Holographic multiplexing metasurface with twisted diffractive neural network

Area of Science:

• Optics and Photonics
• Materials Science
• Data Storage Technologies

Background:

• Current data storage methods face challenges including high energy consumption, maintenance costs, and electromagnetic interference.
• The increasing demand for data in AI and machine learning necessitates advanced storage solutions beyond conventional planar approaches.

Purpose of the Study:

• To introduce and investigate the concept of a metasurface disk (meta-disk) for high-capacity optical holographic storage.
• To overcome the limitations of existing data storage technologies by leveraging structural twists for increased data density.

Main Methods:

• Development of a physical twisted neural network to model the optical properties of the meta-disk.
• Comprehensive lateral error analysis to assess data integrity and storage capacity.
• Experimental demonstration using Pancharatnam-Berry metasurfaces and advanced three-dimensional (3D) printing technology.

Main Results:

• A two-layer 640µm x 640µm meta-disk demonstrated the capability to store hundreds of high-fidelity images with a Structural Similarity Index Measure (SSIM) of 0.8.
• The meta-disk utilizes internal structural multiplexing to store large volumes of information efficiently.
• Successful experimental validation of optical holographic storage using metasurfaces.

Conclusions:

• The proposed meta-disk technology offers a significant advancement in optical data storage capacity.
• This technology supports next-generation applications in optical storage, display, encryption, and optical analog computing.
• Metasurface-based optical storage presents a viable alternative to energy-intensive and less robust conventional storage methods.