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On-demand design of spectrally sensitive multiband absorbers using an artificial neural network

查看全文 作　　者：Sunae [1]So;Younghwan [1]Yang;Taejun [1]Lee;Junsuk [1,2,3]Rho 高影响力作者 机构地区：[1]Department of Mechanical Engineering,Pohang University of Science and Technology(POSTECH),Pohang 37673,Republic of Korea;[2]Department of Chemical Engineering,Pohang University of Science and Technology(POSTECH),Pohang 37673,Republic of Korea;[3]National Institute of Nanomaterials Technology(NINT),Pohang 37673,Republic of Korea高影响力机构 出　　处：《Photonics Research》索引2021年第9卷第4期,共6页高影响力期刊 基　　金：National Research Foundation of Korea(NRF-2018M3D1A1058998,NRF-2019R1A2C3003129,CAMM-2019M3A6B3030637,NRF-2019R1A5A8080290,NRF-2020K1A3A1A21024374);Ministry of Education(NRF-2017H1A2A1043322,NRF-2019H1A2A1076295). 摘　　要：We report an approach assisted by deep learning to design spectrally sensitive multiband absorbers that work in the visible range.We propose a five-layered metal-insulator-metal grating structure composed of aluminum and silicon dioxide,and we design its structural parameters by using an artificial neural network(ANN).For a spectrally sensitive design,spectral information of resonant wavelengths is additionally provided as input as well as the reflection spectrum.The ANN facilitates highly robust design of a grating structure that has an average mean squared error(MSE)of 0.023.The optical properties of the designed structures are validated using electromagnetic simulations and experiments.Analysis of design results for gradually changing target wavelengths of input shows that the trained ANN can learn physical knowledge from data.We also propose a method to reduce the size of the ANN by exploiting observations of the trained ANN for practical applications.Our design method can also be applied to design various nanophotonic structures that are particularly sensitive to resonant wavelengths,such as spectroscopic detection and multi-color applications. 关 键 词：network MULTIBAND ABSORBER
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