Multiplexed Metasurfaces for Diffractive Optics via Phase Correlation Method

TL;DR

This paper introduces a phase correlation method using a neural network to simplify the design of multiplexed metasurfaces, enabling efficient multi-channel phase control for applications like neural networks and holography.

Contribution

It proposes a novel phase correlation approach with a multi-layer perceptron to reduce design complexity and computational cost in multiplexed metasurface development.

Findings

Achieved up to 90% classification accuracy in image recognition

Demonstrated effective multi-wavelength color holography

Reduced design complexity and computational cost

Abstract

The multiplexing capability of metasurfaces has been successfully demonstrated in applications such as holography and diffractive neural networks. However, identifying a suitable structure that simultaneously satisfies the phase requirements across multiple channels remains a significant challenge in many multiplexing design scenarios. In this study, we propose an innovative phase correlation method for metasurface multiplexing design that utilizes a multi-layer perceptron to establish phase correlations across multiple channels. This approach reduces the difficulty of multi-channel phase training by converting it into a simpler single-channel optimization task, thereby reducing design complexity and computational cost. Using the proposed method, we design a dual-wavelength multiplexed diffractive neural network and a multi-wavelength metasurface color holography under a linear polarization. The designed multiplexed metasurface achieves up to 90% classification accuracy in image recognition and exhibits good performance in color holography.