Exploiting optical degrees of freedom for information multiplexing in diffractive neural networks

Chao Zuo; Qian Chen

doi:10.1038/s41377-022-00903-8

Go to Nature Website

Your Location：

Home >

Browse articles >

Exploiting optical degrees of freedom for information multiplexing in diffractive neural networks

News & Views

- Exploiting optical degrees of freedom for information multiplexing in diffractive neural networks
- Exploiting optical degrees of freedom for information multiplexing in diffractive neural networks
- Light: Science & Applications 2022年11卷第8期页码：1639-1642
- Affiliations：
  
  1.Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, 210094, Nanjing, Jiangsu Province, China
  2.Smart Computational Imaging Research Institute (SCIRI) of Nanjing University of Science and Technology, 210019, Nanjing, Jiangsu Province, China
  3.Jiangsu Key Laboratory of Spectral Imaging & Intelligent Sense, 210094, Nanjing, Jiangsu Province, China
- Author bio：
  
  Chao Zuo (zuochao@njust.edu.cn)
- Funds：
- DOI：10.1038/s41377-022-00903-8
  中图分类号：
- 纸质出版日期：2022-08-31，
  
  网络出版日期：2022-07-06，
- Accepted：
Scan for full text
Chao Zuo, Qian Chen. Exploiting optical degrees of freedom for information multiplexing in diffractive neural networks[J]. Light: Science & Applications, 2022,11(8):1639-1642.

Zuo, C. & Chen Q. Exploiting optical degrees of freedom for information multiplexing in diffractive neural networks. Light: Science & Applications, 11, 1639-1642 (2022).
Chao Zuo, Qian Chen. Exploiting optical degrees of freedom for information multiplexing in diffractive neural networks[J]. Light: Science & Applications, 2022,11(8):1639-1642. DOI： 10.1038/s41377-022-00903-8.

Zuo, C. & Chen Q. Exploiting optical degrees of freedom for information multiplexing in diffractive neural networks. Light: Science & Applications, 11, 1639-1642 (2022). DOI： 10.1038/s41377-022-00903-8.

摘要

Abstract

Exploiting internal degrees of freedom of light

such as polarization

provides efficient ways to scale the capacity of optical diffractive computing

which may ultimately lead to high-throughput

multifunctional all-optical diffractive processors that can execute a diverse range of tasks in parallel.

关键词

Keywords

references

Sze, V. et al. Hardware for machine learning: challenges and opportunities. In: 2017 IEEE Custom Integrated Circuits Conference (CICC) 1–8 (IEEE, 2017), https://doi.org/10.1109/CICC.2017.7993626https://doi.org/10.1109/CICC.2017.7993626.

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

G. Wetzstein , 等 . Inference in artificial intelligence with deep optics and photonics . Nature , 2020 . 588 39 -47 . DOI:10.1038/s41586-020-2973-6http://doi.org/10.1038/s41586-020-2973-6 .

D. Brunner , 等 . Parallel photonic information processing at gigabyte per second data rates using transient states . Nat. Commun. , 2013 . 4 1364 DOI:10.1038/ncomms2368http://doi.org/10.1038/ncomms2368 .

Y. S. Abu-Mostafa , D. Psaltis . Optical neural computers . Sci. Am. , 1987 . 256 88 -95 . DOI:10.1038/scientificamerican0387-88http://doi.org/10.1038/scientificamerican0387-88 .

Denz, C. Optical Neural Networks. (Springer Science & Business Media, Wiesbaden, 2013).

Y. C. Shen , 等 . Deep learning with coherent nanophotonic circuits . Nat. Photonics , 2017 . 11 441 -446 . DOI:10.1038/nphoton.2017.93http://doi.org/10.1038/nphoton.2017.93 .

U. Teğin , 等 . Scalable optical learning operator . Nat. Comput. Sci. , 2021 . 1 542 -549 . DOI:10.1038/s43588-021-00112-0http://doi.org/10.1038/s43588-021-00112-0 .

X. Lin , 等 . All-optical machine learning using diffractive deep neural networks . Science , 2018 . 361 1004 -1008 . DOI:10.1126/science.aat8084http://doi.org/10.1126/science.aat8084 .

