Multiplexed Metasurfaces for Diffractive Optics via Phase Correlation Method

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.

References in corpus (4)

• All-Optical Machine Learning Using Diffractive Deep Neural Networks
• A broadband achromatic polarization-insensitive metalens consisting of anisotropic nanostructures
• Optical secret sharing with cascaded metasurface holography
• Diffusion Probabilistic Model Based Accurate and High-Degree-of-Freedom Metasurface Inverse Design