检索结果-南通市图书馆

Polarization multiplexed diffractive computing:all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network

引用

Light(Science & Applications) 2022年第7期11卷 1423-1442页

作者： Jingxi Li Yi-Chun Hung onur kulce Deniz Mengu Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute(CNSI) University of CaliforniaLos AngelesCA 90095USA

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.

关键词： valued arbitrarily Polarization

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

All-optical information-processing capacity of diffractive surfaces

引用

Light(Science & Applications) 2021年第2期10卷 217-233页

作者： onur kulce Deniz Mengu Yair Rivenson Aydogan Ozcan Electrical and Computer Engineering Department University of CaliforniaLos AngelesCA 90095USA Bioengineering Department University of CaliforniaLos AngelesCA 90095USA California NanoSystems Institute University of CaliforniaLos AngelesCA 90095USA

The precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and *** advances related to the engineering of materials with new functionalities have also opened up exciting avenues for designing trainable surfaces that can perform computation and machine-learning tasks through light-matter interactions and ***,we analyze the information-processing capacity of coherent optical networks formed by diffractive surfaces that are trained to perform an all-optical computational task between a given input and output *** show that the dimensionality of the all-optical solution space covering the complex-valued transformations between the input and output fields-of-view is linearly proportional to the number of diffractive surfaces within the optical network,up to a limit that is dictated by the extent of the input and output *** diffractive networks that are composed of larger numbers of trainable surfaces can cover a higher-dimensional subspace of the complex-valued linear transformations between a larger input field-of-view and a larger output field-of-view and exhibit depth advantages in terms of their statistical inference,learning,and generalization capabilities for different image classification tasks when compared with a single trainable diffractive *** analyses and conclusions are broadly applicable to various forms of diffractive surfaces,including,e.g.,plasmomc and/or dielectric-based metasurfaces and flat optics,which can be used to form all-optical processors.

关键词： surface. valued optics

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

All-optical synthesis of an arbitrary linear transformation using diffractive surfaces

引用

Light(Science & Applications) 2021年第10期10卷 1928-1948页

Spatially-engineered diffractive surfaces have emerged as a powerful framework to control light-matter interactions for statistical inference and the design of task-specific optical ***,we report the design of diffractive surfaces to all-optically perform arbitrary complex-valued linear transformations between an input(Ni)and output(No),where Ni and No represent the number of pixels at the input and output fields-of-view(FOVs),***,we consider a single diffractive surface and use a matrix pseudoinverse-based method to determine the complex-valued transmission coefficients of the diffractive features/neurons to all-optically perform a desired/target linear *** addition to this data-free design approach,we also consider a deep learning-based design method to optimize the transmission coefficients of diffractive surfaces by using examples of input/output fields corresponding to the target *** compared the all-optical transformation errors and diffraction efficiencies achieved using data-free designs as well as data-driven(deep learning-based)diffractive designs to all-optically perform(i)arbitrarily-chosen complex-valued transformations including unitary,nonunitary,and noninvertible transforms,(ii)2D discrete Fourier transformation,(iii)arbitrary 2D permutation operations,and(iv)high-pass filtered coherent *** analyses reveal that if the total number(N)of spatially-engineered diffractive features/neurons is≥Ni×No,both design methods succeed in all-optical implementation of the target transformation,achieving negligible ***,compared to data-free designs,deep learning-based diffractive designs are found to achieve significantly larger diffraction efficiencies for a given N and their all-optical transformations are more accurate for N

关键词： arbitrary valued unitary

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：