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arXiv · 2606.16938

Edge-Enhanced Diffractive Neural Networks Based on Spin-Multiplexed Nonlocal Metasurfaces

Qianqian He·Kenan Guo·Jumin Qiu·Shuyuan Xiao·Tingting Liu
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Abstract

Single-layer diffractive neural networks often face classification accuracy bottlenecks due to limited wavefront modulation capabilities. Edge detection, as an optical image processing technique, extracts image contours and offers a promising way to simplify classification tasks. However, integrating edge detection and DNN-based classification on a single chip remains a challenge. Here, we propose an integrated nonlocal meta-platform that achieves all-optical edge detection and DNN-based classification via spin-multiplexing. By exploiting the dispersion properties of the nonlocal Huygens' metasurface, the co-polarized component in the output light performs momentum-space filtering for real-time edge detection. The cross-polarized component undergoes geometric phase modulation to execute image classification within the DNN. We couple quasi-bound states in the continuum and magnetic dipole resonances in crescent-shaped nanopillars, achieving a high polarization conversion efficiency of approximately $55\%$. This edge-enhanced DNN architecture significantly reduces data redundancy, elevating the classification accuracy of the single-layer network on the MNIST dataset from $64.2\%$ to $80.7\%$. Our work provides a compact, high-efficiency solution for integrated all-optical machine vision and intelligent photonic computing.

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Qianqian He, Kenan Guo, Jumin Qiu, Shuyuan Xiao, Tingting Liu. 2026-06-15. Edge-Enhanced Diffractive Neural Networks Based on Spin-Multiplexed Nonlocal Metasurfaces. https://arxiv.org/abs/2606.16938

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