期刊
NANO LETTERS
卷 20, 期 1, 页码 329-338出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.nanolett.9b03971
关键词
Deep learning; nanophotonics; rapid nano-optics simulations; silicon nanostructures; plasmonics
类别
资金
- German Research Foundation (DFG) [WI 5261/1-1]
- EPSRC [EP/M009122/1]
Deep artificial neural networks are powerful tools with many possible applications in nanophotonics. Here, we demonstrate how a deep neural network can be used as a fast, general purpose predictor of the full near-field and far-field response of plasmonic and dielectric nanostructures. A trained neural network is shown to infer the internal fields of arbitrary three-dimensional nanostructures many orders of magnitude faster compared to conventional numerical simulations. Secondary physical quantities are derived from the deep learning predictions and faithfully reproduce a wide variety of physical effects without requiring specific training. We discuss the strengths and limitations of the neural network approach using a number of model studies of single particles and their near-field interactions. Our approach paves the way for fast, yet universal, methods for design and analysis of nanophotonic systems.
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