Holographic multiplexing metasurface with twisted diffractive neural network
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.
The meta-disk composing of NMs has the capability to implement high-capacity optical holographic storage with angle-multiplexed strategy. With interlayer structural twist, it yields twin diffraction domains, resulting in a distinctive response with metasurface rotation. The network characteristics of TDNN can dynamically take shape with the varying angles of multiple layers of NMs, leading to a substantial expansion in design flexibility and freedom. Through the continuous playback of the meta-disk, a series of diffraction domains and innovative holographic videos emerges. NM, neuro-metasurface.
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