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...

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Veröffentlicht in:Nanophotonics (Berlin, Germany) Jg. 12; H. 15; S. 3165 - 3177
Hauptverfasser: Hou, Mengdie, Xu, Mengjun, Xu, Jiangtao, Lu, Jiafeng, An, Yi, Huang, Liangjin, Zeng, Xianglong, Pang, Fufei, Li, Jun, Yi, Lilin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Germany De Gruyter 20.07.2023
Walter de Gruyter GmbH
Schlagworte:
Algorithms
Angular momentum
Circular polarization
Decomposition
Deep learning
Image reconstruction
mode decomposition
Quantum computing
Switching
vector eigenmodes
vortex switching
Wave fronts
Wave plates
deep learning
mode decomposition
vortex switching
vector eigenmodes
ISSN:2192-8614, 2192-8606, 2192-8614
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Abstract 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.
AbstractList 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.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.
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.
Author Li, Jun
Lu, Jiafeng
Yi, Lilin
Xu, Jiangtao
Zeng, Xianglong
Pang, Fufei
Huang, Liangjin
Hou, Mengdie
An, Yi
Xu, Mengjun
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  givenname: Mengjun
  surname: Xu
  fullname: Xu, Mengjun
  email: stella@shu.edu.cn
  organization: The Key Lab of Specialty Fiber Optics and Optical Access Network, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200444, China
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  givenname: Jiangtao
  surname: Xu
  fullname: Xu, Jiangtao
  email: tsyjxjt@shu.edu.cn
  organization: The Key Lab of Specialty Fiber Optics and Optical Access Network, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai 200444, China
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  surname: Huang
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  email: hlj203@nudt.edu.cn
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  orcidid: 0000-0001-7817-173X
  surname: Zeng
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/39635056$$D View this record in MEDLINE/PubMed
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Issue 15
Keywords deep learning
mode decomposition
vortex switching
vector eigenmodes
Language English
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Snippet Structured optical fields, such as cylindrical vector (CV) and orbital angular momentum (OAM) modes, have attracted considerable attention due to their...
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StartPage 3165
SubjectTerms Algorithms
Angular momentum
Circular polarization
Decomposition
Deep learning
Image reconstruction
mode decomposition
Quantum computing
Switching
vector eigenmodes
vortex switching
Wave fronts
Wave plates
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Title Deep learning–based vortex decomposition and switching based on fiber vector eigenmodes
URI https://www.degruyter.com/doi/10.1515/nanoph-2023-0202
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