A Deep-Learning Enabled Discrete Dielectric Lens Antenna for Terahertz Reconfigurable Holographic Imaging

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)823-827
Number of pages5
Journal / PublicationIEEE Antennas and Wireless Propagation Letters
Volume21
Issue number4
Online published9 Feb 2022
Publication statusPublished - Apr 2022

Abstract

A deep-learning enabled discrete dielectric lens (DDL) antenna with terahertz hologram reconfigurability is proposed. The antenna is constructed by two cascaded discrete dielectric lenses, which are designed based on a diffractive deep neural network (D2NN) with an improved loss function, fed by a static horn. The DDL antenna can achieve dynamic holographic imaging by a simple mechanical translation of the PEC mask attached to the first lens instead of locally controlling individual meta-atoms through incorporating active elements or phase change materials with complicated feeding networks. The phase profiles for the DDL antenna design are obtained by training four customized input field patterns and corresponding anticipated output target images with the modified D2NN. Dynamic switching of four number images "1, 2, 3, 4" is demonstrated by both full-wave simulated and experimental results. The proposed DDL antenna and design strategy present a new approach to achieve wavefront reconfiguration, especially in the absence of tunable components at high frequencies.

Research Area(s)

  • Antennas, Artificial neural networks, Dielectrics, diffractive deep neural network, discrete dielectric lens antenna, Holography, Lenses, Optical imaging, Reconfigurable holograms, terahertz, Training

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