Polarization multiplexed diffractive computing:all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network
作者机构：Electrical and Computer Engineering DepartmentUniversity of CaliforniaLos AngelesCA 90095USA Bioengineering DepartmentUniversity of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI)University of CaliforniaLos AngelesCA 90095USA
出 版 物：《Light(Science & Applications)》 (光（科学与应用)（英文版）)
年 卷 期：2022年第11卷第7期
页 面：1423-1442页
核心收录：
学科分类：070207[理学-光学] 07[理学] 08[工学] 0803[工学-光学工程] 0702[理学-物理学]
基 金：the support of US Air Force Office of Scientific Research (AFOSR) Materials with Extreme Properties Program funding
主 题：valued arbitrarily Polarization
摘 要：Research on optical computing has recently attracted significant attention due to the transformative advances in machine *** 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 ***,we introduce a polarization-multiplexed diffractive processor to all-optically perform multiple,arbitrarily-selected linear transformations through a single diffractive network trained using deep *** 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 *** 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 *** 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 N_(p)N_(i)N_(o),where Ni and N_(o) represent the number of pixels at the input and output fields-of-view,respectively,and N_(p) refers to the number of unique linear transformations assigned to different input/output polarization *** polarization-multiplexed all-optical diffractive processor can find various applications in optical computing and polarization-based machine vision tasks.
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