Journal of Electronic Materials

, Volume 46, Issue 8, pp 4963–4975 | Cite as

Machine-Learning Approach for Design of Nanomagnetic-Based Antennas

  • Carmine GianfagnaEmail author
  • Huan Yu
  • Madhavan Swaminathan
  • Raj Pulugurtha
  • Rao Tummala
  • Giulio Antonini


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.


Antenna machine learning magneto-dielectric nanomaterial 


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  1. 1.
    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.
  2. 2.
    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.CrossRefGoogle Scholar
  3. 3.
    J. Huang, F. Ma, X. Jiang, H. Wang, and J. Li, J. Magn. Magn. Mater. 331, 151 (2013)Google Scholar
  4. 4.
    K.N. Rozanov, M.Y. Koledintseva, and J.L. Drewniak, J. Magn. Magn. Mater. 324, 1063 (2012).Google Scholar
  5. 5.
    Ph. Toneguzzo, O. Acher, G. Viau, F. Fiévet-Vincent, and F. Fiévet, J. Appl. Phys., 81(8), 5546 (1997).Google Scholar
  6. 6.
    I. Conde-Leborán, D. Serantes, and D. Baldomir, J. Magn. Magn. Mater. 380, 321 (2015).CrossRefGoogle Scholar
  7. 7.
    R. Ramprasad, P. Zurcher, M. Petras, M. Miller, and P. Renaud, J. Appl. Phys. 9, 519 (2004).CrossRefGoogle Scholar
  8. 8.
    G. Pilania, C. Wang, X. Jiang, S. Rajasekaran, and R. Ramprasad, in Sci. Rep. 3, 2810.Google Scholar
  9. 9.
    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
  10. 10.
    R. Bikky, N. Badi, and A. Bensaoula, COMSOL Conf. 2010.Google Scholar
  11. 11.
    I.J. Youngs, N. Bowler, K.P. Lymer, and S. Hussain, J. Phys. D Appl. Phys. 38, 188 (2005).CrossRefGoogle Scholar
  12. 12.
    K.F. Young and H.P.R. Frederikse, J. Phys. Chem. Ref. Data 2, 313 (1973). doi: 10.1063/1.3253121.CrossRefGoogle Scholar
  13. 13.
    CST Microwave Studio, Computer Simulation Technology. Accessed 10 July 2015
  14. 14.
    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
  15. 15.
    S. Haykin, Neural Networks and Learning Machines, 3rd ed. (Upper Saddle River, NJ: Pearson - Prentice Hill, 2009), pp. 1–46.Google Scholar
  16. 16.
    K.K. Aggarwal, Y. Singh, P. Chandra, and M. Puri, J. Comput. Sci. 4, 505 (2005). doi: 10.3844/jcssp.2005.505.509.Google Scholar
  17. 17.
    D. Michie, D.J. Spiegelhalter, and C.C. Taylor, Machine Learning, Neural and Statistical Classification (Upper Saddle River: Ellis Horwood, 1994), pp. 98–99.Google Scholar
  18. 18.
    Neural Network Toolbox—Matlab. https://www.mathworks. com/products/neural-network.html. Accessed 10 July 2015
  19. 19.
    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.CrossRefGoogle Scholar
  20. 20.
    J.R. Liu, M. Itoh, T. Horikawa, M. Itakura, N. Kuwano, and K. Machida, J. Phys. D Appl. Phys. 37, 2737 (2004).CrossRefGoogle Scholar
  21. 21.
    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.CrossRefGoogle Scholar

Copyright information

© The Minerals, Metals & Materials Society 2017

Authors and Affiliations

  1. 1.Department of Industrial and Information Engineering and of Economy (DIIIE)University of L’AquilaL’AquilaItaly
  2. 2.Interconnect and Packaging Research CenterGeorgia Institute of TechnologyAtlantaUSA
  3. 3.Packaging Research CenterGeorgia Institute of TechnologyAtlantaUSA
  4. 4.Georgia Tech Research InstituteAtlantaUSA

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