Pyramid diffractive optical networks for unidirectional magnification
  and demagnification
Bijie Bai Xilin Yang Tianyi Gan Jingxi Li Deniz Mengu Mona Jarrahi Aydogan Ozcan
Diffractive deep neural networks (D2NNs) are composed of successive
transmissive layers optimized using supervised deep learning to all-optically
implement various computational tasks between an input and output field-of-view
(FOV). Here, we present a pyramid-structured diffractive optical network design
(which we term P-D2NN), optimized specifically for unidirectional image
magnification and demagnification. In this P-D2NN design, the diffractive
layers are pyramidally scaled in alignment with the direction of the image
magnification or demagnification. Our analyses revealed the efficacy of this
P-D2NN design in unidirectional image magnification and demagnification tasks,
producing high-fidelity magnified or demagnified images in only one direction,
while inhibiting the image formation in the opposite direction - confirming the
desired unidirectional imaging operation. Compared to the conventional D2NN
designs with uniform-sized successive diffractive layers, P-D2NN design
achieves similar performance in unidirectional magnification tasks using only
half of the diffractive degrees of freedom within the optical processor volume.
Furthermore, it maintains its unidirectional image
magnification/demagnification functionality across a large band of illumination
wavelengths despite being trained with a single illumination wavelength. With
this pyramidal architecture, we also designed a wavelength-multiplexed
diffractive network, where a unidirectional magnifier and a unidirectional
demagnifier operate simultaneously in opposite directions, at two distinct
illumination wavelengths. The efficacy of the P-D2NN architecture was also
validated experimentally using monochromatic terahertz illumination,
successfully matching our numerical simulations. P-D2NN offers a
physics-inspired strategy for designing task-specific visual processors.