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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.
Tatsuhiro Onodera,Martin Stein,Benjamin Ash,Logan Wright, andPeter McMahon
"Machine learning with complex wave propagation in inverse-designed photonic device", Proc. SPIE PC12903, AI and Optical Data Sciences V, PC1290303 (13 March 2024); https://doi.org/10.1117/12.3002762
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Tatsuhiro Onodera, Martin Stein, Benjamin Ash, Logan Wright, Peter McMahon, "Machine learning with complex wave propagation in inverse-designed photonic device," Proc. SPIE PC12903, AI and Optical Data Sciences V, PC1290303 (13 March 2024); https://doi.org/10.1117/12.3002762