  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.

Optics;Computer Vision and Pattern Recognition;Neural and Evolutionary Computing;Applied Physics