Plasmonic nanostructure characterized by deep-neuralnetwork-assisted spectroscopy[Invited]  

作　　者：董奇奥 王文琦 曹欣怡 肖依博 郭笑涵 马敬轩 汪联辉 高丽 Qi'ao Dong;Wenqi Wang;Xinyi Cao;Yibo Xiao;Xiaohan Guo;Jingxuan Ma;Lianhui Wang;Li Gao(State Key Laboratory for Organic Electronics and Information Displays,Institute of Advanced Materials,School of Materials Science and Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China)

机构地区：[1]State Key Laboratory for Organic Electronics and Information Displays,Institute of Advanced Materials,School of Materials Science and Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210023,China

出　　处：《Chinese Optics Letters》2023年第1期17-24,共8页中国光学快报（英文版）

基　　金：supported by the National Natural Science Foundation of China(Nos.61974069,62022043,and 62235008);National Key Research and Development Program of China(No.2021YFA1202904);Natural Science Foundation of Jiangsu Province Major Project(No.BK20212012);State Key Laboratory of Organic Electronics and Information Displays(No.GDX2022010007);Nanjing University of Posts and Telecommunications(Nos.NUPTSF NY219008 and NJUPT 1311 Talent Program).

摘　　要：The lateral geometry and material property of plasmonic nanostructures are critical parameters for tailoring their optical resonance for sensing applications.While lateral geometry can be easily observed by a scanning electron microscope or an atomic force microscope,characterizing materials properties of plasmonic devices is not straightforward and requires delicate examination of material composition,cross-sectional thickness,and refractive index.In this study,a deep neural network is adopted to characterize these parameters of unknown plasmonic nanostructures through simple transmission spectra.The network architecture is established based on simulated data to achieve accurate identification of both geometric and material parameters.We then demonstrate that the network training by a mixture of simulated and experimental data can result in correct material property recognition.Our work may indicate a simple and intelligent characterization approach to plasmonic nanostructures by spectroscopic techniques.

关 键 词：PLASMONICS soft nanoimprint lithography deep neural network nanostructure characterization 

分 类 号：TN2[电子电信—物理电子学]