Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures.,Nano Letters

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Deep Learning Meets Nanophotonics: A Generalized Accurate Predictor for Near Fields and Far Fields of Arbitrary 3D Nanostructures.
Nano Letters ( IF 9.6 ) Pub Date : 2019-12-13 , DOI: 10.1021/acs.nanolett.9b03971
Peter R Wiecha ₁ , Otto L Muskens ₁

Affiliation

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.

中文翻译：

深度学习与纳米光子学相遇：任意3D纳米结构近场和远场的通用精确预测器。

深度人工神经网络是强大的工具，在纳米光子学中有许多可能的应用。在这里，我们演示了如何将深层神经网络用作等离激元和介电纳米结构的完整近场和远场响应的快速通用预测因子。与常规数值模拟相比，训练有素的神经网络显示出可以推断任意三维纳米结构内部场的速度快了多个数量级。中学物理量是根据深度学习预测得出的，可以忠实地再现各种物理效果，而无需进行专门的培训。我们使用许多单个粒子及其近场相互作用的模型研究来讨论神经网络方法的优势和局限性。我们的方法为快速但通用的方式铺平了道路，

更新日期：2019-12-13

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