Multiplexed metasurfaces for diffractive optics via a phase correlation method.

Area of Science:

• Optics and Photonics
• Metamaterials
• Computational Electromagnetics

Background:

• Metasurfaces offer multiplexing capabilities for advanced optical applications.
• Designing metasurfaces for multiple channels simultaneously presents significant challenges in meeting phase requirements.
• Existing methods often involve complex multi-channel training and high computational costs.

Purpose of the Study:

• To propose an innovative phase correlation method for simplifying metasurface multiplexing design.
• To reduce the complexity and computational cost associated with multi-channel phase training.
• To demonstrate the method's effectiveness in designing functional multiplexed metasurfaces.

Main Methods:

• Utilized a multi-layer perceptron (MLP) to establish phase correlations across multiple channels.
• Converted the multi-channel phase training problem into a simplified single-channel optimization task.
• Designed a dual-wavelength multiplexed diffractive neural network (DW-MDNN) and a multi-wavelength metasurface color holography.

Main Results:

• Achieved up to 90% classification accuracy in image recognition using the designed DW-MDNN.
• Demonstrated good performance in multi-wavelength metasurface color holography.
• Successfully reduced design complexity and computational cost compared to traditional methods.

Conclusions:

• The proposed phase correlation method effectively simplifies the design of multiplexed metasurfaces.
• This approach enables efficient realization of complex optical functionalities like DW-MDNNs and color holography.
• The method holds significant potential for advancing metasurface applications requiring multi-channel operation.