Performing optical logic operations by a diffractive neural network

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...

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Main Authors: Qian, Chao, Lin, Xiao, Lin, Xiaobin, Xu, Jian, Sun, Yang, Li, Erping, Zhang, Baile, Chen, Hongsheng
Other Authors: School of Physical and Mathematical Sciences
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/138750
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1387502023-02-28T19:56:28Z Performing optical logic operations by a diffractive neural network Qian, Chao Lin, Xiao Lin, Xiaobin Xu, Jian Sun, Yang Li, Erping Zhang, Baile Chen, Hongsheng School of Physical and Mathematical Sciences Science::Physics Electronics, Photonics and Device Physics Metamaterials 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. MOE (Min. of Education, S’pore) Published version 2020-05-12T06:46:47Z 2020-05-12T06:46:47Z 2020 Journal Article Qian, C., Lin, X., Lin, X., Xu, J., Sun, Y., Li, E., . . . Chen, H. (2020). Performing optical logic operations by a diffractive neural network. Light: Science and Applications, 9, 59-. doi:10.1038/s41377-020-0303-2 2095-5545 https://hdl.handle.net/10356/138750 10.1038/s41377-020-0303-2 1 9 en Light: Science & Applications © 2020 The Author(s). 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/. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Science::Physics
Electronics, Photonics and Device Physics
Metamaterials
spellingShingle Science::Physics
Electronics, Photonics and Device Physics
Metamaterials
Qian, Chao
Lin, Xiao
Lin, Xiaobin
Xu, Jian
Sun, Yang
Li, Erping
Zhang, Baile
Chen, Hongsheng
Performing optical logic operations by a diffractive neural network
description 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.
author2 School of Physical and Mathematical Sciences
author_facet School of Physical and Mathematical Sciences
Qian, Chao
Lin, Xiao
Lin, Xiaobin
Xu, Jian
Sun, Yang
Li, Erping
Zhang, Baile
Chen, Hongsheng
format Article
author Qian, Chao
Lin, Xiao
Lin, Xiaobin
Xu, Jian
Sun, Yang
Li, Erping
Zhang, Baile
Chen, Hongsheng
author_sort Qian, Chao
title Performing optical logic operations by a diffractive neural network
title_short Performing optical logic operations by a diffractive neural network
title_full Performing optical logic operations by a diffractive neural network
title_fullStr Performing optical logic operations by a diffractive neural network
title_full_unstemmed Performing optical logic operations by a diffractive neural network
title_sort performing optical logic operations by a diffractive neural network
publishDate 2020
url https://hdl.handle.net/10356/138750
_version_ 1759853152177225728