等离子体子
纳米光子学
人工神经网络
近场和远场
计算机科学
非线性系统
感知器
超材料
深度学习
材料科学
电子工程
人工智能
光学
光电子学
物理
工程类
量子力学
作者
Qingxin Wu,Xiaozhong Li,Li Jun Jiang,Xialing Xu,Dong Fang,Jingjing Zhang,Chunyuan Song,Zongfu Yu,Lianhui Wang,Li Gao
摘要
The electromagnetic response of plasmonic nanostructures is highly sensitive to their geometric parameters. In multi-dimensional parameter space, conventional full-wave simulation and numerical optimization can consume significant computation time and resources. It is also highly challenging to find the globally optimized result and perform inverse design for a highly nonlinear data structure. In this work, we demonstrate that a simple multi-layer perceptron deep neural network can capture the highly nonlinear, complex relationship between plasmonic geometry and its near- and far-field properties. Our deep learning approach proves accurate inverse design of near-field enhancement and far-field spectrum simultaneously, which can enable the design of dual-functional optical sensors. Such implementation is helpful for exploring subtle, complex multifunctional nanophotonics for sensing and energy conversion applications.
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