Total Transmission from Deep Learning Designs  

作　　者：Bei Wu Zhan-Lei Hao Jin-Hui Chen Qiao-Liang Bao Yi-Neng Liu Huan-Yang Chen 

机构地区：[1]Department of Physics,Xiamen University,Xiamen 361005 [2]Institute of Electromagnetics and Acoustics,Xiamen University,Xiamen 361005 [3]Xiamen Key Laboratory of Multiphysics Electronic Information,Xiamen 361005 [4]Fujian Provincial Key Laboratory of Electromagnetic Wave Science and Detection Technology,Xiamen 361005 [5]Fujian Engineering Research Center for EDA,Xiamen 361005

出　　处：《Journal of Electronic Science and Technology》2022年第1期9-19,共11页电子科技学刊（英文版）

基　　金：supported by the National Key Research and Development Program of China under Grant No.2020YFA0710100;the National Natural Science Foundation of China under Grants No.92050102,No.11874311,and No.11504306;the Fundamental Research Funds for the Central Universities under Grant No.20720200074。

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.

关 键 词：Artificial neural networks(ANNs) deep learning forward spectral prediction inverse material design total transmission 

分 类 号：O43[机械工程—光学工程] TP18[理学—光学]