Direct field-to-pattern monolithic design of holographic metasurface via residual encoder-decoder convolutional neural network

Complex-amplitude holographic metasurfaces (CAHMs) with the flexibility in modulating phase and amplitude profiles have been used to manipulate the propagation of wavefront with an unprecedented level, leading to higher image-reconstruction quality compared with their natural counterparts. However,...

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Vydáno v:Opto-Electronic Advances Ročník 6; číslo 8; s. 220148
Hlavní autoři: Zhu, Ruichao, Wang, Jiafu, Qiu, Tianshuo, Yang, Dingkang, Feng, Bo, Chu, Zuntian, Liu, Tonghao, Han, Yajuan, Chen, Hongya, Qu, Shaobo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Shaanxi Key Laboratory of Artificially-Structured Functional Materials and Devices,Air Force Engineering University,Xi'an 710051,China%The Academy for Engineering & Technology,Fudan University,Shanghai 200433,China 01.01.2023
Institue of Optics and Electronics, Chinese Academy of Sciences
Témata:
complex amplitude
deep learning
holography
metasurface
monolithic design
deep learning
holography
complex amplitude
monolithic design
metasurface
ISSN:2096-4579
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Shrnutí:Complex-amplitude holographic metasurfaces (CAHMs) with the flexibility in modulating phase and amplitude profiles have been used to manipulate the propagation of wavefront with an unprecedented level, leading to higher image-reconstruction quality compared with their natural counterparts. However, prevailing design methods of CAHMs are based on Huygens-Fresnel theory, meta-atom optimization, numerical simulation and experimental verification, which results in a consumption of computing resources. Here, we applied residual encoder-decoder convolutional neural network to directly map the electric field distributions and input images for monolithic metasurface design. A pretrained network is firstly trained by the electric field distributions calculated by diffraction theory, which is subsequently migrated as transfer learning framework to map the simulated electric field distributions and input images. The training results show that the normalized mean pixel error is about 3％ on dataset. As verification, the metasurface prototypes are fabricated, simulated and measured. The reconstructed electric field of reverse-engineered metasurface exhibits high similarity to the target electric field, which demonstrates the effectiveness of our design. Encouragingly, this work provides a monolithic field-to-pattern design method for CAHMs, which paves a new route for the direct reconstruction of metasurfaces.
ISSN:2096-4579
DOI:10.29026/oea.2023.220148