Machine learning with complex wave propagation in inverse-designed photonic device

Deep learning acceleration with integrated photonics has typically employed a circuit-centric approach with Mach-Zehnder interferometers. This requires a large spatial footprint, which has motivated the direct training of spatial refractive index distribution within a slab waveguide. Here, we demonstrate through simulations that nonlinear optical material platforms with large electro-optic coefficients can capitalize on this approach. We show that a linear device with realistic device parameters can perform 50 by 50 unitary matrix multiplications. We also performed MNIST digit classification, achieving 90.5% classification accuracy with minimal digital preprocessing. Finally, we comment on device implementation with Lithium Niobate or Barium Titanate slab waveguides.