Chinese Optics Letters, Volume. 21, Issue 1, 010004(2023)

Plasmonic nanostructure characterized by deep-neural-network-assisted spectroscopy [Invited]

Qi'ao Dong, Wenqi Wang, Xinyi Cao, Yibo Xiao, Xiaohan Guo, Jingxuan Ma, Lianhui Wang**, and Li Gao*
Author Affiliations
  • 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
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    Figures & Tables(5)
    Schematic diagram of the nanostructure characterization process and studied parameters. (a) Process flow of nanostructure characterization process; (b) periodic nanohole and nanopillar plasmonic nanostructure formed by nanoimprint lithography on glass substrate; (c) representative SEM images of the experimental samples of (b); (d) Au, Ag, and Al metal films evaporated on the structures; (e) dielectric coating covered on the structures to be identified for its refractive index.
    Results of DNN1. (a) The architecture of DNN1. The input layer has 201 neurons and the output layer has four neurons; there are five hidden layers. (b)–(d) The absolute error of the testing data set, where (b) is for diameter, (c) is for thickness, and (d) is for period. (e) Relative error of the testing data set; (f) example of two transmission spectra of the real structure and the predicted structure, with an MSE slightly higher than the mean value.
    Results of DNN3. (a) The architecture of DNN3. The input layer has 201 neurons and the output layer has six neurons; there are five hidden layers. (b)–(e) The absolute error of the testing data set, where (b) is for diameter, (c) is for thickness, (d) is for period, and (e) is for the dielectric coating refractive index. (f) Relative error of the testing data set; (g) example of two transmission spectra of the real structure and the predicted structure, with an MSE slightly higher than the mean value.
    Results of DNN4. (a) The architecture of DNN4. The input layer has 201 neurons and the output layer has seven neurons; there are five hidden layers. (b) SEM images of nanohole and nanopillar structures prepared by nanoimprint lithography; (c) comparison of the experimental and simulated spectra; the yellow line indicates that the dielectric layer material is SU8, while the blue one indicates the air. (d) The absolute error of the testing data set; the top is the experimental data group, and the bottom is the total mixed data. (e) The relative error of the testing data set; the top is the experimental data, and the bottom is the total mixed data. (f) Statistics of the number of predictions that successfully identify the data type (experimental or simulated), structure type (nanohole or nanopillar), metal type, metal film thickness, and refractive index of dielectric layer collected for experimental data.
    • Table 1. Statistics of Characterization Error Distribution of DNNs

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      Table 1. Statistics of Characterization Error Distribution of DNNs

       Regression ProblemsClassification Problems
      ParametersAccuracyParametersTruth
      >90%>95%>98%
      DNN1 Data size: 525Diameter525517500Structure type525
      Period525523474
      Thickness525519517
      DNN2 Data size: 735Diameter735727674Structure type735
      Period735735735Dielectric coating727
      Thickness729727712
      DNN3 Data size: 3969Diameter396638573415Structure type3950
      Period396939513829Metal type3805
      Thickness362135233413
      Refractive index394139333925
      DNN4 Data size: 4008Diameter399238423265Structure type3997
      Period399339573784Metal type3822
      Thickness359634413261Data type4008
      Refractive index398139473903
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    Qi'ao Dong, Wenqi Wang, Xinyi Cao, Yibo Xiao, Xiaohan Guo, Jingxuan Ma, Lianhui Wang, Li Gao, "Plasmonic nanostructure characterized by deep-neural-network-assisted spectroscopy [Invited]," Chin. Opt. Lett. 21, 010004 (2023)

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    Paper Information

    Special Issue: SPECIAL ISSUE ON OPTICAL METASURFACES: FUNDAMENTALS AND APPLICATIONS

    Received: Aug. 17, 2022

    Accepted: Oct. 25, 2022

    Published Online: Nov. 24, 2022

    The Author Email: Lianhui Wang (iamlhwang@njupt.edu.cn), Li Gao (iamlgao@njupt.edu.cn)

    DOI:10.3788/COL202321.010004

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