Holographic multiplexing metasurface with twisted diffractive neural network

Zhixiang Fan, Chao Qian (), Yuetian Jia, Yiming Feng, Haoliang Qian (), Er-Ping Li, Romain Fleury and Hongsheng Chen ()
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Zhixiang Fan: Zhejiang University
Chao Qian: Zhejiang University
Yuetian Jia: Zhejiang University
Yiming Feng: Zhejiang University
Haoliang Qian: Zhejiang University
Er-Ping Li: Zhejiang University
Romain Fleury: EPFL
Hongsheng Chen: Zhejiang University

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract As the cornerstone of AI generated content, data drives human-machine interaction and is essential for developing sophisticated deep learning agents. Nevertheless, the associated data storage poses a formidable challenge from conventional energy-intensive planar storage, high maintenance cost, and the susceptibility to electromagnetic interference. In this work, we introduce the concept of metasurface disk, meta-disk, to expand the capacity limits of optical holographic storage by leveraging uncorrelated structural twist. We develop a physical twisted neural network to describe the optical behavior of the meta-disk and conduct a comprehensive lateral error analysis, where the meta-disk stores large volumes of information through internal structural multiplexing. Two-layer 640 µm x 640 µm meta-disk is sufficient to store over hundreds of high-fidelity images with SSIM of 0.8. By harnessing advanced three-dimensional (3D) printing technology, optical holographic storage is experimentally demonstrated with Pancharatnam-Berry metasurfaces. Our technology provides essential backing for the next generation of optical storage, display, encryption, and multifunctional optical analog computing.

Date: 2024
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DOI: 10.1038/s41467-024-53749-6

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