# Optical Edge Detection

Single-layer diffractive neural networks hit an accuracy ceiling because their wavefront modulation is limited. Edge detection and classification are now combined on one nonlocal metasurface through spin multiplexing: the co-polarized output component performs momentum-space filtering for real-time edge detection, while the cross-polarized component is geometrically phase-modulated to classify. Crescent-shaped nanopillars couple quasi-bound states in the continuum with magnetic dipole resonances, reaching about 55% polarization conversion efficiency. On MNIST, this edge-enhanced architecture raises single-layer accuracy from 64.2% to 80.7%.

Single-layer diffractive neural networks hit an accuracy ceiling because their wavefront modulation is limited. Edge detection and classification are now combined on one nonlocal metasurface through spin multiplexing: the co-polarized output component performs momentum-space filtering for real-time edge detection, while the cross-polarized component is geometrically phase-modulated to classify. Crescent-shaped nanopillars couple quasi-bound states in the continuum with magnetic dipole resonances, reaching about 55% polarization conversion efficiency. On MNIST, this edge-enhanced architecture raises single-layer accuracy from 64.2% to 80.7%.