典型文献
Total Transmission from Deep Learning Designs
文献摘要：
Total transmission plays an important role in efficiency improvement and wavefront control, and has made great progress in many applications, such as the optical film and signal transmission. Therefore, many traditional physical methods represented by transformation optics have been studied to achieve total transmission. However, these methods have strict limitations on the size of the photonic structure, and the calculation is complex. Here, we exploit deep learning to achieve this goal. In deep learning, the data-driven prediction and design are carried out by artificial neural networks (ANNs), which provide a convenient architecture for large dataset problems. By taking the transmission characteristic of the multi-layer stacks as an example, we demonstrate how optical materials can be designed by using ANNs. The trained network directly establishes the mapping from optical materials to transmission spectra, and enables the forward spectral prediction and inverse material design of total transmission in the given parameter space. Our work paves the way for the optical material design with special properties based on deep learning.
文献关键词：
中图分类号：
作者姓名：
Bei Wu;Zhan-Lei Hao;Jin-Hui Chen;Qiao-Liang Bao;Yi-Neng Liu;Huan-Yang Chen
作者机构：
Department of Physics, Xiamen University, Xiamen 361005;Institute of Electromagnetics and Acoustics, Xiamen University, Xiamen 361005;Xiamen Key Laboratory of Multiphysics Electronic Information, Xiamen 361005;Fujian Provincial Key Laboratory of Electromagnetic Wave Science and Detection Technology, Xiamen 361005;Fujian Engineering Research Center for EDA, Xiamen 361005;Guangdong Nanhe Laser Technology Co., Ltd., Foshan 528314
文献出处：
引用格式：
[1]Bei Wu;Zhan-Lei Hao;Jin-Hui Chen;Qiao-Liang Bao;Yi-Neng Liu;Huan-Yang Chen-.Total Transmission from Deep Learning Designs)[J].电子科技学刊,2022(01):9-19
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Total,Transmission,from,Deep,Learning,Designs,transmission,plays,important,role,efficiency,improvement,wavefront,control,has,made,great,progress,many,applications,such,optical,film,signal,Therefore,traditional,physical,methods,represented,by,transformation,optics,have,been,studied,achieve,total,However,these,strict,limitations,size,photonic,structure,calculation,complex,Here,exploit,deep,learning,this,goal,In,driven,prediction,are,carried,out,artificial,neural,networks,ANNs,which,provide,convenient,architecture,large,dataset,problems,By,taking,characteristic,multi,layer,stacks,example,demonstrate,how,materials,can,designed,using,trained,directly,establishes,mapping,enables,forward,spectral,inverse,given,parameter,space,Our,paves,way,special,properties
AB值：
0.644183
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