Intelligent optoelectronic processor for orbital angular momentum spectrum measurement
doi: 10.1186/s43074-022-00079-9
Intelligent optoelectronic processor for orbital angular momentum spectrum measurement
-
-
关键词:
-
[1] Willner AE, Pang K, Song H, Zou K, Zhou H. Orbital angular momentum of light for communications. Appl Phys Rev. 2021;8:041312. [2] Yuanjie Y, Yuxuan R, Mingzhou C, Yoshihiko A, Carmelo R-G. Optical trapping with structured light: a review. Adv. Photonics 3 (2021). [3] Erhard M, Fickler R, Krenn M, Zeilinger A. Twisted photons: new quantum perspectives in high dimensions. Light Sci Appl. 2018;7:17146–6. [4] Xie Z, et al. Ultra-broadband on-chip twisted light emitter for optical communications. Light Sci Appl. 2018;7:18001–1. [5] Lin Z, Hu J, Chen Y, Brès C-S, Yu S (2022) arXiv:2206.12883 [6] Hickmann JM, Fonseca EJS, Soares WC, Chávez-Cerda S. Unveiling a truncated Optical Lattice Associated with a triangular aperture using light’s Orbital Angular Momentum. Phys Rev Lett. 2010;105:053904. [7] Lv Y, et al. Sorting orbital angular momentum of photons through a multi-ring azimuthal-quadratic phase. Opt Lett. 2022;47:5032–5. [8] Wen Y, et al. Spiral Transformation for high-resolution and efficient sorting of Optical Vortex Modes. Phys Rev Lett. 2018;120:193904. [9] Grillo V, et al. Measuring the orbital angular momentum spectrum of an electron beam. Nat Commun. 2017;8:15536. [10] Fu S, et al. Universal orbital angular momentum spectrum analyzer for beams. PhotoniX. 2020;1:19. [11] D’Errico A, D’Amelio R, Piccirillo B, Cardano F, Marrucci L. Measuring the complex orbital angular momentum spectrum and spatial mode decomposition of structured light beams. Optica. 2017;4:1350–7. [12] Schulze C, Dudley A, Flamm D, Duparré M, Forbes A. Measurement of the orbital angular momentum density of light by modal decomposition. New J Phys. 2013;15:073025. [13] Zhou H-L, et al. Orbital angular momentum complex spectrum analyzer for vortex light based on the rotational Doppler effect. Light Sci Appl. 2017;6:e16251–1. [14] Malik M, et al. Direct measurement of a 27-dimensional orbital-angular-momentum state vector. Nat Commun. 2014;5:3115. [15] Chen P, et al. Digitalizing self-assembled Chiral Superstructures for Optical Vortex Processing. Adv Mat. 2018;30:1705865. [16] Forbes A, Dudley, A,McLaren, M. Creation and detection of optical modes with spatial light modulators. Adv Opt Photonics. 2016;8:200–27. [17] Zhang S, et al. Broadband detection of multiple spin and Orbital Angular Momenta via Dielectric Metasurface. Laser Photonics Rev. 2020;14:2000062. [18] Xu C-T, et al. Tunable band-pass optical vortex processor enabled by wash-out-refill chiral superstructures. Appl Phys Lett. 2021;118:151102. [19] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44. [20] Eraslan G, Avsec Ž, Gagneur J, Theis FJ. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet. 2019;20:389–403. [21] Elmarakeby HA, et al. Biologically informed deep neural network for prostate cancer discovery. Nature. 2021;598:348–52. [22] Lu L, Jin P, Pang G, Zhang Z, Karniadakis GE. Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators. Nat Mach Intell. 2021;3:218–29. [23] Joowon L, Ahmed BA, Demetri P. Three-dimensional tomography of red blood cells using deep learning. Adv Photonics. 2020;2:1–9. [24] Zhenbo R, Zhimin X, Edmund YML. End-to-end deep learning framework for digital holographic reconstruction. Adv Photonics. 2019;1:1–12. [25] Genty G, et al. Machine learning and applications in ultrafast photonics. Nat Photonics. 2021;15:91–101. [26] Shijie F, et al. Fringe pattern analysis using deep learning. Adv Photonics. 2019;1:1–7. [27] Giordani T, et al. Machine learning-based classification of Vector Vortex Beams. Phys Rev Lett. 2020;124:160401. [28] Liu Z, Yan S, Liu H, Chen X. Superhigh-Resolution Recognition of Optical Vortex Modes assisted by a deep-learning method. Phys Rev Lett. 2019;123:183902. [29] Wang H, et al. Deep-learning-based recognition of multi-singularity structured light. Nanophotonics. 