End-to-End Diverse Metasurface Design and Evaluation Using an Invertible Neural Network

被引:6
|
作者
Wang, Yunxiang [1 ]
Yang, Ziyuan [2 ]
Hu, Pan [1 ]
Hossain, Sushmit [1 ]
Liu, Zerui [1 ]
Ou, Tse-Hsien [1 ]
Ye, Jiacheng [1 ]
Wu, Wei [1 ]
机构
[1] Univ Southern Calif, Ming Hsieh Dept Elect Engn, Los Angeles, CA 90089 USA
[2] Renmin Univ China, High Sch, CUIWEI Campus, Beijing 100086, Peoples R China
关键词
deep learning; invertible neural network; metasurface; metalens; holograms; INVERSE DESIGN; DEEP;
D O I
10.3390/nano13182561
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Employing deep learning models to design high-performance metasurfaces has garnered significant attention due to its potential benefits in terms of accuracy and efficiency. A deep learning-based metasurface design framework typically comprises a forward prediction path for predicting optical responses and a backward retrieval path for generating geometrical configurations. In the forward design path, a specific geometrical configuration corresponds to a unique optical response. However, in the inverse design path, a single performance metric can correspond to multiple potential designs. This one-to-many mapping poses a significant challenge for deep learning models and can potentially impede their performance. Although representing the inverse path as a probabilistic distribution is a widely adopted method for tackling this problem, accurately capturing the posterior distribution to encompass all potential solutions remains an ongoing challenge. Furthermore, in most pioneering works, the forward and backward paths are captured using separate models. However, the knowledge acquired from the forward path does not contribute to the training of the backward model. This separation of models adds complexity to the system and can hinder the overall efficiency and effectiveness of the design framework. Here, we utilized an invertible neural network (INN) to simultaneously model both the forward and inverse process. Unlike other frameworks, INN focuses on the forward process and implicitly captures a probabilistic model for the inverse process. Given a specific optical response, the INN enables the recovery of the complete posterior over the parameter space. This capability allows for the generation of novel designs that are not present in the training data. Through the integration of the INN with the angular spectrum method, we have developed an efficient and automated end-to-end metasurface design and evaluation framework. This novel approach eliminates the need for human intervention and significantly speeds up the design process. Utilizing this advanced framework, we have effectively designed high-efficiency metalenses and dual-polarization metasurface holograms. This approach extends beyond dielectric metasurface design, serving as a general method for modeling optical inverse design problems in diverse optical fields.
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页数:14
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