Customized Inverse Design of Metamaterial Absorber Based on Target-Driven Deep Learning Method
Metamaterials (MMs) have already achieved wide applications in academia and industry. The traditional design approach for MMs highly relies on full-wave numerical simulations and trial-and-error methods. It is time-consuming and laborious to obtain the optimal design parameters. Recently, extensive...
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doaj-de79ece31ae94161950968e8348e894c2021-03-30T04:31:26ZengIEEEIEEE Access2169-35362020-01-01821184921185910.1109/ACCESS.2020.30389339262867Customized Inverse Design of Metamaterial Absorber Based on Target-Driven Deep Learning MethodJunjie Hou0https://orcid.org/0000-0002-4058-9126Hai Lin1https://orcid.org/0000-0002-9796-7802Weilin Xu2https://orcid.org/0000-0002-5174-9316Yuze Tian3https://orcid.org/0000-0002-6482-3466You Wang4Xintong Shi5Feng Deng6Lijie Chen7College of Physical Science and Technology, Central China Normal University, Wuhan, ChinaCollege of Physical Science and Technology, Central China Normal University, Wuhan, ChinaSchool of the Information and Communication, Guilin University of Electronic Technology, Guilin, ChinaCollege of Physical Science and Technology, Central China Normal University, Wuhan, ChinaCollege of Physical Science and Technology, Central China Normal University, Wuhan, ChinaCollege of Physical Science and Technology, Central China Normal University, Wuhan, ChinaScience and Technology on Electromagnetic Compatibility Laboratory, China Ship Development and Design Centre, Wuhan, ChinaScience and Technology on Electromagnetic Compatibility Laboratory, China Ship Development and Design Centre, Wuhan, ChinaMetamaterials (MMs) have already achieved wide applications in academia and industry. The traditional design approach for MMs highly relies on full-wave numerical simulations and trial-and-error methods. It is time-consuming and laborious to obtain the optimal design parameters. Recently, extensive researches have shown advantages and superiority of the deep learning method in solving non-intuitive problem. Several attempts have been made to demonstrate Artificial Intelligence (AI) usage in the electromagnetic field. In this article, a target-driven method empowered by deep learning to realize customized metamaterial absorber (MMA) design has been proposed and demonstrated numerically. Unlike previous deep-learning-based design methods for MMs which directly use the spectrum response to generate the MM's design parameters, this method takes the frequency domain response of the absorber as the intermediary bridge to establish the mapping between MMAs geometry/material parameters and customized figure-of-merits. The proposed design framework greatly simplified the design process of MMAs, and it can also be generalized to realize automatic inverse design for other kinds of metasurfaces.https://ieeexplore.ieee.org/document/9262867/Metamaterialsabsorberinverse designdeep learningneural networkresistive films |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Junjie Hou Hai Lin Weilin Xu Yuze Tian You Wang Xintong Shi Feng Deng Lijie Chen |
spellingShingle |
Junjie Hou Hai Lin Weilin Xu Yuze Tian You Wang Xintong Shi Feng Deng Lijie Chen Customized Inverse Design of Metamaterial Absorber Based on Target-Driven Deep Learning Method IEEE Access Metamaterials absorber inverse design deep learning neural network resistive films |
author_facet |
Junjie Hou Hai Lin Weilin Xu Yuze Tian You Wang Xintong Shi Feng Deng Lijie Chen |
author_sort |
Junjie Hou |
title |
Customized Inverse Design of Metamaterial Absorber Based on Target-Driven Deep Learning Method |
title_short |
Customized Inverse Design of Metamaterial Absorber Based on Target-Driven Deep Learning Method |
title_full |
Customized Inverse Design of Metamaterial Absorber Based on Target-Driven Deep Learning Method |
title_fullStr |
Customized Inverse Design of Metamaterial Absorber Based on Target-Driven Deep Learning Method |
title_full_unstemmed |
Customized Inverse Design of Metamaterial Absorber Based on Target-Driven Deep Learning Method |
title_sort |
customized inverse design of metamaterial absorber based on target-driven deep learning method |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Metamaterials (MMs) have already achieved wide applications in academia and industry. The traditional design approach for MMs highly relies on full-wave numerical simulations and trial-and-error methods. It is time-consuming and laborious to obtain the optimal design parameters. Recently, extensive researches have shown advantages and superiority of the deep learning method in solving non-intuitive problem. Several attempts have been made to demonstrate Artificial Intelligence (AI) usage in the electromagnetic field. In this article, a target-driven method empowered by deep learning to realize customized metamaterial absorber (MMA) design has been proposed and demonstrated numerically. Unlike previous deep-learning-based design methods for MMs which directly use the spectrum response to generate the MM's design parameters, this method takes the frequency domain response of the absorber as the intermediary bridge to establish the mapping between MMAs geometry/material parameters and customized figure-of-merits. The proposed design framework greatly simplified the design process of MMAs, and it can also be generalized to realize automatic inverse design for other kinds of metasurfaces. |
topic |
Metamaterials absorber inverse design deep learning neural network resistive films |
url |
https://ieeexplore.ieee.org/document/9262867/ |
work_keys_str_mv |
AT junjiehou customizedinversedesignofmetamaterialabsorberbasedontargetdrivendeeplearningmethod AT hailin customizedinversedesignofmetamaterialabsorberbasedontargetdrivendeeplearningmethod AT weilinxu customizedinversedesignofmetamaterialabsorberbasedontargetdrivendeeplearningmethod AT yuzetian customizedinversedesignofmetamaterialabsorberbasedontargetdrivendeeplearningmethod AT youwang customizedinversedesignofmetamaterialabsorberbasedontargetdrivendeeplearningmethod AT xintongshi customizedinversedesignofmetamaterialabsorberbasedontargetdrivendeeplearningmethod AT fengdeng customizedinversedesignofmetamaterialabsorberbasedontargetdrivendeeplearningmethod AT lijiechen customizedinversedesignofmetamaterialabsorberbasedontargetdrivendeeplearningmethod |
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