A Deep Learning Approach for Objective-Driven All-Dielectric Metasurface Design
Name
A Deep Learning Approach for Objective-Driven All-Dielectric Metasurface Design.pdf
Description
Accepted version
Size
1.58 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
An, Sensong
Fowler, Clayton
Zheng, Bowen
Shalaginov, Mikhail
Tang, Hong
Li, Hang
Zhou, Li
Ding, Jun
Agarwal, Anuradha
Rivero-Baleine, Clara
Date Issued
November 2019
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
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.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
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.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acsphotonics.9b00966
