Orbital angular momentum mode logical operation using optical diffractive neural network

Peipei Wang; Wenjie Xiong; Zebin Huang; Yanliang He; Zhiqiang Xie; Junmin Liu; Huapeng Ye; Ying Li; Dianyuan Fan; Shuqing Chen

doi:10.1364/PRJ.432919

[1] J. Touch, A.-H. Badawy, V. J. Sorger. Optical computing. Nanophotonics, 6, 503-505(2017).

[2] A. A. Sawchuk, T. C. Strand. Digital optical computing. Proc. IEEE, 72, 758-779(1984).

[3] H. J. Caulfield, S. Dolev. Why future supercomputing requires optics. Nat. Photonics, 4, 261-263(2010).

[4] N. D. Lane, S. Bhattacharya, A. Mathur, P. Georgiev, C. Forlivesi, F. Kawsar. Squeezing deep learning into mobile and embedded devices. IEEE Pervasive Comput., 16, 82-88(2017).

[5] Z. Gu, Y. Gao, X. Liu. Optronic convolutional neural networks of multi-layers with different functions executed in optics for image classification. Opt. Express, 29, 5877-5889(2021).

[6] D. R. Solli, B. Jalali. Analog optical computing. Nat. Photonics, 9, 704-706(2015).

[7] D. Xu, S. He, J. Zhou, S. Chen, S. Wen, H. Luo. Optical analog computing of two-dimensional spatial differentiation based on the Brewster effect. Opt. Lett., 45, 6867-6870(2020).

[8] H. Rajabalipanah, A. Abdolali, S. Iqbal, L. Zhang, T. J. Cui. Analog signal processing through space-time digital metasurfaces. Nanophotonics, 10, 1753-1764(2021).

[9] D. Woods, T. J. Naughton. Optical computing. Appl. Math. Comput., 215, 1417-1430(2009).

[10] L. Allen, M. W. Beijersbergen, R. J. C. Spreeuw, J. P. Woerdman. Orbital angular momentum of light and the transformation of Laguerre-Gaussian laser modes. Phys. Rev. A, 45, 8185-8189(1992).

[11] L. Allen, S. M. Barnett, M. J. Padgett. Optical Angular Momentum(2016).

[12] N. Bozinovic, Y. Yue, Y. Ren, M. Tur, P. Kristensen, H. Huang, A. E. Willner, S. Ramachandran. Terabit-scale orbital angular momentum mode division multiplexing in fibers. Science, 340, 1545-1548(2013).

[13] J. Wang, J. Y. Yang, I. M. Fazal, N. Ahmed, Y. Yan, H. Huang, Y. Ren, Y. Yue, S. Dolinar, M. Tur. Terabit free-space data transmission employing orbital angular momentum multiplexing. Nat. Photonics, 6, 488-496(2012).

[14] Y. He, P. Wang, C. Wang, J. Liu, H. Ye, X. Zhou, Y. Li, S. Chen, X. Zhang, D. Fan. All-optical signal processing in structured light multiplexing with dielectric meta-optics. ACS Photon., 7, 135-146(2020).

[15] J. Liu, P. Wang, X. Zhang, Y. He, X. Zhou, H. Ye, Y. Li, S. Xu, S. Chen, D. Fan. Deep learning based atmospheric turbulence compensation for orbital angular momentum beam distortion and communication. Opt. Express, 27, 16671-16688(2019).

[16] D. G. Grier. A revolution in optical manipulation. Nature, 424, 810-816(2003).

[17] A. E. Willner, L. Li, G. Xie, Y. Ren, H. Huang, Y. Yue, N. Ahmed, M. J. Willner, A. J. Willner, Y. Yan, Z. Zhao, Z. Wang, C. Liu, M. Tur, S. Ashrafi. Orbital-angular-momentum-based reconfigurable optical switching and routing. Photon. Res., 4, B5-B8(2016).

[18] Z. Xie, S. Gao, T. Lei, S. Feng, Y. Zhang, F. Li, J. Zhang, Z. Li, X. Yuan. Integrated (de)multiplexer for orbital angular momentum fiber communication. Photon. Res., 6, 743-749(2018).

[19] W. Xiong, P. Wang, M. Cheng, J. Liu, Y. He, X. Zhou, J. Xiao, Y. Li, S. Chen, D. Fan. Convolutional neural network based atmospheric turbulence compensation for optical orbital angular momentum multiplexing. J. Lightwave Technol., 38, 1712-1721(2020).

[20] X. Fang, H. Ren, M. Gu. Orbital angular momentum holography for high-security encryption. Nat. Photonics, 14, 102-108(2020).

