Deeplearning-designed diffractive processor computes hundreds of transformations in parallel SPIEtweets
separate monochromatic diffractive networks is not a feasible strategy that can compete with a wavelength-multiplexed design. Furthermore, if additional multiplexing schemes other than the wavelength multiplexing reported here were to be used, such as temporal multiplexing, switching between different diffractive networks, they would also require the use of additional optoelectronic control elements, further increasing the hardware complexity of the system, which would not be feasible for a largeIt is worth further emphasizing that even if multiple separately optimized monochromatic diffractive networks could be trained to individually perform different target linear transforms at different wavelengths, it is not possible to directly combine the converged/optimized layers of these diffractive networks to match the broadband operation of the wavelength-multiplexed diffractive network presented here.
Since these monochromatic networks are individually trained using only a single illumination wavelength, the optimized modulation of each wavelength under broadband illumination would produce destructive patterns to other wavelengths, and their transformation accuracies would be collectively hampered. This, once again, highlights the significance of our wavelength multiplexing scheme: a wavelength-multiplexed diffractive optical network can be realized through the engineering of the surface profiles of dielectric diffractive layers with arbitrary dispersion properties, whereas these profiles should be designed by simultaneously taking into account all theTo the best of our knowledge, there has not been a demonstration of a design for the all-optical implementation of a complex-valued, arbitrary linear transformation using metasurfaces or metamaterials. In principle, having different diffractive metaunits placed on the same substrate to perform different transformations at different wavelengths could be attempted as an alternative approach to what we presented in this paper. However, such an approach would face severe challenges since at large spectral multiplexing factors shown in this work, the lateral period for each spectral metadesign will substantially increase per substrate, lowering the accuracy of each transformation; at each illumination wavelength, the other metaunits designed for the other spectral components, will also introduce “cross-talk fields” that will severely contaminate the desired responses in each wavelength and cannot be neglected since; the phase responses of the spectrally encoded metaunits, in general, cover a small angular range, leading to low numerical aperture solutions compared to the diffractive solutions reported in this work, where NA=1 ; the low NA of metaunits fundamentally limits the space-bandwidth product of each transformation channel; and if multiple layers of metasurfaces are used in a given design, all of these aforementioned sources of errors associated with spectral metaunits will accumulate and get amplified through the subsequent field propagation in a cascaded manner, causing severe degradations to the final output fields, compared to the desired fields. Perhaps due to these significant challenges outlined here, metasurface or metamaterial-based diffractive designs have not yet been reported as a solution to perform universal linear transformations—neither an arbitrary complex-valued linear transformation nor a group of linear transformations through some form of multiplexing.
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