Enabling antenna design with nano-magnetic materials using machine learning

Carmine Gianfagna, Madhavan Swaminathan, P. Markondeya Raj, Rao Tummala, Giulio Antonini

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Scopus citations

Abstract

A machine learning approach to design with magneto dielectric nano-composite (MDNC) substrate for planar inverted-F antenna (PIFA) is presented. A new mixing rule model has been developed. A database of material properties has been created using several particle radius and volume fraction. A second database built with antenna simulations has been developed to complete the machine learning dataset. It is shown that, starting from particle radius and volume fraction of the nano-magnetic material, it is possible to calculate the antenna parameters like gain, bandwidth, radiation efficiency, resonant frequency, and viceversa with good precision by using machine learning techniques.

Original languageEnglish (US)
Title of host publication2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781467393621
DOIs
StatePublished - Mar 22 2016
Event10th IEEE Nanotechnology Materials and Devices Conference, NMDC 2015 - Anchorage, United States
Duration: Sep 12 2015Sep 16 2015

Publication series

Name2015 IEEE Nanotechnology Materials and Devices Conference, NMDC 2015

Conference

Conference10th IEEE Nanotechnology Materials and Devices Conference, NMDC 2015
Country/TerritoryUnited States
CityAnchorage
Period9/12/159/16/15

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials

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