Optical diffractive deep neural network-based orbital angular momentum mode add–drop multiplexer

Xiong, Wenjie; Huang, Zebin; Wang, Peipei; Wang, Xinrou; He, Yanliang; Wang, Chaofeng; Liu, Junmin; Ye, Huapeng; Fan, Dianyuan; Chen, Shuqing

doi:10.1364/oe.441905

Cited by 13 publications

(3 citation statements)

References 50 publications

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“…While some modes are dropped from the region where the grating is eliminated, the modes can be added by incident in the same region with an inverse optical axis. In this way, the azimuthal beam shaper acts as an add-drop multiplexer for OAM modes 32 , 33 …”

Section: Resultsmentioning

confidence: 99%

Azimuthal beam shaping in orbital angular momentum basis

Lin,

Zhong,

et al. 2024

Adv. Photon. Nexus

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Shaping the light beam is always essential for laser technology and its applications. Among the shaping technologies, shaping the laser in its Fourier domain is a widely used and effective method, such as a pulse shaper, or a 4f system with a phase mask or an iris in between. Orbital angular momentum (OAM) modes spectrum, the Fourier transform of the light field in azimuth, provides a perspective for shaping the light. Here, we propose and experimentally demonstrate a shaping strategy for the azimuthal field by modulating the complex amplitude of the OAM mode spectrum. The scheme utilizes multi-plane light conversion technology and consists only of a spatial light modulator and a mirror. Multiple functions, including beam rotating, beam splitting/combining in azimuth, and OAM mode filtering, are demonstrated. Our work provides a compact and programmable solution for modulating the OAM mode spectrum and shaping beams in azimuth.

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Section: Resultsmentioning

confidence: 99%

Azimuthal beam shaping in orbital angular momentum basis

Lin,

Zhong,

et al. 2024

Adv. Photon. Nexus

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“…These networks have the capacity to theoretically approximate functional relationships in any input-output domain. In 2021, Xiong et al [155] introduced an OAM add-drop multiplexer (OADM) utilizing an optical diffractive deep neural network (ODNN). By leveraging the powerful data-fitting capability of deep neural networks and the intricate light-field manipulation abilities of multilayer diffraction screens, a five-layer ODNN was constructed.…”

Section: Oam Channel Adding/extractingmentioning

confidence: 99%

Research Progress on Router Devices for the OAM Optical Communication

Wang,

Zhang,

Tian

et al. 2024

Sensors

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Vortex beams carrying orbital angular momentum (OAM) provide a new degree of freedom for light waves in addition to the traditional degrees of freedom, such as intensity, phase, frequency, time, and polarization. Due to the theoretically unlimited orthogonal states, the physical dimension of OAM is capable of addressing the problem of low information capacity. With the advancement of the OAM optical communication technology, OAM router devices (OAM-RDs) have played a key role in significantly improving the flexibility and practicability of communication systems. In this review, major breakthroughs in the OAM-RDs are summarized, and the latest technological standing is examined. Additionally, a detailed account of the recent works published on techniques related to the OAM-RDs has been categorized into five areas: channel multicasting, channel switching, channel filtering, channel hopping, and channel adding/extracting. Meanwhile, the principles, research methods, advantages, and disadvantages are discussed and summarized in depth while analyzing the future development trends and prospects of the OAM-RDs.

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“…However, while DN 2 s commonly rely on phase modulation for information processing, the inputs considered in current experimental implementations 25,35,36,38 are intensity distributions, omitting the phase of the incident fields. Although there are several proposals describing optical neural networks processing complexvalued inputs in recent literature, these works implement the neural networks either in-silico 11,39,40 , as digital neural networks, or they are restricted to numerical models of D 2 N 2 s. The numerically proposed optical networks are focused on performing logical operations on vortex beams 41 or orbital angular momentum multiplexing schemes 42,43 and hence specialized on operations with radially symmetric wavefronts, rather than the retrieval of arbitrary wavefronts, which is the essential problem for imaging systems.…”

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confidence: 99%

Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks

Goi

Schoenhardt

2022

Nat Commun

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Retrieving the pupil phase of a beam path is a central problem for optical systems across scales, from telescopes, where the phase information allows for aberration correction, to the imaging of near-transparent biological samples in phase contrast microscopy. Current phase retrieval schemes rely on complex digital algorithms that process data acquired from precise wavefront sensors, reconstructing the optical phase information at great expense of computational resources. Here, we present a compact optical-electronic module based on multi-layered diffractive neural networks printed on imaging sensors, capable of directly retrieving Zernike-based pupil phase distributions from an incident point spread function. We demonstrate this concept numerically and experimentally, showing the direct pupil phase retrieval of superpositions of the first 14 Zernike polynomials. The integrability of the diffractive elements with CMOS sensors shows the potential for the direct extraction of the pupil phase information from a detector module without additional digital post-processing.

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Optical diffractive deep neural network-based orbital angular momentum mode add–drop multiplexer

Cited by 13 publications

References 50 publications

Azimuthal beam shaping in orbital angular momentum basis

Azimuthal beam shaping in orbital angular momentum basis

Research Progress on Router Devices for the OAM Optical Communication

Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks

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