ABSTRACT
The modern-day resurgence of machine learning has encouraged researchers to revisit older problem spaces from a new perspective. One promising avenue has been implementing deep neural networks to aid in the simulation of physical systems. In the field of optics, densely connected neural networks able to mimic wave propagation have recently been constructed. These diffractive deep neural networks (D2NN) not only offer new insights into wave propagation, but provide a novel tool for investigating and discovering multi-functional diffractive elements. In this paper, we derive an efficient GPU-friendly D2NN methodology based on Rayleigh-Sommerfeld diffraction. We then use the implementation to virtually forge cascades of optical phase masks subject to different beam steering conditions. The input and output conditions we use to train each D2NN instance is based on commercial electro-optic modulated waveguide systems to encourage experimental follow-on. In total, we analyze the beam steering efficacy of 27 individual D2NN instances which explore different permutations of input sources, mask cascades, and output steering targets.
