Polarization and wavelength routers based on diffractive neural network

Abstract

In the field of information processing, all-optical routers are significant for achieving high-speed, high-capacity signal processing and transmission. In this study, we developed three types of structurally simple and flexible routers using the deep diffractive neural network (D²NN), capable of routing incident light based on wavelength and polarization. First, we implemented a polarization router for routing two orthogonally polarized light beams. The second type is the wavelength router that can route light with wavelengths of 1550, 1300, and 1100 nm, demonstrating outstanding performance with insertion loss as low as 0.013 dB and an extinction ratio of up to 18.96 dB, while also maintaining excellent polarization preservation. The final router is the polarization-wavelength composite router, capable of routing six types of input light formed by pairwise combinations of three wavelengths (1550, 1300, and 1100 nm) and two orthogonal linearly polarized lights, thereby enhancing the information processing capability of the device. These devices feature compact structures, maintaining high contrast while exhibiting low loss and passive characteristics, making them suitable for integration into future optical components. This study introduces new avenues and methodologies to enhance performance and broaden the applications of future optical information processing systems.

Graphical abstract

Keywords

Optical diffractive neural network / All-optical routers / Polarization degree of freedom / Wavelength degree of freedom

References

[1] Gershenfeld, N. , Krikorian, R. , Cohen, D. : The internet of things. Sci. Am. 291 (4), 76- 81 (2004)

[2] Xu, L. , He, W. , Li, S. : Internet of things in industries: a survey. IEEE Trans. Industr. Inform. 10 (4), 2233- 2243 (2014)

[3] Li, S. , Xu, L. , Zhao, S. : The internet of things: a survey. Inf. Syst. Front. 17 (2), 243- 259 (2015)

[4] Paul, A. , Jeyaraj, R. : Internet of things: a primer. Hum. Behav. Emerg. Technol. 1 (1), 37- 47 (2019)

[5] Birje, M.N. , Challagidad, P.S. , Goudar, R.H. , Tapale, M.T. : Cloud computing review: concepts, technology, challenges and security. Int. J. Cloud Comput. 6 (1), 32- 57 (2017)

[6] Dikaiakos, M.D. , Katsaros, D. , Mehra, P. , Pallis, G. , Vakali, A. : Cloud computing: distributed internet computing for IT and scientific research. IEEE Internet Comput. 13 (5), 10- 13 (2009)

[7] Srinivas, J. , Reddy, K.V.S. , Qyser, A.M. : Cloud computing basics. Int. J. Adv. Res. Comput. Commun. Eng. 1, 343- 347 (2012)

[8] Lee, J. : A view of cloud computing. International Journal of Networked and Distributed Computing 1 (1), 2- 8 (2013)

[9] Marston, S. , Li, Z. , Bandyopadhyay, S. , Zhang, J. , Ghalsasi, A. : Cloud computing—The business perspective. Decis. Support. Syst. 51 (1), 176- 189 (2011)

[10] Zhang, Q. , Cheng, L. , Boutaba, R. : Cloud computing: state-of-theart and research challenges. J. Internet Serv. Appl. 1 (1), 7- 18 (2010)

[11] Goodman, J.W. , Leonberger, F.J. , Kung, S.Y. , Athale, R.A. : Optical interconnections for VLSI systems. Proc. IEEE 72 (7), 850- 866 (1984)

[12] Haugen, P.R. , Rychnovsky, S. , Husain, A. , Hutcheson, L.D. : Optical interconnects for high speed computing. Opt. Eng. 25 (10), 1076- 1085 (1986)

[13] Tsang, D.Z. , Goblick, T.J. : Free-space optical interconnection technology in parallel processing systems. Opt. Eng. 33 (5), 1524- 1531 (1994)

[14] Lytel, R. , Davidson, H.L. , Nettleton, N. , Sze, T. : Optical interconnections within modern high-performance computing systems. Proc. IEEE 88 (6), 758- 763 (2000)

[15] Biberman, A. , Bergman, K. : Optical interconnection networks for high-performance computing systems. Rep. Prog. Phys. 75 (4), 046402 (2012)

