Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network  

作　　者：Jingxi Li Tianyi Gan Bijie Bai Yi Luo Mona Jarrahi Aydogan Ozcan 

机构地区：[1]University of California,Electrical and Computer Engineering Department,Los Angeles,California,United States [2]University of California,Bioengineering Department,Los Angeles,California,United States [3]University of California,California NanoSystems Institute,Los Angeles,California,United States

出　　处：《Advanced Photonics》2023年第1期27-49,共23页先进光子学（英文）

基　　金：the US Air Force Office of Scientific Research funding(Grant No.FA9550-21-1-0324)。

摘　　要：Large-scale linear operations are the cornerstone for performing complex computational tasks.Using optical computing to perform linear transformations offers potential advantages in terms of speed,parallelism,and scalability.Previously,the design of successive spatially engineered diffractive surfaces forming an optical network was demonstrated to perform statistical inference and compute an arbitrary complex-valued linear transformation using narrowband illumination.We report deep-learning-based design of a massively parallel broadband diffractive neural network for all-optically performing a large group of arbitrarily selected,complex-valued linear transformations between an input and output field of view,each with Ni and No pixels,respectively.This broadband diffractive processor is composed of Nw wavelength channels,each of which is uniquely assigned to a distinct target transformation;a large set of arbitrarily selected linear transformations can be individually performed through the same diffractive network at different illumination wavelengths,either simultaneously or sequentially(wavelength scanning).We demonstrate that such a broadband diffractive network,regardless of its material dispersion,can successfully approximate Nw unique complex-valued linear transforms with a negligible error when the number of diffractive neurons(N)in its design is≥2NwNiNo.We further report that the spectral multiplexing capability can be increased by increasing N;our numerical analyses confirm these conclusions for Nw>180 and indicate that it can further increase to Nw∼2000,depending on the upper bound of the approximation error.Massively parallel,wavelength-multiplexed diffractive networks will be useful for designing highthroughput intelligent machine-vision systems and hyperspectral processors that can perform statistical inference and analyze objects/scenes with unique spectral properties.

关 键 词：optical neural network deep learning diffractive optical network wavelength multiplexing optical computing 

分 类 号：O43[机械工程—光学工程]