Abstract
We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
C. Gianfagna, M. Swaminathan, P.M. Raj, R. Tummala, and G. Antonini, Nanotechnology Materials and Devices Conference (NMDC), 2015 IEEE 10th, pp. 1–5. doi: 10.1109/NMDC.2015.7439256.
K. Han, M. Swaminathan, R. Pulugurtha, H. Sharma, R. Tummala, S. Yang, and V. Nair, IEEE Antennas Wirel. Propag. Lett. 15, 72 (2016). doi:10.1109/LAWP.2015.2430284.
J. Huang, F. Ma, X. Jiang, H. Wang, and J. Li, J. Magn. Magn. Mater. 331, 151 (2013)
K.N. Rozanov, M.Y. Koledintseva, and J.L. Drewniak, J. Magn. Magn. Mater. 324, 1063 (2012).
Ph. Toneguzzo, O. Acher, G. Viau, F. Fiévet-Vincent, and F. Fiévet, J. Appl. Phys., 81(8), 5546 (1997).
I. Conde-Leborán, D. Serantes, and D. Baldomir, J. Magn. Magn. Mater. 380, 321 (2015).
R. Ramprasad, P. Zurcher, M. Petras, M. Miller, and P. Renaud, J. Appl. Phys. 9, 519 (2004).
G. Pilania, C. Wang, X. Jiang, S. Rajasekaran, and R. Ramprasad, in Sci. Rep. 3, 2810.
K.N. Rozanov, M.Y. Koledintseva, and J.L. Drewniak, URSI International Symposium on Electromagnetic Theory (EMTS) (2010), pp. 584–587. doi:10.1109/URSI-EMTS.2010.5637159
R. Bikky, N. Badi, and A. Bensaoula, COMSOL Conf. 2010.
I.J. Youngs, N. Bowler, K.P. Lymer, and S. Hussain, J. Phys. D Appl. Phys. 38, 188 (2005).
K.F. Young and H.P.R. Frederikse, J. Phys. Chem. Ref. Data 2, 313 (1973). doi:10.1063/1.3253121.
CST Microwave Studio, Computer Simulation Technology. http://www.cst.com/Products/CSTMWS. Accessed 10 July 2015
G. Montavon, M. Rupp, V. Gobre, A. Vazquez-Mayagoitia, K. Hansen, A. Tkatchenko, K.R. Muller, and O. A. von Lilienfeld, New J. Phys. 13 (2013I. doi:10.1088/1367-2630/15/9/095003
S. Haykin, Neural Networks and Learning Machines, 3rd ed. (Upper Saddle River, NJ: Pearson - Prentice Hill, 2009), pp. 1–46.
K.K. Aggarwal, Y. Singh, P. Chandra, and M. Puri, J. Comput. Sci. 4, 505 (2005). doi:10.3844/jcssp.2005.505.509.
D. Michie, D.J. Spiegelhalter, and C.C. Taylor, Machine Learning, Neural and Statistical Classification (Upper Saddle River: Ellis Horwood, 1994), pp. 98–99.
Neural Network Toolbox—Matlab. https://www.mathworks. com/products/neural-network.html. Accessed 10 July 2015
P.M. Raj, H. Sharma, G.P. Reddy, N. Altunyurt, M. Swaminathan, R. Tummala, and V. Nair, J. Electron. Mater. 43, 1097 (2014). doi:10.1007/s11664-014-3025-5.
J.R. Liu, M. Itoh, T. Horikawa, M. Itakura, N. Kuwano, and K. Machida, J. Phys. D Appl. Phys. 37, 2737 (2004).
H. Sharma, S. Jain, P. Markondeya Raj, K.P. Murali, and R. Tummala, J. Electron. Mater. 44, 3819 (2015). doi:10.1007/s11664-015-3801-x.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gianfagna, C., Yu, H., Swaminathan, M. et al. Machine-Learning Approach for Design of Nanomagnetic-Based Antennas. J. Electron. Mater. 46, 4963–4975 (2017). https://doi.org/10.1007/s11664-017-5487-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11664-017-5487-8