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Paper Abstract and Keywords
Presentation 2022-10-20 17:10
Structural Design by Deep Learning for Improving Coupling Efficiency between Si Thin Wire and Topological Waveguide
Itsuki Sakamoto, Tomohiro Amemiya, Sho Okada, Hibiki Kagami, Nobuhiko Nishiyama (Tokyo Tech), Xiao Hu (NIMS) OCS2022-25 OPE2022-71 LQE2022-34
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a structure design method using deep learning to achieve highly efficient coupling between a normal Si waveguide and a topological transmission line. In topological photonic systems, it is not realistic to design the parameters of all unit cell elements individually, because the number of parameters increases in powers of unity. Therefore, we have applied deep learning to the structure designing. In the procedure of structural design using deep learning, a dataset consisting of 6,000 data was obtained using the finite difference time domain method by randomly shifting the distance from the center of the unit cell to the air pore for each unit cell. Next, to use the datasets, we constructed a neural network consisting of five layers, including a convolutional layer. By training the network, we obtained a regression function with a correlation coefficient of 0.943. By exploring the structural parameter space from the regression function, we were able to derive structural parameters that exceed the highest coupling efficiency in the dataset, demonstrating the effectiveness of the method using deep learning.
Keyword (in Japanese) (See Japanese page) 
(in English) Topological photonics / Silicon photonics / Deep learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 217, OPE2022-71, pp. 45-50, Oct. 2022.
Paper # OPE2022-71 
Date of Issue 2022-10-13 (OCS, OPE, LQE) 
ISSN Online edition: ISSN 2432-6380
Copyright
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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)
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Conference Information
Committee OPE OCS LQE  
Conference Date 2022-10-20 - 2022-10-21 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To OPE 
Conference Code 2022-10-OPE-OCS-LQE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Structural Design by Deep Learning for Improving Coupling Efficiency between Si Thin Wire and Topological Waveguide 
Sub Title (in English)  
Keyword(1) Topological photonics  
Keyword(2) Silicon photonics  
Keyword(3) Deep learning  
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1st Author's Name Itsuki Sakamoto  
1st Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
2nd Author's Name Tomohiro Amemiya  
2nd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
3rd Author's Name Sho Okada  
3rd Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
4th Author's Name Hibiki Kagami  
4th Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
5th Author's Name Nobuhiko Nishiyama  
5th Author's Affiliation Tokyo Institute of Technology (Tokyo Tech)
6th Author's Name Xiao Hu  
6th Author's Affiliation National Institute for Materials Science (NIMS)
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Speaker Author-1 
Date Time 2022-10-20 17:10:00 
Presentation Time 25 minutes 
Registration for OPE 
Paper # OCS2022-25, OPE2022-71, LQE2022-34 
Volume (vol) vol.122 
Number (no) no.216(OCS), no.217(OPE), no.218(LQE) 
Page pp.45-50 
#Pages 6 
Date of Issue 2022-10-13 (OCS, OPE, LQE) 

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