J. L. Chang , 等 . Hybrid optical-electronic convolutional neural networks with optimized diffractive optics for image classification . Sci. Rep. , 2018 . 8 12324 DOI:10.1038/s41598-018-30619-yhttp://doi.org/10.1038/s41598-018-30619-y .

C. Liu , 等 . A programmable diffractive deep neural network based on a digital-coding metasurface array . Nat. Electron. , 2022 . 5 113 -122 . DOI:10.1038/s41928-022-00719-9http://doi.org/10.1038/s41928-022-00719-9 .

J. X. Li , 等 . Spectrally encoded single-pixel machine vision using diffractive networks . Sci. Adv. , 2021 . 7 DOI:10.1126/sciadv.abd7690http://doi.org/10.1126/sciadv.abd7690 .

Mengu, D. & Ozcan, A. All-optical phase recovery: diffractive computing for quantitative phase imaging. Adv. Opt. Mater. https://doi.org/10.1002/adom.202200281https://doi.org/10.1002/adom.202200281 (2022).

Y. Luo , 等 . Computational imaging without a computer: seeing through random diffusers at the speed of light . eLight , 2022 . 2 4 DOI:10.1186/s43593-022-00012-4http://doi.org/10.1186/s43593-022-00012-4 .

O. Kulce , 等 . All-optical information-processing capacity of diffractive surfaces . Light.: Sci. Appl. , 2021 . 10 25 DOI:10.1038/s41377-020-00439-9http://doi.org/10.1038/s41377-020-00439-9 .

O. Kulce , 等 . All-optical synthesis of an arbitrary linear transformation using diffractive surfaces . Light.: Sci. Appl. , 2021 . 10 196 DOI:10.1038/s41377-021-00623-5http://doi.org/10.1038/s41377-021-00623-5 .

J. X. Li , 等 . Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network . Light.: Sci. Appl. , 2022 . 11 153 DOI:10.1038/s41377-022-00849-xhttp://doi.org/10.1038/s41377-022-00849-x .

M. V. Lauve . Die Freiheitsgrade von strahlenbündeln . Ann. Phys. , 1914 . 349 1197 -1212 . DOI:10.1002/andp.19143491606http://doi.org/10.1002/andp.19143491606 .

C. E. Shannon . A mathematical theory of communication . Bell Syst. Tech. J. , 1948 . 27 379 -423 . DOI:10.1002/j.1538-7305.1948.tb01338.xhttp://doi.org/10.1002/j.1538-7305.1948.tb01338.x .

Gabor, D. in Progress in Optics (ed Wolf, E. ) 109–153 (Elsevier, 1961).

G. T. Di Francia . Degrees of freedom of an image . J. Opt. Soc. Am. , 1969 . 59 799 -804 . DOI:10.1364/JOSA.59.000799http://doi.org/10.1364/JOSA.59.000799 .

W. Lukosz . Optical systems with resolving powers exceeding the classical limit . J. Opt. Soc. Am. , 1966 . 56 1463 -1471 . DOI:10.1364/JOSA.56.001463http://doi.org/10.1364/JOSA.56.001463 .

Lukosz, W. Optical systems with resolving powers exceeding the classical limit. Ⅱ. J. Opt. Soc. Am. 57, 932–941 (1967).

D. A. B. Miller . Waves, modes, communications, and optics: a tutorial . Adv. Opt. Photonics , 2019 . 11 679 -825 . DOI:10.1364/AOP.11.000679http://doi.org/10.1364/AOP.11.000679 .

D. A. B. Miller . All linear optical devices are mode converters . Opt. Express , 2012 . 20 23985 -23993 . DOI:10.1364/OE.20.023985http://doi.org/10.1364/OE.20.023985 .

A. W. Lohmann , D. P. Paris . Superresolution for nonbirefringent objects . Appl. Opt. , 1964 . 3 1037 -1043 . DOI:10.1364/AO.3.001037http://doi.org/10.1364/AO.3.001037 .

Z. Y. Chen , 等 . Use of polarization freedom beyond polarization-division multiplexing to support high-speed and spectral-efficient data transmission . Light.: Sci. Appl. , 2017 . 6 DOI:10.1038/lsa.2016.207http://doi.org/10.1038/lsa.2016.207 .

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据