MIT Libraries homeMIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A Deep Learning Approach for Objective-Driven All-Dielectric Metasurface Design

Author(s)
An, Sensong; Fowler, Clayton; Zheng, Bowen; Shalaginov, Mikhail; Tang, Hong; Li, Hang; Zhou, Li; Ding, Jun; Agarwal, Anuradha; Rivero-Baleine, Clara; Richardson, Kathleen A.; Gu, Tian; Hu, Juejun; Zhang, Hualiang; ... Show more Show less
Thumbnail
DownloadAccepted version (1.582Mb)
Publisher Policy

Publisher Policy

Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.

Terms of use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Metadata
Show full item record
Abstract
Metasurfaces have become a promising means for manipulating optical wavefronts in flat and high-performance optical devices. Conventional metasurface device design relies on trial-and-error methods to obtain target electromagnetic (EM) responses, an approach that demands significant efforts to investigate the enormous number of possible meta-atom structures. In this paper, a deep learning modeling approach is introduced that significantly improves on both speed and accuracy compared to techniques currently used to characterize the subwavelength optical structures. Our neural network approach overcomes two key challenges that have limited previous neural-network-based design schemes: input/output vector dimensional mismatch and accurate EM-wave phase prediction. Additionally, this is the first neural network to characterize 3-D dielectric structures. By combining with optimization algorithms or neural networks, this approach can be generically applied to a wide variety of metasurface device designs across the entire electromagnetic spectrum. Using this new methodology, examples of neural networks capable of producing on-demand designs for meta-atoms, metasurface filters, and phase-change reconfigurable metasurfaces are demonstrated.
Date issued
2019-11
URI
https://hdl.handle.net/1721.1/128030
Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Journal
ACS Photonics
Publisher
American Chemical Society (ACS)
Citation
An, Sensong et al. "A Deep Learning Approach for Objective-Driven All-Dielectric Metasurface Design." ACS Photonics 6, 12 (November 2019): 3196–3207 © 2019 American Chemical Society.
Version: Author's final manuscript
ISSN
2330-4022

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries homeMIT Libraries logo

Find us on

Twitter Facebook Instagram YouTube RSS

MIT Libraries navigation

SearchHours & locationsBorrow & requestResearch supportAbout us
PrivacyPermissionsAccessibility
MIT
Massachusetts Institute of Technology
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.