2022;11:779–86. [30] Feng F, et al. Deep learning-enabled Orbital Angular Momentum-Based information encryption transmission. ACS Photonics. 2022;9:820-9. [31] Wang J, Fu S, Shang Z, Hai L, Gao C. Adjusted EfficientNet for the diagnostic of orbital angular momentum spectrum. Opt Lett. 2022;47:1419–22. [32] Wetzstein G, et al. Inference in artificial intelligence with deep optics and photonics. Nature. 2020;588:39–47. [33] Goi E, Zhang Q, Chen X, Luan H, Gu M. Perspective on photonic memristive neuromorphic computing. PhotoniX. 2020;1:3. [34] Shen Y, et al. Deep learning with coherent nanophotonic circuits. Nat Photonics. 2017;11:441–6. [35] Lin X, et al. All-optical machine learning using diffractive deep neural networks. Science. 2018;361:1004–8. [36] Feldmann J, et al. Parallel convolutional processing using an integrated photonic tensor core. Nature. 2021;589:52–8. [37] Rafayelyan M, Dong J, Tan Y, Krzakala F, Gigan S. Large-scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction. Phys Rev X. 2020;10:041037. [38] Wright LG, et al. Deep physical neural networks trained with backpropagation. Nature. 2022;601:549–55. [39] Ying Z, et al. Optical neural network quantum state tomography. Adv Photonics. 2022;4:1–7. [40] Jingxi L, Deniz M, Yi L, Yair R, Aydogan O. Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Adv Photonics. 2019;1:1–13. [41] Kulce O, Mengu D, Rivenson Y, Ozcan A. All-optical information-processing capacity of diffractive surfaces. Light Sci Appl. 2021;10:25. [42] Zhou T, et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat Photonics. 2021;15:367–73. [43] Luo Y, et al. Computational imaging without a computer: seeing through random diffusers at the speed of light. eLight. 2022;2:4. [44] Veli M, et al. Terahertz pulse shaping using diffractive surfaces. Nat Commun. 2021;12:37. [45] Qian C, et al. Performing optical logic operations by a diffractive neural network. Light Sci Appl. 2020;9:59. [46] Goi E, et al. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light Sci Appl. 2021;10:40. [47] Weng J, et al. Meta-neural-network for real-time and passive deep-learning-based object recognition. Nat Commun. 2020;11:6309. [48] Liu C, et al. A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat Electron. 2022;5:113–22. [49] Chen H, et al. Diffractive Deep Neural Networks at Visible Wavelengths Engineering. 2021;7:1483–91. [50] Mengu D, Luo Y, Rivenson Y, Ozcan A. Analysis of Diffractive Optical neural networks and their integration with electronic neural networks. IEEE J Sel Top Quantum Electron. 2020;26:1–14. [51] Huang C, et al. A silicon photonic–electronic neural network for fibre nonlinearity compensation. Nat Electron. 2021;4:837–44. [52] Wang Z, et al. Recognizing the orbital angular momentum (OAM) of vortex beams from speckle patterns. Sci China Phys Mech Astron. 2022;65:244211. [53] Venkatesh B, Anuradha JA. Review of feature selection and its methods. Cybern Inf Technol. 2019;19:3–26. [54] Shiyao F, et al. Orbital angular momentum comb generation from azimuthal binary phases. Adv Photonics Nexus. 2022;1:016003. [55] Lin Z, et al. Single-shot Kramers-Kronig complex orbital angular momentum spectrum retrieval. 2022;ArXiv.2206.12883. Preprint at https://arxiv.org/abs/2206.12883. [56] Shen Y, et al. Optical vortices 30 years on: OAM manipulation from topological charge to multiple singularities. Light Sci Appl. 2019;8:90. [57] Wang X, et al. Learning to recognize misaligned hyperfine orbital angular momentum modes. Photonics Res. 2021;9:B81–6. [58] Lin J, Yuan XC, Chen M, Dainty JC. Application of orbital angular momentum to simultaneous determination of tilt and lateral displacement of a misaligned laser beam. J Opt Soc Am A. 2010;27:2337–43. [59] Fu S, Gao C. Influences of atmospheric turbulence effects on the orbital angular momentum spectra of vortex beams. Photonics Res. 2016;4:B1–4. [60] Lavery M, Chen Z, Cheng M, Mckee D,Yao A. Sensing with structured beams. 