Research on optical computing has recently attracted significant attention due to the transformative advances in machine learning.Among different approaches,diffractive optical networks composed of spatially-engineered transmissive surfaces have been demonstrated for all-optical statistical inference and performing arbitrary linear transformations using passive,free-space optical layers.Here,we introduce a polarization-multiplexed diffractive processor to all-optically perform multiple,arbitrarily-selected linear transformations through a single diffractive network trained using deep learning.In this framework,an array of pre-selected linear polarizers is positioned between trainable transmissive diffractive materials that are isotropic,and different target linear transformations(complex-valued)are uniquely assigned to different combinations of input/output polarization states.The transmission layers of this polarization-multiplexed diffractive network are trained and optimized via deep learning and error-backpropagation by using thousands of examples of the input/output fields corresponding to each one of the complex-valued linear transformations assigned to diffferent input/output polarization combinations.Our results and analysis reveal that a single diffractive network can successfully approximate and all-optically implement a group of arbitrarily-selected target transformations with a negligible error when the number of trainable diffractive features/neurons(N)approaches NpNiNo1where Ni and No represent the number of pixels at the input and output fields-of-view,respectively,and Np refers to the number of unique linear transformations assigned to different input/output polarization combinations.This polarization-multiplexed all-optical diffractive processor can find various applications in optical computing and polarization-based machine vision tasks.

Jingxi Li;Yi-Chun Hung;Onur Kulce;Deniz Mengu;Aydogan Ozcan

Electrical and Computer Engineering Department,University of California,Los Angeles,CA 90095,USA;Bioengineering Department,University of California,Los Angeles,CA 90095,USA;California NanoSystems Institute(CNSI),University of California,Los Angeles,CA 90095,USA

光：科学与应用（英文版）

[1]Jingxi Li;Yi-Chun Hung;Onur Kulce;Deniz Mengu;Aydogan Ozcan-.Polarization multiplexed diffractive computing:all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network)[J].光：科学与应用（英文版）,2022(07):1423-1442

trainable,diffferent,NpNiNo1where

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