Efficient Design of Metamaterial Absorbers using Parametric Macromodels

Authors

  • Giulio Antonini 1UAq EMC Laboratory, Dipartimento di Ingegneria Industriale e dell’Informazione e di Economia Universit`a degli Studi dell’Aquila, 67100 L’Aquila, Italy
  • Maria Denise Astorino 2Department of Information Engineering, Electronics and Telecommunications La Sapienza University of Rome, 00184 Rome, Italy
  • Francesco Ferranti Microwave Department Institut Mines-T´el´ecom Atlantique, CNRS UMR 6285 Lab-STICC, 29238 Brest CEDEX 3, France
  • Fabrizio Frezza Department of Information Engineering, Electronics and Telecommunications La Sapienza University of Rome, 00184 Rome, Italy
  • Nicola Tedeschi Department of Information Engineering, Electronics and Telecommunications La Sapienza University of Rome, 00184 Rome, Italy

Keywords:

Efficient Design, Metamaterial Absorbers, Optimization, Parametric Macromodeling

Abstract

Metamaterial absorbers have recently attracted a lot of interest for applications spanning from microwave to terahertz, near infrared and optical frequencies, such as electromagnetic compatibility, thermal emitters, solar cells and micro- bolometers. In this paper, a procedure for the efficient design of metamaterial absorbers based on parametric macromodels is presented. These models are used to describe the frequency-domain behaviour of complex systems as a function of frequency and design parameters (e.g., layout features). Parametric macromodels are very efficient and can be used to speed up the design fow in comparison with using electromagnetic simulators for design tasks. The use of quasi-random sequences for the sampling of the design space and of radial basis functions and polynomial functions for the model construction is proposed. Numerical results validate the efficiency and accuracy of the proposed technique for multiple optimizations.

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Published

2021-07-22

How to Cite

[1]
Giulio Antonini, Maria Denise Astorino, Francesco Ferranti, Fabrizio Frezza, and Nicola Tedeschi, “Efficient Design of Metamaterial Absorbers using Parametric Macromodels”, ACES Journal, vol. 33, no. 07, pp. 772–780, Jul. 2021.

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