Photonics Research, 2020, 8 (7): 07001213, Published Online: Jun. 30, 2020  

Multitask deep-learning-based design of chiral plasmonic metamaterials Download: 615次

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

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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