2021;11926. (SPIE). [61] Huff DT, Weisman AJ, Jeraj R. Interpretation and visualization techniques for deep learning models in medical imaging. Phys Med Biol. 2021;66:04TR01. [62] Zeiler MD, Fergus R. Computer vision – ECCV 2014. (Fleet D, Pajdla T, Schiele B, Tuytelaars T, editors) pp. 818–33 (Springer International Publishing, Cham; 2014). [63] Selvaraju RR, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis. 2020;128:336–59. [64] Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H Understanding Neural Networks Through Deep Visualization. 2015;ArXiv.1506.06579. Preprint at https://arxiv.org/abs/1506.06579. [65] Laurens VDM, Hinton GJ. J.o.M.L.R. Visualizing Data using t-SNE. J Mach Learn Res. 2008;9:2579–605. [66] Zhou B, et al. Learning Deep Features for Discriminative Localization. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016. p. 2921–29. https://doi.org/10.1109/CVPR.2016.319. [67] Li J, et al. Deep learning-based quantitative optoacoustic tomography of deep tissues in the absence of labeled experimental data. Optica. 2022;9:32–41. [68] Rahman MSS, Li J, Mengu D, Rivenson Y, Ozcan A. Ensemble learning of diffractive optical networks. Light Sci Appl. 2021;10:14. [69] Sakib Rahman MS, Ozcan A, Computer-Free. All-Optical Reconstruction of Holograms using Diffractive Networks. ACS Photonics. 2021;8:3375–84. [70] Li J, Hung Y-C, Kulce O, Mengu D, Ozcan A. Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network. Light Sci Appl. 2022;11:153. [71] Chen R, et al Physics-aware Complex-valued Adversarial Machine Learning in Reconfigurable Diffractive All-optical Neural Network. 2022;ArXiv.2203.06055. Preprint at https://arxiv.org/abs/2203.06055. [72] Luo X, et al. Metasurface-enabled on-chip multiplexed diffractive neural networks in the visible. Light Sci Appl. 2022;11:158. [73] Georgi P, et al. Optical secret sharing with cascaded metasurface holography. Sci Adv. 2021;7:eabf9718. [74] Faraji-Dana M, et al. Compact folded metasurface spectrometer. Nat Commun. 2018;9:4196. [75] Zhu L, et al. Pancharatnam–Berry phase reversal via opposite-chirality-coexisted superstructures. Light Sci Appl. 2022;11:135. [76] Chen P, Wei B-Y, Hu W, Lu Y-Q. Liquid-crystal-mediated geometric phase: from Transmissive to Broadband Reflective Planar Optics. Adv Mat. 2020;32:1903665. [77] Matsushima K, Shimobaba T, Band-Limited. Angular Spectrum Method for Numerical Simulation of Free-Space Propagation in Far and Near Fields. Opt Express. 2009;17:19662–73. [78] Shi L, Li B, Kim C, Kellnhofer P, Matusik W. Towards real-time photorealistic 3D holography with deep neural networks. Nature. 2021;591:234–9. [79] Zhuang F, et al. A Comprehensive Survey on Transfer Learning. Proc IEEE. 2021;109:43–76. [80] Zhou Y, et al. Sorting photons by Radial Quantum Number. Phys Rev Lett. 2017;119:263602. [81] Wang H, et al. Deep-learning-assisted communication capacity enhancement by non-orthogonal state recognition of structured light. Opt Express. 2022;30:29781–95. [82] Forbes A, de Oliveira M, Dennis MR. Structured light. Nat Photonics. 2021;15:253–62. [83] Wu C, et al. Harnessing optoelectronic noises in a photonic generative network. Sci Adv. 2022;8:eabm2956. [84] Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network. 2015;ArXiv.1503.02531. Preprint at https://arxiv.org/abs/1503.02531. [85] Berthelot D, et al MixMatch: A Holistic Approach to Semi-Supervised Learning. 2019;ArXiv.1905.02249. Preprint at https://arxiv.org/abs/1905.02249. -
下载:
点击查看大图
图(1)
计量
- 文章访问数: 79
- HTML全文浏览量: 0
- PDF下载量: 3
- 被引次数: 0
