Intelligent nanophotonics: when machine learning sheds light
Abstract The synergistic development of nanophotonics and machine learning has inspired tremendous innovations in both fields in the past decade. In diverse photonics research, deep-learning methods using artificial neural networks become the key game changer that greatly facilitates rapid nanophoto...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | eLight |
| Online Access: | https://doi.org/10.1186/s43593-025-00085-x |
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| author | Nanfan Wu Yuxiang Sun Jingtian Hu Chuang Yang Zichun Bai Fenglei Wang Xingzhe Cui Shengjie He Yingjie Li Chi Zhang Ke Xu Jun Guan Shumin Xiao Qinghai Song |
| author_facet | Nanfan Wu Yuxiang Sun Jingtian Hu Chuang Yang Zichun Bai Fenglei Wang Xingzhe Cui Shengjie He Yingjie Li Chi Zhang Ke Xu Jun Guan Shumin Xiao Qinghai Song |
| author_sort | Nanfan Wu |
| collection | DOAJ |
| description | Abstract The synergistic development of nanophotonics and machine learning has inspired tremendous innovations in both fields in the past decade. In diverse photonics research, deep-learning methods using artificial neural networks become the key game changer that greatly facilitates rapid nanophotonics design and the versatile processing of optical information. Moreover, optical computing platforms that perform calculations through light propagation are receiving tremendous interest as next-generation machine-learning hardware with advantages in computing speed, energy efficiency, and parallelism. This review summarizes the current state-of-the-art nanophotonic devices enabled by machine learning and analyzes the longstanding challenges that must be overcome to make an impact on technology. We also discuss the opportunities of intelligent photonics in applications such as computational imaging/sensing and machine vision. The intersection of nanophotonics with deep learning holds tremendous implications for transformative technologies ranging from internet of things to smart health. Lastly, we provide our perspective on the pressing challenges in intelligent photonics that must be tackled to advance this field to the next level and the vast opportunities for multidisciplinary collaboration. |
| format | Article |
| id | doaj-art-e1aa25ed936942cdbb8a8ffda8004557 |
| institution | DOAJ |
| issn | 2097-1710 2662-8643 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | eLight |
| spelling | doaj-art-e1aa25ed936942cdbb8a8ffda80045572025-08-20T03:06:48ZengSpringerOpeneLight2097-17102662-86432025-04-015112110.1186/s43593-025-00085-xIntelligent nanophotonics: when machine learning sheds lightNanfan Wu0Yuxiang Sun1Jingtian Hu2Chuang Yang3Zichun Bai4Fenglei Wang5Xingzhe Cui6Shengjie He7Yingjie Li8Chi Zhang9Ke Xu10Jun Guan11Shumin Xiao12Qinghai Song13Ministry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems, Harbin Institute of TechnologyMinistry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems, Harbin Institute of TechnologyMinistry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems, Harbin Institute of TechnologyMinistry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems, Harbin Institute of TechnologySchool of Science and Engineering, The Chinese University of Hong KongMinistry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems, Harbin Institute of TechnologyMinistry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems, Harbin Institute of TechnologyMinistry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems, Harbin Institute of TechnologyPengcheng LaboratoryMinistry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems, Harbin Institute of TechnologyMinistry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems, Harbin Institute of TechnologySchool of Science and Engineering, The Chinese University of Hong KongMinistry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems, Harbin Institute of TechnologyMinistry of Industry and Information Technology Key Lab of Micro-Nano Optoelectronic Information System, Guangdong Provincial Key Laboratory of Semiconductor Optoelectronic Materials and Intelligent Photonic Systems, Harbin Institute of TechnologyAbstract The synergistic development of nanophotonics and machine learning has inspired tremendous innovations in both fields in the past decade. In diverse photonics research, deep-learning methods using artificial neural networks become the key game changer that greatly facilitates rapid nanophotonics design and the versatile processing of optical information. Moreover, optical computing platforms that perform calculations through light propagation are receiving tremendous interest as next-generation machine-learning hardware with advantages in computing speed, energy efficiency, and parallelism. This review summarizes the current state-of-the-art nanophotonic devices enabled by machine learning and analyzes the longstanding challenges that must be overcome to make an impact on technology. We also discuss the opportunities of intelligent photonics in applications such as computational imaging/sensing and machine vision. The intersection of nanophotonics with deep learning holds tremendous implications for transformative technologies ranging from internet of things to smart health. Lastly, we provide our perspective on the pressing challenges in intelligent photonics that must be tackled to advance this field to the next level and the vast opportunities for multidisciplinary collaboration.https://doi.org/10.1186/s43593-025-00085-x |
| spellingShingle | Nanfan Wu Yuxiang Sun Jingtian Hu Chuang Yang Zichun Bai Fenglei Wang Xingzhe Cui Shengjie He Yingjie Li Chi Zhang Ke Xu Jun Guan Shumin Xiao Qinghai Song Intelligent nanophotonics: when machine learning sheds light eLight |
| title | Intelligent nanophotonics: when machine learning sheds light |
| title_full | Intelligent nanophotonics: when machine learning sheds light |
| title_fullStr | Intelligent nanophotonics: when machine learning sheds light |
| title_full_unstemmed | Intelligent nanophotonics: when machine learning sheds light |
| title_short | Intelligent nanophotonics: when machine learning sheds light |
| title_sort | intelligent nanophotonics when machine learning sheds light |
| url | https://doi.org/10.1186/s43593-025-00085-x |
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