Deep neural network enabled active metasurface embedded design
Name
Nanophotonics - 2022 - An - Deep neural network enabled active metasurface embedded design.pdf
Description
Published version
Size
2.71 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
An, Sensong
Zheng, Bowen
Julian, Matthew
Williams, Calum
Tang, Hong
Gu, Tian
Zhang, Hualiang
Kim, Hyun Jung
Hu, Juejun
Date Issued
June 10, 2022
Journal
Nanophotonics
Publisher
Wiley
Citation
An, S., Zheng, B., Julian, M., Williams, C., Tang, H., Gu, T., Zhang, H., Kim, H.J. and Hu, J. (2022), Deep neural network enabled active metasurface embedded design. Nanophotonics, 11: 4149-4158.
Version
Final published version
Abstract
In this paper, we propose a deep learning approach for forward modeling and inverse design of photonic devices containing embedded active metasurface structures. In particular, we demonstrate that combining neural network design of metasurfaces with scattering matrix-based optimization significantly simplifies the computational overhead while facilitating accurate objective-driven design. As an example, we apply our approach to the design of a continuously tunable bandpass filter in the mid-wave infrared, featuring narrow passband (∼10 nm), high quality factors (Q-factors ∼ 102), and large out-of-band rejection (optical density ≥ 3). The design consists of an optical phase-change material Ge2Sb2Se4Te (GSST) metasurface atop a silicon heater sandwiched between two distributed Bragg reflectors (DBRs). The proposed design approach can be generalized to the modeling and inverse design of arbitrary response photonic devices incorporating active metasurfaces.
MIT Department
Massachusetts Institute of Technology. Department of Materials Science and Engineering
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
MIT Materials Research Laboratory
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DOI of Published Version
https://doi.org/10.1515/nanoph-2022-0152
