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Exploiting deep learning network in optical chirality tuning and manipulation of diffractive chiral metamaterials

Tao, Z; Zhang, J; You, J; Hao, H; Ouyang, H; Yan, Q; Du, S; ... Jiang, T; + view all (2020) Exploiting deep learning network in optical chirality tuning and manipulation of diffractive chiral metamaterials. Nanophotonics , 9 (9) pp. 2945-2956. 10.1515/nanoph-2020-0194. Green open access

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Abstract

© 2020 Zilong Tao et al., published by De Gruyter, Berlin/Boston 2020. Deep-learning (DL) network has emerged as an important prototyping technology for the advancements of big data analytics, intelligent systems, biochemistry, physics, and nanoscience. Here, we used a DL model whose key algorithm relies on deep neural network to efficiently predict circular dichroism (CD) response in higher-order diffracted beams of two-dimensional chiral metamaterials with different parameters. To facilitate the training process of DL network in predicting chiroptical response, the traditional rigorous coupled wave analysis (RCWA) method is utilized. Notably, these T-like shaped chiral metamaterials all exhibit the strongest CD response in the third-order diffracted beams whose intensities are the smallest, when comparing up to four diffraction orders. Our comprehensive results reveal that by means of DL network, the complex and nonintuitive relations between T-like metamaterials with different chiral parameters (i. e., unit period, width, bridge length, and separation length) and their CD performances are acquired, which owns an ultrafast computational speed that is four orders of magnitude faster than RCWA and a high accuracy. The insights gained from this study may be of assistance to the applications of DL network in investigating different optical chirality in low-dimensional metamaterials and expediting the design and optimization processes for hyper-sensitive ultrathin devices and systems.

Type: Article
Title: Exploiting deep learning network in optical chirality tuning and manipulation of diffractive chiral metamaterials
Open access status: An open access version is available from UCL Discovery
DOI: 10.1515/nanoph-2020-0194
Publisher version: https://doi.org/10.1515/nanoph-2020-0194
Language: English
Additional information: © 2020 Zilong Tao et al., published by De Gruyter, Berlin/Boston. This work is licensed under the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0).
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > Centre for Languages and Intl Educatn
URI: https://discovery.ucl.ac.uk/id/eprint/10108467
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