[21] Q. Xiao, Q. Ma, T. Yan, L. Wu, C. Liu, Z. Wang, X. Wan, Q. Cheng, T. Cui. Orbital‐angular‐momentum‐encrypted holography based on coding information metasurface. Adv. Opt. Mater., 9, 2002155(2021).

[22] S. Fu, T. Wang, C. Gao. Perfect optical vortex array with controllable diffraction order and topological charge. J. Opt. Soc. Am. A, 33, 1836-1842(2016).

[23] C. Huang, C. Zhang, S. Xiao, Y. Wang, Y. Fan, Y. Liu, N. Zhang, G. Qu, H. Ji, J. Han, L. Ge, Y. Kivshar, Q. Song. Ultrafast control of vortex microlasers. Science, 367, 1018-1021(2020).

[24] S. Fu, C. Gao. Influences of atmospheric turbulence effects on the orbital angular momentum spectra of vortex beams. Photon. Res., 4, B1-B4(2016).

[25] H. Wei, Z. Wang, X. Tian, M. Käll, H. Xu. Cascaded logic gates in nanophotonic plasmon networks. Nat. Commun., 2, 1-5(2011).

[26] Y. Sang, X. Wu, S. S. Raja, C. Y. Wang, H. Li, Y. Ding, D. Liu, J. Zhou, H. Ahn, S. Gwo. Broadband multifunctional plasmonic logic gates. Adv. Opt. Mater., 6, 1701368(2018).

[27] H. Liu, Z. Quan, Y. Cheng, S. Deng, L. Yuan. Ultra-compact universal linear-optical logic gate based on single rectangle plasmonic slot nanoantenna. Plasmonics, 16, 1-8(2021).

[28] A. Pal, M. Z. Ahmed, S. Swarnakar. An optimized design of all-optical XOR, OR, and NOT gates using plasmonic waveguide. Opt. Quantum Electron., 53, 84(2021).

[29] Q. Xu, M. Lipson. All-optical logic based on silicon micro-ring resonators. Opt. Express, 15, 924-929(2007).

[30] M. W. McCutcheon, G. W. Rieger, J. F. Young, D. Dalacu, P. J. Poole, R. L. Williams. All-optical conditional logic with a nonlinear photonic crystal nanocavity. Appl. Phys. Lett., 95, 221102(2009).

[31] Y. Fu, X. Hu, C. Lu, S. Yue, H. Yang, Q. Gong. All-optical logic gates based on nanoscale plasmonic slot waveguides. Nano Lett., 12, 5784-5790(2012).

[32] N. K. Fontaine, R. Ryf, H. Chen, D. T. Neilson, K. Kim, J. Carpenter. Laguerre-Gaussian mode sorter. Nat. Commun., 10, 1865(2019).

[33] G. Labroille, B. Denolle, P. Jian, P. Genevaux, N. Treps, J.-F. Morizur. Efficient and mode selective spatial mode multiplexer based on multi-plane light conversion. Opt. Express, 22, 15599-15607(2014).

[34] Y. Sakamaki, T. Saida, T. Hashimoto, H. Takahashi. New optical waveguide design based on wavefront matching method. J. Lightwave Technol., 25, 3511-3518(2007).

[35] F. Brandt, M. Hiekkamäki, F. Bouchard, M. Huber, R. Fickler. High-dimensional quantum gates using full-field spatial modes of photons. Optica, 7, 98-107(2020).

[36] X. Lin, Y. Rivenson, N. T. Yardimci, M. Veli, Y. Luo, M. Jarrahi, A. Ozcan. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).

[37] T. Yan, J. Wu, T. Zhou, H. Xie, F. Xu, J. Fan, L. Fang, X. Lin, Q. Dai. Fourier-space diffractive deep neural network. Phys. Rev. Lett., 123, 023901(2019).

[38] T. Zhou, X. Lin, J. Wu, Y. Chen, H. Xie, Y. Li, J. Fan, H. Wu, L. Fang, Q. Dai. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photonics, 15, 367-373(2021).

[39] M. Veli, D. Mengu, N. T. Yardimci, Y. Luo, J. Li, Y. Rivenson, M. Jarrahi, A. Ozcan. Terahertz pulse shaping using diffractive surfaces. Nat. Commun., 12, 37(2021).

[40] J. Shi, D. Wei, C. Hu, M. Chen, K. Liu, J. Luo, X. Zhang. Robust light beam diffractive shaping based on a kind of compact all-optical neural network. Opt. Express, 29, 7084-7099(2021).

[41] Z. Huang, P. Wang, J. Liu, W. Xiong, Y. He, J. Xiao, H. Ye, Y. Li, S. Chen, D. Fan. All-optical signal processing of vortex beams with diffractive deep neural networks. Phys. Rev. Appl., 15, 014037(2021).