[16] Liao, K. , Chen, Y. , Yu, Z. , Hu, X. , Wang, X. , Lu, C. , Lin, H. , Du, Q. , Hu, J. , Gong, Q. : All-optical computing based on convolutional neural networks. Opto-Electronic Advances. 4 (11), 200060 (2021)

[17] Liao, K. , Li, C. , Dai, T. , Zhong, C. , Lin, H. , Hu, X. , Gong, Q. : Matrix eigenvalue solver based on reconfigurable photonic neural network. Nanophotonics 11 (17), 4089- 4099 (2022)

[18] Zhong, C. , Liao, K. , Dai, T. , Wei, M. , Ma, H. , Wu, J. , Zhang, Z. , Ye, Y. , Luo, Y. , Chen, Z. , Jian, J. , Sun, C. , Tang, B. , Zhang, P. , Liu, R. , Li, J. , Yang, J. , Li, L. , Liu, K. , Hu, X. , Lin, H. : Graphene/silicon heterojunction for reconfigurable phase-relevant activation function in coherent optical neural networks. Nat. Commun. 14 (1), 6939 (2023)

[19] Yuan, H. , Ma, L. , Yuan, Z. , Feng, S. , Li, J. , Hu, X. , Lu, C. : Onchip cascaded bandpass filter and wavelength router using an intelligent algorithm. IEEE Photonics J. 13 (4), 1- 8 (2021)

[20] Yuan, Z. , Feng, S. , Liu, W. , Liu, Z. , Zhang, Y. , Lu, C. : On-chip ultra-small arbitrary-elliptical-polarization converters. IEEE Photonics J. 13, 1- 8 (2021)

[21] Mansuri, M. , Mir, A. , Farmani, A. : Numerical analysis of tunable nonlinear plasmonic router based on nanoscale ring resonators. Opt. Quantum Electron. 52 (10), 1- 15 (2020)

[22] Zheng, X. , Raz, O. , Calabretta, N. , Zhao, D. , Lu, R. , Liu, Y. : Multiport InP monolithically integrated all-optical wavelength router. Opt. Lett. 41 (16), 3892- 3895 (2016)

[23] Qiu, T.H. , Li, H. , Xie, M. , Liu, Q. , Ma, H.Y. , Xu, R. : Efficient all-optical router and beam splitter for light with orbital angular momentum. Opt. Express 28 (13), 19750- 19759 (2020)

[24] Lin, X. , Rivenson, Y. , Yardimci, N.T. , Veli, M. , Luo, Y. , Jarrahi, M. , Ozcan, A. : All-optical machine learning using diffractive deep neural networks. Science 361 (6406), 1004- 1008 (2018)

[25] Ding, X. , Zhao, Z. , Xie, P. , Cai, D. , Meng, F. , Wang, C. , Wu, Q. , Liu, J. , Burokur, S.N. , Hu, G. : Metasurface-based optical logic operators driven by diffractive neural networks. Adv. Mater. 36 (9), 2308993 (2024)

[26] Zhao, Z. , Wang, Y. , Ding, X. , Li, H. , Fu, J. , Zhang, K. , Burokur, S.N. , Wu, Q. : Compact logic operator utilizing a single-layer metasurface. Photon. Res. 10 (2), 316- 322 (2022)

[27] Zhao, Z. , Wang, Y. , Guan, C. , Zhang, K. , Wu, Q. , Li, H. , Liu, J. , Burokur, S.N. , Ding, X. : Deep learning-enabled compact optical trigonometric operator with metasurface. PhotoniX 3 (1), 15 (2022)

[28] 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. 26, 3700114 (2019)

[29] Qian, C. , Wang, Z. , Qian, H. , Cai, T. , Zheng, B. , Lin, X. , Shen, Y. , Kaminer, I. , Li, E. , Chen, H. : Dynamic recognition and mirage using neuro-metamaterials. Nat. Commun. 13 (1), 2694 (2022)

[30] Yan, T. , Yang, R. , Zheng, Z. , Lin, X. , Xiong, H. , Dai, Q. : Alloptical graph representation learning using integrated diffractive photonic computing units. Sci. Adv. 8 (24), eabn7630 (2022)

