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Issue 19, 2019
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Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network

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Abstract

The burgeoning research of graphene and other 2D materials enables many unprecedented metamaterials and metadevices for applications on nanophotonics. The design of on-demand graphene-based metamaterials often calls for the solution of a complex inverse problem within a small sampling space, which highly depends on the rich experiences from researchers of nanophotonics. Conventional optimization algorithms could be used for this inverse design, but they converge to local optimal solutions and take significant computational costs with increased nanostructure parameters. Here, we establish a deep learning method based on an adaptive batch-normalized neural network, aiming to implement smart and rapid inverse design for graphene-based metamaterials with on-demand optical responses. This method allows a quick converging speed with high precision and low computational consumption. As typical complex proof-of-concept examples, the optical metamaterials consisting of graphene/dielectric alternating multilayers are chosen to demonstrate the validity of our design paradigm. Our method demonstrates a high prediction accuracy of over 95% after very few training epochs. A universal programming package is developed to achieve the design goals of graphene-based metamaterials with low absorption and near unity absorption, respectively. Our work may find important design applications in the field of nanoscale photonics based on graphene and other 2D materials.

Graphical abstract: Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network

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Publication details

The article was received on 12 Feb 2019, accepted on 01 May 2019 and first published on 02 May 2019


Article type: Paper
DOI: 10.1039/C9NR01315F
Nanoscale, 2019,11, 9749-9755

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    Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network

    Y. Chen, J. Zhu, Y. Xie, N. Feng and Q. H. Liu, Nanoscale, 2019, 11, 9749
    DOI: 10.1039/C9NR01315F

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