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Performing optical logic operations by a diffractive neural network

Qian, Chao and Lin, Xiao and Lin, Xiaobin and Xu, Jian and Sun, Yang and Li, Erping and Zhang, Baile and Chen, Hongsheng (2020) Performing optical logic operations by a diffractive neural network. Light: Science & Applications, 9 . Art. No. 59. ISSN 2047-7538. https://resolver.caltech.edu/CaltechAUTHORS:20200504-143734032

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Abstract

Optical logic operations lie at the heart of optical computing, and they enable many applications such as ultrahigh-speed information processing. However, the reported optical logic gates rely heavily on the precise control of input light signals, including their phase difference, polarization, and intensity and the size of the incident beams. Due to the complexity and difficulty in these precise controls, the two output optical logic states may suffer from an inherent instability and a low contrast ratio of intensity. Moreover, the miniaturization of optical logic gates becomes difficult if the extra bulky apparatus for these controls is considered. As such, it is desirable to get rid of these complicated controls and to achieve full logic functionality in a compact photonic system. Such a goal remains challenging. Here, we introduce a simple yet universal design strategy, capable of using plane waves as the incident signal, to perform optical logic operations via a diffractive neural network. Physically, the incident plane wave is first spatially encoded by a specific logic operation at the input layer and further decoded through the hidden layers, namely, a compound Huygens’ metasurface. That is, the judiciously designed metasurface scatters the encoded light into one of two small designated areas at the output layer, which provides the information of output logic states. Importantly, after training of the diffractive neural network, all seven basic types of optical logic operations can be realized by the same metasurface. As a conceptual illustration, three logic operations (NOT, OR, and AND) are experimentally demonstrated at microwave frequencies.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1038/s41377-020-0303-2DOIArticle
ORCID:
AuthorORCID
Xu, Jian0000-0002-4743-2471
Li, Erping0000-0002-5006-7399
Zhang, Baile0000-0003-1673-5901
Chen, Hongsheng0000-0002-5735-9781
Additional Information:© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Received 25 November 2019; Revised 19 March 2020; Accepted 24 March 2020; Published 13 April 2020. The work at Zhejiang University was sponsored by the National Natural Science Foundation of China (NNSFC) under Grants Nos. 61625502, 11961141010, and 61975176, the Top-Notch Young Talents Programme of China, the Fundamental Research Funds for the Central Universities, Nanyang Technological University for NAP Start-Up Grant, and the Singapore Ministry of Education (Grant Nos. MOE2018-T2-1-022 (S), MOE2016-T3-1-006 and Tier 1 RG174/16 (S)). C.Q. was supported by the Chinese Scholarship Council (CSC No. 201906320294) and Zhejiang University Academic Award for Outstanding Doctoral Candidates. Author Contributions: C.Q. conceived the idea and conducted the numerical simulation and experiment; Y.S. helped prepare the experimental samples. C.Q. and X.L. interpreted detailed results and contributed extensively to the writing of the manuscript. X.L., B.Z. and H.C. supervised the project. All members contributed to the discussion and analysis of the results. The authors declare that they have no conflict of interest.
Funders:
Funding AgencyGrant Number
National Natural Science Foundation of China61625502
National Natural Science Foundation of China11961141010
National Natural Science Foundation of China61975176
Top-Notch Young Talents Programme of ChinaUNSPECIFIED
Fundamental Research Funds for the Central UniversitiesUNSPECIFIED
Nanyang Technological UniversityUNSPECIFIED
Ministry of Education (Singapore)MOE2018-T2-1-022 (S)
Ministry of Education (Singapore)MOE2016-T3-1-006
Ministry of Education (Singapore)Tier 1 RG174/16 (S)
Chinese Scholarship Council201906320294
Zhejiang UniversityUNSPECIFIED
Subject Keywords:Electronics, photonics and device physics; Metamaterials
Record Number:CaltechAUTHORS:20200504-143734032
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200504-143734032
Official Citation:Qian, C., Lin, X., Lin, X. et al. Performing optical logic operations by a diffractive neural network. Light Sci Appl 9, 59 (2020). https://doi.org/10.1038/s41377-020-0303-2
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:102984
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:04 May 2020 22:01
Last Modified:04 May 2020 22:01

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