state-of-the-art,” IEEE Transactions on Microwave Theory and Technique, vol. 52, no. 1, pp. 420-435, Jan. 2004.

H. Kabir, Y. Cao, and Q. Zhang, “Advances of neural network modeling methods for RF/ microwave applications,” Applied Computational Electromagnetics Society Journal, vol. 25, no. 5, pp. 423-432, May 2010.

Advanced Design System 2009, Agilent Technologies.

Downloads

Published

2021-08-18

How to Cite

[1]
Z. . Marinkovis, “Artificial Neural Network based Design of RF MEMS Capacitive Shunt Switches”, ACES Journal, vol. 31, no. 07, pp. 756–764, Aug. 2021.

Issue

2016: Vol 31 Iss 7

Section

General Submission