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  • Received: Jan. 13, 2020

    Accepted: May. 26, 2020

    Posted: May. 26, 2020

    Published Online: Jun. 30, 2020

    The Author Email: Zhiming M. Wang (zhmwang@uestc.edu.cn)

    DOI: 10.1364/PRJ.388253

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    Eric Ashalley, Kingsley Acheampong, Lucas V. Besteiro, Peng Yu, Arup Neogi, Alexander O. Govorov, Zhiming M. Wang. Multitask deep-learning-based design of chiral plasmonic metamaterials[J]. Photonics Research, 2020, 8(7): 07001213

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Photonics Research, Vol. 8, Issue 7, 07001213 (2020)

Multitask deep-learning-based design of chiral plasmonic metamaterials

Eric Ashalley1, Kingsley Acheampong2, Lucas V. Besteiro1,3, Peng Yu1, Arup Neogi4, Alexander O. Govorov1,5, and Zhiming M. Wang1,*

Author Affiliations

  • 1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
  • 2School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
  • 3Centre Énergie Matériaux et Télécommunications, Institut National de la Recherche Scientifique, Varennes QC J3X 1S2, Canada
  • 4Department of Physics, University of North Texas, Denton, Texas 76203, USA
  • 5Department of Physics and Astronomy, Ohio University, Athens, Ohio 45701, USA

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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