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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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 |
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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 |
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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. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Qian, Chao Lin, Xiao Lin, Xiaobin Xu, Jian Sun, Yang Li, Erping Zhang, Baile Chen, Hongsheng |
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Article |
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Qian, Chao Lin, Xiao Lin, Xiaobin Xu, Jian Sun, Yang Li, Erping Zhang, Baile Chen, Hongsheng |
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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 |
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Performing optical logic operations by a diffractive neural network |
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performing optical logic operations by a diffractive neural network |
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2020 |
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https://hdl.handle.net/10356/138750 |
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1759853152177225728 |