Cargando…

A Fast Design Method of Anisotropic Dielectric Lens for Vortex Electromagnetic Wave Based on Deep Learning

Orbital angular momentum (OAM) has made it possible to regulate classical waves in novel ways, which is more energy- or information-efficient than conventional plane wave technology. This work aims to realize the transition of antenna radiation mode through the rapid design of an anisotropic dielect...

Descripción completa

Detalles Bibliográficos
Autores principales: Liang, Bingyang, Zhang, Yonghua, Zhou, Yuanguo, Liu, Weiqiang, Ni, Tao, Wang, Anyi, Fan, Yanan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052138/
https://www.ncbi.nlm.nih.gov/pubmed/36984134
http://dx.doi.org/10.3390/ma16062254
_version_ 1785015087775350784
author Liang, Bingyang
Zhang, Yonghua
Zhou, Yuanguo
Liu, Weiqiang
Ni, Tao
Wang, Anyi
Fan, Yanan
author_facet Liang, Bingyang
Zhang, Yonghua
Zhou, Yuanguo
Liu, Weiqiang
Ni, Tao
Wang, Anyi
Fan, Yanan
author_sort Liang, Bingyang
collection PubMed
description Orbital angular momentum (OAM) has made it possible to regulate classical waves in novel ways, which is more energy- or information-efficient than conventional plane wave technology. This work aims to realize the transition of antenna radiation mode through the rapid design of an anisotropic dielectric lens. The deep learning neural network (DNN) is used to train the electromagnetic properties of dielectric cell structures. Nine variable parameters for changing the dielectric unit structure are present in the input layer of the DNN network. The trained network can predict the transmission phase of the unit cell structure with greater than 98% accuracy within a specific range. Then, to build the corresponding relationship between the phase and the parameters, the gray wolf optimization algorithm is applied. In less than 0.3 s, the trained network can predict the transmission coefficients of the 31 × 31 unit structure in the arrays with great accuracy. Finally, we provide two examples of neural network-based rapid anisotropic dielectric lens design. Dielectric lenses produce the OAM modes +1, −1, and −1, +2 under TE and TM wave irradiation, respectively. This approach resolves the difficult phase matching and time-consuming design issues associated with producing a dielectric lens.
format Online
Article
Text
id pubmed-10052138
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-100521382023-03-30 A Fast Design Method of Anisotropic Dielectric Lens for Vortex Electromagnetic Wave Based on Deep Learning Liang, Bingyang Zhang, Yonghua Zhou, Yuanguo Liu, Weiqiang Ni, Tao Wang, Anyi Fan, Yanan Materials (Basel) Article Orbital angular momentum (OAM) has made it possible to regulate classical waves in novel ways, which is more energy- or information-efficient than conventional plane wave technology. This work aims to realize the transition of antenna radiation mode through the rapid design of an anisotropic dielectric lens. The deep learning neural network (DNN) is used to train the electromagnetic properties of dielectric cell structures. Nine variable parameters for changing the dielectric unit structure are present in the input layer of the DNN network. The trained network can predict the transmission phase of the unit cell structure with greater than 98% accuracy within a specific range. Then, to build the corresponding relationship between the phase and the parameters, the gray wolf optimization algorithm is applied. In less than 0.3 s, the trained network can predict the transmission coefficients of the 31 × 31 unit structure in the arrays with great accuracy. Finally, we provide two examples of neural network-based rapid anisotropic dielectric lens design. Dielectric lenses produce the OAM modes +1, −1, and −1, +2 under TE and TM wave irradiation, respectively. This approach resolves the difficult phase matching and time-consuming design issues associated with producing a dielectric lens. MDPI 2023-03-10 /pmc/articles/PMC10052138/ /pubmed/36984134 http://dx.doi.org/10.3390/ma16062254 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liang, Bingyang
Zhang, Yonghua
Zhou, Yuanguo
Liu, Weiqiang
Ni, Tao
Wang, Anyi
Fan, Yanan
A Fast Design Method of Anisotropic Dielectric Lens for Vortex Electromagnetic Wave Based on Deep Learning
title A Fast Design Method of Anisotropic Dielectric Lens for Vortex Electromagnetic Wave Based on Deep Learning
title_full A Fast Design Method of Anisotropic Dielectric Lens for Vortex Electromagnetic Wave Based on Deep Learning
title_fullStr A Fast Design Method of Anisotropic Dielectric Lens for Vortex Electromagnetic Wave Based on Deep Learning
title_full_unstemmed A Fast Design Method of Anisotropic Dielectric Lens for Vortex Electromagnetic Wave Based on Deep Learning
title_short A Fast Design Method of Anisotropic Dielectric Lens for Vortex Electromagnetic Wave Based on Deep Learning
title_sort fast design method of anisotropic dielectric lens for vortex electromagnetic wave based on deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10052138/
https://www.ncbi.nlm.nih.gov/pubmed/36984134
http://dx.doi.org/10.3390/ma16062254
work_keys_str_mv AT liangbingyang afastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT zhangyonghua afastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT zhouyuanguo afastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT liuweiqiang afastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT nitao afastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT wanganyi afastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT fanyanan afastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT liangbingyang fastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT zhangyonghua fastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT zhouyuanguo fastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT liuweiqiang fastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT nitao fastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT wanganyi fastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning
AT fanyanan fastdesignmethodofanisotropicdielectriclensforvortexelectromagneticwavebasedondeeplearning