Inverse Design of a Dual-Band Reflective Polarizing Surface Using Generative Machine Learning

Parinaz Naseri*, George Goussetis, Nelson J. G. Fonseca, Sean V. Hum

*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

Reflective linear-to-circular polarization (LP-to-CP) converters are desired for satellite communication applications to reduce the complexity of the antenna system and the number of the required apertures in single-feed-per-beam configurations. Moreover, wideband operation and angular stability are important polarizer attributes in these applications. Here, for the first time, we use a machine-learning technique employing a generative adversarial network (GAN) to systematically and efficiently explore and exploit the solution space of single-layer structures for this problem. By using a GAN to optimize the shape of the polarizing element, superior performance in terms of overlapping axial ratio bandwidth for normal and oblique incidence of 30° compared to the previously studied designs is achieved.

Original languageEnglish
Title of host publication16th European Conference on Antennas and Propagation (EuCAP 2022)
PublisherIEEE
ISBN (Electronic)9788831299046
DOIs
Publication statusPublished - 11 May 2022
Event16th European Conference on Antennas and Propagation 2022 - Madrid, Spain
Duration: 27 Mar 20221 Apr 2022

Conference

Conference16th European Conference on Antennas and Propagation 2022
Abbreviated titleEuCAP 2022
Country/TerritorySpain
CityMadrid
Period27/03/221/04/22

Keywords

  • dual-band polarizer
  • frequency selective surface
  • generative adversarial network (GAN)
  • inverse design
  • machine learning
  • metasurface
  • polarization conversion
  • satellite communications

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Radiation

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