Precision Measurement of Fractional Orbital Angular Momentum

Deng, Duo; Lin, Muchun; Li, Yudong; Zhao, Hua

doi:10.1103/physrevapplied.12.014048

Cited by 59 publications

(15 citation statements)

References 28 publications

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“…Various methods have been proposed for measuring and recognizing OAM modes separated by a fractional interval. Some representative ways are based on specifically designed phase elements, which range from a conventional fork hologram 25 , 26 to an annular grating 27 , multifocal arrays 28 , and mode sorters utilizing optical coordinate transformation 29 , 30 . These methods can obtain quantitative values of fractional OAM by measuring either intensity or focal spot displacement, but there are drawbacks to be improved, such as a low resolution, precision, and a need for additional optical elements.…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications

2021

Sci Rep

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Structured light with spatial degrees of freedom (DoF) is considered a potential solution to address the unprecedented demand for data traffic, but there is a limit to effectively improving the communication capacity by its integer quantization. We propose a data transmission system using fractional mode encoding and deep-learning decoding. Spatial modes of Bessel-Gaussian beams separated by fractional intervals are employed to represent 8-bit symbols. Data encoded by switching phase holograms is efficiently decoded by a deep-learning classifier that only requires the intensity profile of transmitted modes. Our results show that the trained model can simultaneously recognize two independent DoF without any mode sorter and precisely detect small differences between fractional modes. Moreover, the proposed scheme successfully achieves image transmission despite its densely packed mode space. This research will present a new approach to realizing higher data rates for advanced optical communication systems.

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Section: Introductionmentioning

confidence: 99%

Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications

2021

Sci Rep

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“…The former two objectives can be achieved by using structured light sources, and the latter can be addressed by implementing deep learning algorithms. Studies have shown that imparting a topological charge to beams used for optical imaging enhances optical imaging systems [18][19][20]. It has also been shown that vortex beams have more transmitivity through scattering media compared to Gaussian beams [21].…”

Section: Introductionmentioning

confidence: 99%

Detecting objects behind scattering media using vortex beams and deep learning

Balasubramaniam

Biton

Arnon

2021

Preprint

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Reconstructing objects behind scattering media is a challenging issue with applications in biomedical imaging, non-distractive testing, computer-assisted surgery, and autonomous vehicular systems. Such systems’ main challenge is the multiple scattering of the photons in the angular and spatial domain, which results in a blurred image. Previous works try to improve the reconstructing ability using deep learning algorithms, with some success. We enhance these methods by illuminating the set-up using several modes of vortex beams obtaining a series of time-gated images corresponding to each mode. The images are accurately reconstructed using a deep learning algorithm by analyzing the pattern captured in the camera. This study shows that using vortex beams instead of Gaussian beams enhances the deep learning algorithm’s image reconstruction ability in terms of the peak signal-to-noise ratio (PSNR) by ~ 2.5 dB and ~1 dB when low and high scattering scatterers are used respectively.

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“…Owing to the intriguing properties of optical vortices, OAM beams have found numerous applications in particle guiding and trapping, optical communication, quantum computation, holography and high-precision imaging, and novel light-matter interactions with topological materials [2][3][4][5][6][7][8] . Only quite recently have optical beams carrying a fractional orbital angular momentum (FOAM) 9,10 attracted considerable attention for their unusual properties and potential applications 11,12 . FOAM beams display subtle topological features 13 , and their propagation manifests some unusual mathematics of transfinite numbers 14 .…”

Section: Introductionmentioning

confidence: 99%

Ultrafast control of fractional orbital angular momentum of microlaser emissions

et al. 2020

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On-chip integrated laser sources of structured light carrying fractional orbital angular momentum (FOAM) are highly desirable for the forefront development of optical communication and quantum information–processing technologies. While integrated vortex beam generators have been previously demonstrated in different optical settings, ultrafast control and sweep of FOAM light with low-power control, suitable for high-speed optical communication and computing, remains challenging. Here we demonstrate fast control of the FOAM from a vortex semiconductor microlaser based on fast transient mixing of integer laser vorticities induced by a control pulse. A continuous FOAM sweep between charge 0 and charge +2 is demonstrated in a 100 ps time window, with the ultimate speed limit being established by the carrier recombination time in the gain medium. Our results provide a new route to generating vortex microlasers carrying FOAM that are switchable at GHz frequencies by an ultrafast control pulse.

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Precision Measurement of Fractional Orbital Angular Momentum

Cited by 59 publications

References 28 publications

Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications

Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications

Detecting objects behind scattering media using vortex beams and deep learning

Ultrafast control of fractional orbital angular momentum of microlaser emissions

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