Deep learning–based vortex decomposition and switching based on fiber vector eigenmodes
Structured optical fields, such as cylindrical vector (CV) and orbital angular momentum (OAM) modes, have attracted considerable attention due to their polarization singularities and helical phase wavefront structure. However, one of the most critical challenges is still the intelligent generation o...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-06-01
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| Series: | Nanophotonics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nanoph-2023-0202 |
| _version_ | 1797773453600751616 |
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| author | Hou Mengdie Xu Mengjun Xu Jiangtao Lu Jiafeng An Yi Huang Liangjin Zeng Xianglong Pang Fufei Li Jun Yi Lilin |
| author_facet | Hou Mengdie Xu Mengjun Xu Jiangtao Lu Jiafeng An Yi Huang Liangjin Zeng Xianglong Pang Fufei Li Jun Yi Lilin |
| author_sort | Hou Mengdie |
| collection | DOAJ |
| description | Structured optical fields, such as cylindrical vector (CV) and orbital angular momentum (OAM) modes, have attracted considerable attention due to their polarization singularities and helical phase wavefront structure. However, one of the most critical challenges is still the intelligent generation or precise control of these modes. Here, we demonstrate the first simulation and experimental realization of decomposing the CV and OAM modes by reconstructing the multi-view images of projected intensity distribution. Assisted by the deep learning–based stochastic parallel gradient descent (SPGD) algorithm, the modal coefficients and optical field distributions can be retrieved in 1.32 s within an average error of 0.416 % showing high efficiency and accuracy. Especially, the interference pattern and quarter-wave plate are exploited to confirm the phase and distinguish elliptical or circular polarization direction, respectively. The generated donut modes are experimentally decomposed in the CV and OAM modes, where purity of CV modes reaches 99.5 %. Finally, fast switching vortex modes is achieved by electrically driving the polarization controller to deliver diverse CV modes. Our findings may provide a convenient way to characterize and deepen the understanding of CV or OAM modes in view of modal proportions, which is expected of latent applied value on information coding and quantum computation. |
| first_indexed | 2024-03-12T22:06:43Z |
| format | Article |
| id | doaj.art-08e00ad1109646d6896f7961edefedf5 |
| institution | Directory Open Access Journal |
| issn | 2192-8614 |
| language | English |
| last_indexed | 2024-03-12T22:06:43Z |
| publishDate | 2023-06-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nanophotonics |
| spelling | doaj.art-08e00ad1109646d6896f7961edefedf52023-07-24T11:19:09ZengDe GruyterNanophotonics2192-86142023-06-0112153165317710.1515/nanoph-2023-0202Deep learning–based vortex decomposition and switching based on fiber vector eigenmodesHou Mengdie0Xu Mengjun1Xu Jiangtao2Lu Jiafeng3An Yi4Huang Liangjin5Zeng Xianglong6Pang Fufei7Li Jun8Yi Lilin9The Key Lab of Specialty Fiber Optics and Optical Access Network, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai200444, ChinaThe Key Lab of Specialty Fiber Optics and Optical Access Network, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai200444, ChinaThe Key Lab of Specialty Fiber Optics and Optical Access Network, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai200444, ChinaThe Key Lab of Specialty Fiber Optics and Optical Access Network, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai200444, ChinaCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha410073, ChinaNanhu Laser Laboratory, College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha410073, ChinaThe Key Lab of Specialty Fiber Optics and Optical Access Network, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai200444, ChinaThe Key Lab of Specialty Fiber Optics and Optical Access Network, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai200444, ChinaCollege of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha410073, ChinaState Key Laboratory of Advanced Optical Communication Systems and Networks, Shanghai Jiao Tong University, Shanghai200240, ChinaStructured optical fields, such as cylindrical vector (CV) and orbital angular momentum (OAM) modes, have attracted considerable attention due to their polarization singularities and helical phase wavefront structure. However, one of the most critical challenges is still the intelligent generation or precise control of these modes. Here, we demonstrate the first simulation and experimental realization of decomposing the CV and OAM modes by reconstructing the multi-view images of projected intensity distribution. Assisted by the deep learning–based stochastic parallel gradient descent (SPGD) algorithm, the modal coefficients and optical field distributions can be retrieved in 1.32 s within an average error of 0.416 % showing high efficiency and accuracy. Especially, the interference pattern and quarter-wave plate are exploited to confirm the phase and distinguish elliptical or circular polarization direction, respectively. The generated donut modes are experimentally decomposed in the CV and OAM modes, where purity of CV modes reaches 99.5 %. Finally, fast switching vortex modes is achieved by electrically driving the polarization controller to deliver diverse CV modes. Our findings may provide a convenient way to characterize and deepen the understanding of CV or OAM modes in view of modal proportions, which is expected of latent applied value on information coding and quantum computation.https://doi.org/10.1515/nanoph-2023-0202deep learningmode decompositionvector eigenmodesvortex switching |
| spellingShingle | Hou Mengdie Xu Mengjun Xu Jiangtao Lu Jiafeng An Yi Huang Liangjin Zeng Xianglong Pang Fufei Li Jun Yi Lilin Deep learning–based vortex decomposition and switching based on fiber vector eigenmodes Nanophotonics deep learning mode decomposition vector eigenmodes vortex switching |
| title | Deep learning–based vortex decomposition and switching based on fiber vector eigenmodes |
| title_full | Deep learning–based vortex decomposition and switching based on fiber vector eigenmodes |
| title_fullStr | Deep learning–based vortex decomposition and switching based on fiber vector eigenmodes |
| title_full_unstemmed | Deep learning–based vortex decomposition and switching based on fiber vector eigenmodes |
| title_short | Deep learning–based vortex decomposition and switching based on fiber vector eigenmodes |
| title_sort | deep learning based vortex decomposition and switching based on fiber vector eigenmodes |
| topic | deep learning mode decomposition vector eigenmodes vortex switching |
| url | https://doi.org/10.1515/nanoph-2023-0202 |
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