Deep learning modeling approach for metasurfaces with high degrees of freedom
Name
oe-28-21-31932.pdf
Description
Published version
Size
4.36 MB
Format
Adobe PDF
Checksum (MD5)
fb1af725f0a189c2f4f0b90c70fa559e
Author(s)
an, sensong
Zheng, Bowen
Shalaginov, Mikhail Y
Tang, Hong
Li, Hang
Zhou, Li
Ding, Jun
Agarwal, Anuradha Murthy
Rivero-Baleine, Clara
Kang, Myungkoo
Date Issued
2020
Journal
Optics Express
Publisher
The Optical Society
Citation
an, sensong, Zheng, Bowen, Shalaginov, Mikhail Y, Tang, Hong, Li, Hang et al. 2020. "Deep learning modeling approach for metasurfaces with high degrees of freedom." Optics Express, 28 (21).
Version
Final published version
Abstract
© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement Metasurfaces have shown promising potentials in shaping optical wavefronts while remaining compact compared to bulky geometric optics devices. The design of meta-atoms, the fundamental building blocks of metasurfaces, typically relies on trial and error to achieve target electromagnetic responses. This process includes the characterization of an enormous amount of meta-atom designs with varying physical and geometric parameters, which demands huge computational resources. In this paper, a deep learning-based metasurface/meta-atom modeling approach is introduced to significantly reduce the characterization time while maintaining accuracy. Based on a convolutional neural network (CNN) structure, the proposed deep learning network is able to model meta-atoms with nearly freeform 2D patterns and different lattice sizes, material refractive indices and thicknesses. Moreover, the presented approach features the capability of predicting a meta-atom’s wide spectrum response in the timescale of milliseconds, attractive for applications necessitating fast on-demand design and optimization of a meta-atom/metasurface.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
MIT Materials Research Laboratory
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/10.1364/OE.401960
