文章基本信息

标题：Exploiting deep learning network in optical chirality tuning and manipulation of diffractive chiral metamaterials
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作者：Zilong Tao ; Jun Zhang ; Jie You 等
期刊名称：Nanophotonics
印刷版ISSN：2192-8606
电子版ISSN：2192-8614
出版年度：2020
卷号：9
期号：9
页码：2945-2956
DOI：10.1515/nanoph-2020-0194
出版社：Walter de Gruyter GmbH
摘要：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.
关键词：circular dichroism ; deep learning neural networks ; diffractive chiral metamaterials ; optical chirality ; polarization-selective devices