Chinese Journal of Lasers, Volume. 50, Issue 11, 1101013(2023)
Machine Learning Predicting Mode Properties for Multi-Layer Active Fibers
[1] Nilsson J, Payne D N. High-power fiber lasers[J]. Science, 332, 921-922(2011).
[2] Zervas M N, Codemard C A. High power fiber lasers: a review[J]. IEEE Journal of Selected Topics in Quantum Electronics, 20, 219-241(2014).
[3] Zhou P, Leng J Y, Xiao H et al. High average power fiber lasers: research progress and future prospect[J]. Chinese Journal of Lasers, 48, 2000001(2021).
[4] Tang H, Shen Y, Long L Y. Analysis and prospects for development of laser science and technology in China from the perspective of national science foundation of China[J]. Chinese Journal of Lasers, 50, 0200001(2023).
[5] Shao C Y, Yu C L, Hu L L. Radiation-resistant active fibers for space applications[J]. Chinese Journal of Lasers, 47, 0500014(2020).
[6] Chen X, Yao T F, Huang L J et al. Functional fibers and functional fiber-based components for high-power lasers[J]. Advanced Fiber Materials, 5, 59-106(2023).
[7] Wang X L, Wen Y J, Zhang H W et al. Ytterbium-doped core-diameter-variable fiber laser: current situation and develop tendency[J]. Chinese Journal of Lasers, 49, 2100001(2022).
[8] Kobyakov A, Sauer M, Chowdhury D. Stimulated Brillouin scattering in optical fibers[J]. Advances in Optics and Photonics, 2, 1-59(2010).
[9] Liu W, Ma P F, Lü H B et al. General analysis of SRS-limited high-power fiber lasers and design strategy[J]. Optics Express, 24, 26715-26721(2016).
[10] Jauregui C, Stihler C, Limpert J. Transverse mode instability[J]. Advances in Optics and Photonics, 12, 429-484(2020).
[11] Fallahkhair A, Li K S, Murphy T E. Vector finite difference modesolver for anisotropic dielectric waveguides[J]. Journal of Lightwave Technology, 26, 1423-1431(2008).
[12] Cucinotta A, Selleri S, Vincetti L et al. Holey fiber analysis through the finite-element method[J]. IEEE Photonics Technology Letters, 14, 1530-1532(2002).
[13] García S, Gasulla I. Universal characteristic equation for multi-layer optical fibers[J]. IEEE Journal of Selected Topics in Quantum Electronics, 26, 4300111(2020).
[14] Pu G Q, Yi L L, Zhang L et al. Intelligent programmable mode-locked fiber laser with a human-like algorithm[J]. Optica, 6, 362-369(2019).
[15] Pu G Q, Yi L L, Zhang L et al. Intelligent control of mode-locked femtosecond pulses by time-stretch-assisted real-time spectral analysis[J]. Light: Science & Applications, 9, 1-8(2020).
[16] Wei X M, Jing J C, Shen Y C et al. Harnessing a multi-dimensional fibre laser using genetic wavefront shaping[J]. Light: Science & Applications, 9, 1-10(2020).
[17] Jiang M, Wu H S, An Y et al. Fiber laser development enabled by machine learning: review and prospect[J]. PhotoniX, 3, 16(2022).
[18] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 521, 436-444(2015).
[19] Barbastathis G, Ozcan A, Situ G H. On the use of deep learning for computational imaging[J]. Optica, 6, 921-943(2019).
[20] Wu J L, Guo Z H, Chen X F et al. Three-dimensional measurement method of light field imaging based on deep learning[J]. Chinese Journal of Lasers, 47, 1204005(2020).
[21] Zuo C, Feng S J, Zhang X Y et al. Deep learning based computational imaging: status, challenges, and future[J]. Acta Optica Sinica, 40, 0111003(2020).
[22] Wang Y T, Zhou H Q, Yan J X et al. Advances in computational optics based on deep learning[J]. Chinese Journal of Lasers, 48, 1918004(2021).
[23] Lohani S, Knutson E M, O’Donnell M et al. On the use of deep neural networks in optical communications[J]. Applied Optics, 57, 4180-4190(2018).
[24] Jiang J Q, Chen M K, Fan J A. Deep neural networks for the evaluation and design of photonic devices[J]. Nature Reviews Materials, 6, 679-700(2021).
[25] Wang X Y, Wu T Y, Dong C et al. Integrating deep learning to achieve phase compensation for free-space orbital-angular-momentum-encoded quantum key distribution under atmospheric turbulence[J]. Photonics Research, 9, B9-B17(2021).
[26] Wang W H, Zhao X, Jiang Z X et al. Deep learning-based scattering removal of light field imaging[J]. Chinese Optics Letters, 20, 041101(2022).
[27] Saba A, Gigli C, Ayoub A B et al. Physics-informed neural networks for diffraction tomography[J]. Advanced Photonics, 4, 066001(2022).
[28] Borhani N, Kakkava E, Moser C et al. Learning to see through multimode fibers[J]. Optica, 5, 960-966(2018).