[42] A. M. Yao, M. J. Padgett. Orbital angular momentum: origins, behavior and applications. Adv. Opt. Photon., 3, 161-204(2011).

[43] L. Jing, X. Lin, Z. Wang, I. Kaminer, H. Hu, E. Li, Y. Liu, M. Chen, B. Zhang, H. Chen. Polarization shaping of free‐electron radiation by gradient bianisotropic metasurfaces. Laser Photon. Rev., 15, 2000426(2021).

[44] H. Lu, B. Zheng, T. Cai, C. Qian, Y. Yang, Z. Wang, H. Chen. Frequency‐controlled focusing using achromatic metasurface. Adv. Opt. Mater., 9, 2001311(2021).

[45] P. Xu, H. Tian, W. Jiang, Z. Chen, T. Cao, C. Qiu, T. Cui. Phase and polarization modulations using radiation‐type metasurfaces. Adv. Opt. Mater., 9, 2100159(2021).

[46] Y. He, Z. Xie, B. Yang, X. Chen, J. Liu, H. Ye, X. Zhou, Y. Li, S. Chen, D. Fan. Controllable photonic spin Hall effect with phase function construction. Photon. Res., 8, 963-971(2020).

[47] Z. Ji, W. Liu, S. Krylyuk, X. Fan, Z. Zhang, A. Pan, L. Feng, A. Davydov, R. Agarwal. Photocurrent detection of the orbital angular momentum of light. Science, 368, 763-767(2020).

[48] E. Goi, X. Chen, Q. Zhang, B. P. Cumming, S. Schoenhardt, H. Luan, M. Gu. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light Sci. Appl., 10, 40(2021).

[49] H. Chen, J. Feng, M. Jiang, Y. Wang, J. Lin, J. Tan, P. Jin. Diffractive deep neural networks at visible wavelengths. Engineering(2021).

[50] M. Soskin, V. Gorshkov, M. Vasnetsov, J. Malos, N. Heckenberg. Topological charge and angular momentum of light beams carrying optical vortices. Phys. Rev. A, 56, 4064-4075(1997).

[51] L. Chen, W. Zhang, Q. Lu, X. Lin. Making and identifying optical superpositions of high orbital angular momenta. Phys. Rev. A, 88, 053831(2013).

[52] H. Sztul, R. Alfano. Double-slit interference with Laguerre-Gaussian beams. Opt. Lett., 31, 999-1001(2006).

[53] J. Hickmann, E. Fonseca, W. Soares, S. Chávez-Cerda. Unveiling a truncated optical lattice associated with a triangular aperture using light’s orbital angular momentum. Phys. Rev. Lett., 105, 053904(2010).

[54] Y. He, J. Liu, P. Wang, W. Xiong, Y. Wu, X. Zhou, Y. Cheng, Y. Gao, Y. Li, S. Chen. Detecting orbital angular momentum modes of vortex beams using feed-forward neural network. J. Lightwave Technol., 37, 5848-5855(2019).

[55] Z. Huang, P. Wang, J. Liu, W. Xiong, Y. He, X. Zhou, J. Xiao, Y. Li, S. Chen, D. Fan. Identification of hybrid orbital angular momentum modes with deep feedforward neural network. Results Phys., 15, 102790(2019).

[56] Z. Liu, S. Yan, H. Liu, X. Chen. Superhigh-resolution recognition of optical vortex modes assisted by a deep-learning method. Phys. Rev. Lett., 123, 183902(2019).

[57] L. C. Andrews, R. L. Phillips. Laser Beam Propagation through Random Media(2005).

[58] Y. LeCun, Y. Bengio, G. Hinton. Deep learning. Nature, 521, 436-444(2015).

[59] J. Schmidhuber. Deep learning in neural networks: an overview. Neural Netw., 61, 85-117(2015).

[60] H. Ren, W. Shao, Y. Li, F. Salim, M. Gu. Three-dimensional vectorial holography based on machine learning inverse design. Sci. Adv., 6, eaaz4261(2020).

[61] R. Zhu, T. Qiu, J. Wang, S. Sui, C. Hao, T. Liu, Y. Li, M. Feng, A. Zhang, C. Qiu. Phase-to-pattern inverse design paradigm for fast realization of functional metasurfaces via transfer learning. Nat. Commun., 12, 1(2021).

[62] Q. Zhang, H. Yu, M. Barbiero, B. Wang, M. Gu. Artificial neural networks enabled by nanophotonics. Light Sci. Appl., 8, 1(2019).