Optimization of metamaterials and metamaterial-microcavity based on deep neural networks

Lan, Guoqiang, Wang, Yu and Ou, Jun-Yu (2022) Optimization of metamaterials and metamaterial-microcavity based on deep neural networks. Nanoscale Advances, 4 (23), 5137-5143, [5137]. (doi:10.1039/D2NA00592A).

Record type: Article

Abstract

Computational inverse-design and forward prediction approaches provide promising pathways for on-demand nanophotonics. Here, we use a deep-learning method to optimize the design of split-ring metamaterials and metamaterial-microcavities. Once the deep neural network is trained, it can predict the optical response of the split-ring metamaterial in a second which is much faster than conventional simulation methods. The pretrained neural network can also be used for the inverse design of split-ring metamaterials and metamaterial-microcavities. We use this method for the design of the metamaterial-microcavity with the absorptance peak at 1310 nm. Experimental results verified that the deep-learning method is a fast, robust, and accurate method for designing metamaterials with complex nanostructures.

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Accepted/In Press date: 27 October 2022

e-pub ahead of print date: 28 October 2022

Published date: 28 October 2022

Additional Information: Funding Information: We acknowledge Dr Eric Plum from the University of Southampton for the beneficial discussion. This work is supported by the UK Engineering and Physical Science Research Council (grants EP/M009122/1 and EP/T02643X/1) and the Basic Scientific Research of Heilongjiang University (2020-KYYWF-0997), China. Publisher Copyright: © 2022 RSC.

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