Machine-Learning Approach for Design of Nanomagnetic-Based Antennas

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

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)4963-4975
Number of pages13
JournalJournal of Electronic Materials
Volume46
Issue number8
DOIs
StatePublished - Aug 1 2017

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Electrical and Electronic Engineering
  • Materials Chemistry

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