[29] Meng L, Hu H F, Hu J Z et al. Image reconstruction of multimode fiber scattering media based on deep learning[J]. Chinese Journal of Lasers, 47, 1206005(2020).
[30] Hu J K, Guo X J, Li J P et al. Deep learning-based recognition of modes and mode groups in multimode optical fiber communication system[J]. Acta Optica Sinica, 42, 0406004(2022).
[31] An Y, Huang L J, Li J et al. Learning to decompose the modes in few-mode fibers with deep convolutional neural network[J]. Optics Express, 27, 10127-10137(2019).
[32] Yan B R, Zhang J Y, Wang M G et al. Degenerated mode decomposition with convolutional neural network for few-mode fibers[J]. Optics & Laser Technology, 154, 108287(2022).
[33] Tian Z C, Pei L, Wang J S et al. Accurate mode decomposition for ring core fibers based on deep learning[J]. Acta Optica Sinica, 43, 0406003(2023).
[34] An Y, Li J, Huang L J et al. Deep learning enabled superfast and accurate M2 evaluation for fiber beams[J]. Optics Express, 27, 18683-18694(2019).
[35] Ma Z H, Gong R, Li B et al. Optical fiber multi-parameter measurement based on machine learning[J]. Acta Optica Sinica, 42, 2006003(2022).
[36] Chugh S, Gulistan A, Ghosh S et al. Machine learning approach for computing optical properties of a photonic crystal fiber[J]. Optics Express, 27, 36414-36425(2019).
[37] Jabin M A, Fok M P. Prediction of 12 photonic crystal fiber optical properties using MLP in deep learning[J]. IEEE Photonics Technology Letters, 34, 391-394(2022).
[38] Yuan S Y, Chen S C, Yang J L et al. Efficient calculation of optical properties of suspended-core fiber via a machine learning algorithm[J]. Applied Optics, 61, 5714-5721(2022).
[39] Zhang Z L, Zhang F F, Lin X F et al. Home-made confined-doped fiber with 3-kW all-fiber laser oscillating output[J]. Acta Physica Sinica, 69, 234205(2020).
[40] Wu H S, Li R X, Xiao H et al. First demonstration of a bidirectional tandem-pumped high-brightness 8 kW level confined-doped fiber amplifier[J]. Journal of Lightwave Technology, 40, 5673-5681(2022).
[41] Huang L J, Wu H S, Li R X et al. Homemade confined-doped fiber for 10 kW level fiber laser output with good beam quality[J]. High Power Laser and Particle Beams, 34, 111002(2022).
[42] Hautakorpi M, Kaivola M. Modal analysis of M-type-dielectric-profile optical fibers in the weakly guiding approximation[J]. Journal of the Optical Society of America A, 22, 1163-1169(2005).
[43] Jain D, George M A, Harris B et al. Approximate modal cut-off wavelengths and the V-parameter for M-type optical fibers and its novel applications[J]. Journal of Lightwave Technology, 39, 4478-4488(2021).
[44] Lin X F, Zhang Z L, Xing Y B et al. Near-single-mode 2-kW fiber amplifier based on M-type ytterbium-doped fiber[J]. Acta Physica Sinica, 71, 034205(2022).
[45] Yoo S, Webb A S, Boyland A J et al. Linearly polarized ytterbium-doped fiber laser in a pedestal design with aluminosilicate inner cladding[J]. Laser Physics Letters, 8, 453-457(2011).
[46] Saha M, Das Chowdhury S, Shekhar N K et al. Yb-doped pedestal silica fiber through vapor phase doping for pulsed laser applications[J]. IEEE Photonics Technology Letters, 28, 1022-1025(2016).
[47] Liu R, Yan D P, Fan Z J et al. Fabrication and 1046 nm laser behaviors of Yb-doped phosphosilicate binary fiber with a pedestal structure[J]. Optical Materials Express, 10, 464-471(2020).
[48] Jain D, Jung Y, Nunez-Velazquez M et al. Extending single mode performance of all-solid large-mode-area single trench fiber[J]. Optics Express, 22, 31078-31091(2014).
[49] Zhang H H, Zhang W L, Wang Z et al. Decoherence of fiber light sources using a single-trench fiber[J]. Chinese Physics B, 29, 124210(2020).
[50] An Y, Yang H, Chen X et al. Seeing the strong suppression of higher order modes in single trench fiber using the S2 technique[J]. Optics Letters, 48, 61-64(2023).
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Yi An, Min Jiang, Xiao Chen, Jun Li, Rongtao Su, Liangjin Huang, Zhiyong Pan, Jinyong Leng, Zongfu Jiang, Pu Zhou. Machine Learning Predicting Mode Properties for Multi-Layer Active Fibers[J]. Chinese Journal of Lasers, 2023, 50(11): 1101013
Category: laser devices and laser physics
Received: Jan. 30, 2023
Accepted: Mar. 15, 2023
Published Online: May. 29, 2023
The Author Email: Huang Liangjin (hlj203@nudt.edu.cn), Zhou Pu (zhoupu203@163.com)