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Paper Abstract and Keywords
Presentation 2025-01-31 09:45
[Invited Lecture] Inverse design of broadband metamaterial absorber by deep learning
Tatsunosuke Matsui (Mie Univ.) OME2024-45
Abstract (in Japanese) (See Japanese page) 
(in English) The electromagnetic (EM) wave absorbers have garnered significant attention in recent years, and research on metamaterial absorbers (MMAs), which can be fabricated as thin films, has been conducted worldwide. Many MMAs are constructed with a tri-layer structure consisting of a metallic patterned layer, a dielectric layer, and a metallic ground plate. Optimizing structural parameters, such as the dimensions of the metallic patterns and their thickness, etc., is essential for achieving the desired EM wave absorption characteristics. Traditionally, this optimization has involved costly and time-consuming numerical simulations conducted through trial and error. However, recent studies have reported advancements in the less time-consuming inverse design of optical devices based on deep learning. In this study, we report on the inverse design of a broadband MMA based on deep learning techniques. The data sets for training and validation are generated through numerical simulation based on parameters from the results of an ultra-wideband, resistively loaded MMA fabricated using inkjet-printing technology [S. D. Assimonis and V. Fusco, Sci. Rep., 9:12334, 2019]. The forward training of the neural network (FNN) was conducted by inputting the structural parameters and evaluating the absorption spectra. Subsequently, the inverse design of the broadband MMA was executed using a tandem neural network that combined the inverse design neural network (INN) with the pretrained FNN.
Keyword (in Japanese) (See Japanese page) 
(in English) Metamaterial / Metamaterial Absorber / Inverse Design / Deep Learning / Neural Network / Tandem Neural Network / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 370, OME2024-45, pp. 18-23, Jan. 2025.
Paper # OME2024-45 
Date of Issue 2025-01-23 (OME) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF OME2024-45

Conference Information
Committee OME IEE-DEI  
Conference Date 2025-01-30 - 2025-01-31 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English) Organic thin films, Organic and bio devices, General 
Paper Information
Registration To OME 
Conference Code 2025-01-OME-DEI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Inverse design of broadband metamaterial absorber by deep learning 
Sub Title (in English)  
Keyword(1) Metamaterial  
Keyword(2) Metamaterial Absorber  
Keyword(3) Inverse Design  
Keyword(4) Deep Learning  
Keyword(5) Neural Network  
Keyword(6) Tandem Neural Network  
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Keyword(8)  
1st Author's Name Tatsunosuke Matsui  
1st Author's Affiliation Mie University (Mie Univ.)
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Speaker Author-1 
Date Time 2025-01-31 09:45:00 
Presentation Time 25 minutes 
Registration for OME 
Paper # OME2024-45 
Volume (vol) vol.124 
Number (no) no.370 
Page pp.18-23 
#Pages 6 
Date of Issue 2025-01-23 (OME) 

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