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Multitask deep-learning-based design of chiral plasmonic metamaterials

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Abstract

The field of chiral plasmonics has registered considerable progress with machine-learning (ML)-mediated metamaterial prototyping, drawing from the success of ML frameworks in other applications such as pattern and image recognition. Here, we present an end-to-end functional bidirectional deep-learning (DL) model for three-dimensional chiral metamaterial design and optimization. This ML model utilizes multitask joint learning features to recognize, generalize, and explore in detail the nontrivial relationship between the metamaterials’ geometry and their chiroptical response, eliminating the need for auxiliary networks or equivalent approaches to stabilize the physically relevant output. Our model efficiently realizes both forward and inverse retrieval tasks with great precision, offering a promising tool for iterative computational design tasks in complex physical systems. Finally, we explore the behavior of a sample ML-optimized structure in a practical application, assisting the sensing of biomolecular enantiomers. Other potential applications of our metastructure include photodetectors, polarization-resolved imaging, and circular dichroism (CD) spectroscopy, with our ML framework being applicable to a wider range of physical problems.

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DOI:10.1364/PRJ.388253

所属栏目:Surface Optics and Plasmonics

基金项目:National Key Research and Development Program of China10.13039/501100012166; China Postdoctoral Science Foundation10.13039/501100002858; National Science Foundation EFRI; UESTC Shared Research Facilities ofElectromagnetic Wave and Matter Interaction;

收稿日期:2020-01-13

录用日期:2020-05-26

网络出版日期:2020-05-26

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Eric Ashalley:Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
Kingsley Acheampong:School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
Lucas V. Besteiro:Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China;Centre énergie Matériaux et Télécommunications, Institut National de la Recherche Scientifique, Varennes QC J3X 1S2, Canada
Peng Yu:Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
Arup Neogi:Department of Physics, University of North Texas, Denton, Texas 76203, USA
Alexander O. Govorov:Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China;Department of Physics and Astronomy, Ohio University, Athens, Ohio 45701, USA
Zhiming M. Wang:Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China

联系人作者:Zhiming M. Wang(zhmwang@uestc.edu.cn)

备注:National Key Research and Development Program of China10.13039/501100012166; China Postdoctoral Science Foundation10.13039/501100002858; National Science Foundation EFRI; UESTC Shared Research Facilities ofElectromagnetic Wave and Matter Interaction;

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引用该论文

Eric Ashalley, Kingsley Acheampong, Lucas V. Besteiro, Peng Yu, Arup Neogi, Alexander O. Govorov, and Zhiming M. Wang, "Multitask deep-learning-based design of chiral plasmonic metamaterials," Photonics Research 8(7), 1213-1225 (2020)

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