Physics-aware-trained diffractive deep neural networks

We have applied physics aware training (PAT) to diffractive deep neural networks (D2NN) consisting of multiple spatial light modulators (SLMs) to close the reality gap between the simulation model and the physical system. Compared to conventional training methods using only simulation models, PAT improves classification accuracy in the experiment. In this method, an analytic expression for backpropagation is based on Rayleigh-Sommerfeld diffraction integral as conventional, but the backpropagated error values are replaced by the measured values.