Journal of Electronic Materials

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

Machine-Learning Approach for Design of Nanomagnetic-Based Antennas

  • Carmine Gianfagna
  • Huan Yu
  • Madhavan Swaminathan
  • Raj Pulugurtha
  • Rao Tummala
  • Giulio Antonini
Article

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.

Keywords

Antenna machine learning magneto-dielectric nanomaterial 

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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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