Pyramid diffractive optical networks for unidirectional magnification and demagnification

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.