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Metasurface-Based Wavelength-Multiplexed Diffractive Neural Networks for Multilabel Intelligent Vision.

Rui Yang1,2, Lei Chen1,2, Zhao Wang1,2

  • 1School of Artificial Intelligence Science and Technology, University of Shanghai for Science and Technology, Shanghai 200093, China.

Nano Letters
|April 21, 2026
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Summary
This summary is machine-generated.

This study introduces a wavelength-multiplexed diffractive neural network (WMDNN) for advanced optical AI. The WMDNN enables simultaneous object and color recognition, overcoming limitations of single-label systems.

Keywords:
diffractive neural networksintelligent visionmetasurfacemultilabel recognitionwavelength multiplexing

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Area of Science:

  • Optics and Photonics
  • Artificial Intelligence
  • Materials Science

Background:

  • Diffractive neural networks (DNNs) offer energy-efficient optical AI but are limited to single-label recognition.
  • Complex visual perception tasks require systems capable of multi-label recognition.

Purpose of the Study:

  • To propose and validate a metasurface-based wavelength-multiplexed diffractive neural network (WMDNN).
  • To enable simultaneous object category and color recognition in a single optical forward pass.

Main Methods:

  • Implementation using cascaded titanium dioxide (TiO2) metasurfaces based on the Pancharatnam-Berry phase principle.
  • Spatially interleaving TiO2 nanoblocks with distinct geometries for independent phase modulation across RGB wavelengths.
  • Validation on a joint category-color Fashion-MNIST dataset.

Main Results:

  • Achieved numerical accuracy of 91.7% and experimental accuracy of 85.8% for joint category-color recognition.
  • Demonstrated simultaneous identification of object category and color in a single pass.

Conclusions:

  • The proposed WMDNN architecture overcomes limitations of traditional DNNs for complex visual tasks.
  • This work enables high-dimensional intelligent perception systems through wavelength multiplexing.