Automatic mode-locking fiber lasers: progress and perspectives

Pu, Guoqing; Zhang, Li; Hu, Weisheng; Yi, Lilin

doi:10.1007/s11432-020-2883-0

Cited by 28 publications

(9 citation statements)

References 97 publications

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“…However, quantitative modeling of stochastic and sensitive birefringence is unclear. Traversal and optimization algorithms have been wildly studied for automatic mode-locking techniques [30,[132][133][134][135][136][137]. In 2014, a research group at the University of Washington achieved birefringence characterization of MLFLs based on machine learning sparse representation in a numerical simulation [138].…”

Section: Mode Locking In Mode-locked Lasermentioning

confidence: 99%

Fiber laser development enabled by machine learning: review and prospect

Jiang

et al. 2022

PhotoniX

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In recent years, machine learning, especially various deep neural networks, as an emerging technique for data analysis and processing, has brought novel insights into the development of fiber lasers, in particular complex, dynamical, or disturbance-sensitive fiber laser systems. This paper highlights recent attractive research that adopted machine learning in the fiber laser field, including design and manipulation for on-demand laser output, prediction and control of nonlinear effects, reconstruction and evaluation of laser properties, as well as robust control for lasers and laser systems. We also comment on the challenges and potential future development.

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Section: Mode Locking In Mode-locked Lasermentioning

confidence: 99%

Fiber laser development enabled by machine learning: review and prospect

Jiang

et al. 2022

PhotoniX

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“…Moving away from metadevices, it should be mentioned that the AI tools become a part of establishing control systems for lasers [177]. In this case, ML algorithms are integrated into feedback mechanisms automatically adjusting position and orientation of laser elements, such as waveplates and polarizers.…”

Section: Self-adapting Metasystemsmentioning

confidence: 99%

Intelligent metaphotonics empowered by machine learning

Krasikov,

Tranter,

Bogdanov

et al. 2021

Preprint

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In the recent years, we observe a dramatic boost of research in photonics empowered by the concepts of machine learning and artificial intelligence. The corresponding photonic systems, to which this new methodology is applied, can range from traditional optical waveguides to nanoantennas and metasurfaces, and these novel approaches underpin the fundamental principles of light-matter interaction developed for a smart design of intelligent photonic devices. Concepts and approaches of artificial intelligence and machine learning penetrate rapidly into the fundamental physics of light, and they provide effective tools for the study of the field of metaphotonics driven by opticallyinduced electric and magnetic resonances. Here, we introduce this new field with its application to metaphotonics and also present a summary of the basic concepts of machine learning with some specific examples developed and demonstrated for metasystems and metasurfaces.

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“…[ 1 ] In this context, significant advancements have emerged in the field of nonlinear optics such as the development of all‐optical switching and modulation devices, [ 2 ] optical limiters, [ 3 ] and laser mode‐locking technologies. [ 4 ] In the regime of continuous wave and long‐pulsed laser irradiation, the observation of optical solitons, has led to applications such as waveguiding, beam‐splitting, frequency conversion, and others. [ 1b ] In the case of ultrashort pulses, new phenomena have been observed due to the finite time response of plasma formation in an optical medium, giving rise to the effect of femtosecond laser filamentation.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Propagation of Laser Light in Plasmonic Nanocomposites

Agiotis

Meunier

2022

Laser & Photonics Reviews

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Plasmonic nanocomposites have been extensively studied for over 3 decades. According to early theoretical studies, a large enhancement of nonlinear response has been predicted. Nonetheless, the promised enhancement of coherent or Kerr-type nonlinearities incurs major limitations related to strong absorption and saturation effects. Accordingly, diffraction-limited interactions and long-scale propagation in ultrafast timescales are undermined despite nanoscale-localized electronic field enhancement. Seemingly only nanometric devices operating at low intensity regimes are benefitted from the foresaid effect. Nonetheless, numerous studies have still exploited configurable properties of the nonlinear response of plasmonic nanocomposites, such as nonlinear absorption, high-order nonlinearities, and diffusive nonlinearities for the development of novel processes within the framework of nonlinear wave propagation described by effective medium properties. In this review, the most recent developments on the understanding of the nonlinear response of metals and plasmonic nanocomposites in various temporal regimes are presented. Furthermore, a synthesis of their experimentally determined third-order properties obtained by various experimental techniques, along with practical considerations, is provided. Computational models used for the formulation of nonlinear wave propagation in plasmonic nanocomposites are subsequently presented, corresponding to applicable concepts. Most recent related applications are concisely summarized, indicating the directions of increasing interest in the field, and outlining shortcomings.

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Automatic mode-locking fiber lasers: progress and perspectives

Cited by 28 publications

References 97 publications

Fiber laser development enabled by machine learning: review and prospect

Fiber laser development enabled by machine learning: review and prospect

Intelligent metaphotonics empowered by machine learning

Nonlinear Propagation of Laser Light in Plasmonic Nanocomposites

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