Pyramid diffractive optical networks for unidirectional image 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.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 design,the diffractive layers are pyramidally scaled in alignment with the direction of the image magnification or demagnification.This P-D2NN design creates high-fidelity magnified or demagnified images in only one direction,while inhibiting the image formation in the opposite direction—achieving the desired unidirectional imaging operation using a much smaller number of diffractive degrees of freedom within the optical processor volume.Furthermore,the P-D2NN design maintains its unidirectional image magnification/demagnification functionality across a large band of illumination wavelengths despite being trained with a single wavelength.We also designed a wavelength-multiplexed P-D2NN,where a unidirectional magnifier and a unidirectional demagnifier operate simultaneously in opposite directions,at two distinct illumination wavelengths.Furthermore,we demonstrate that by cascading multiple unidirectional P-D2NN modules,we can achieve higher magnification factors.The efficacy of the P-D2NN architecture was also validated experimentally using terahertz illumination,successfully matching our numerical simulations.P-D2NN offers a physics-inspired strategy for designing task-specific visual processors.