[31] Zheng, M. , Shi, L. , Zi, J. : Optimize performance of a diffractive neural network by controlling the Fresnel number. Photon. Res. 10 (11), 2667- 2676 (2022)

[32] Qu, G. , Cai, G. , Sha, X. , Chen, Q. , Cheng, J. , Zhang, Y. , Han, J. , Song, Q. , Xiao, S. : All-dielectric metasurface empowered optical-electronic hybrid neural networks. Laser Photonics Rev. 16 (10), 2100732 (2022)

[33] Bai, B. , Li, Y. , Luo, Y. , Li, X. , Çetintaş, E. , Jarrahi, M. , Ozcan, A. : All-optical image classification through unknown random diffusers using a single-pixel diffractive network. Light Sci. Appl. 12 (1), 69 (2023)

[34] Duan, Z. , Chen, H. , Lin, X. : Optical multi-task learning using multi-wavelength diffractive deep neural networks. Nanophotonics 12 (5), 893- 903 (2023)

[35] Fu, T. , Zang, Y. , Huang, Y. , Du, Z. , Huang, H. , Hu, C. , Chen, M. , Yang, S. , Chen, H. : Photonic machine learning with on-chip diffractive optics. Nat. Commun. 14 (1), 70 (2023)

[36] Zhang, K. , Liao, K. , Cheng, H. , Feng, S. , Hu, X. : Advanced alloptical classification using orbital-angular-momentum-encoded diffractive networks. Advanced Photonics Nexus 2 (6), 66006 (2023)

[37] Li, J. , Mengu, D. , Luo, Y. , Rivenson, Y. , Ozcan, A. : Class-specific differential detection in diffractive optical neural networks improves inference accuracy. Adv. Photonics 1 (4), 46001 (2019)

[38] Yan, T. , Wu, J. , Zhou, T. , Xie, H. , Xu, F. , Fan, J. , Fang, L. , Lin, X. , Dai, Q. : Fourier-space diffractive deep neural network. Phys. Rev. Lett. 123 (2), 023901 (2019)

[39] Mengu, D. , Rivenson, Y. , Ozcan, A. : Scale-, shift-, and rotationinvariant diffractive optical networks. ACS Photonics 8 (1), 324- 334 (2021)

[40] Zhou, T. , Fang, L. , Yan, T. , Wu, J. , Li, Y. , Fan, J. , Wu, H. , Lin, X. , Dai, Q. : In situ optical backpropagation training of diffractive optical neural networks. Photon. Res. 8 (6), 940- 953 (2020)

[41] Kulce, O. , Mengu, D. , Rivenson, Y. , Ozcan, A. : All-optical information-processing capacity of diffractive surfaces. Light Sci. Appl. 10 (1), 25 (2021)

[42] Rahman, M.S.S. , Li, J. , Mengu, D. , Rivenson, Y. , Ozcan, A. : Ensemble learning of diffractive optical networks. Light Sci. Appl. 10 (1), 14 (2021)

[43] Kulce, O. , Mengu, D. , Rivenson, Y. , Ozcan, A. : All-optical synthesis of an arbitrary linear transformation using diffractive surfaces. Light Sci. Appl. 10 (1), 196 (2021)

[44] Liu, C. , Ma, Q. , Luo, Z.J. , Hong, Q.R. , Xiao, Q. , Zhang, H.C. , Miao, L. , Yu, W.M. , Cheng, Q. , Li, L. , Cui, T.J. : A programmable diffractive deep neural network based on a digital-coding metasurface array. Nat. Electron. 5 (2), 113- 122 (2022)

[45] Qian, C. , Lin, X. , Lin, X. , Xu, J. , Sun, Y. , Li, E. , Zhang, B. , Chen, H. : Performing optical logic operations by a diffractive neural network. Light Sci. Appl. 9 (1), 59 (2020)

[46] Luo, Y. , Mengu, D. , Ozcan, A. : Cascadable all-optical NAND gates using diffractive networks. Sci. Rep. 12 (1), 7121 (2022)

[47] Li, Z. , Guo, Y. : Orbital angular momentum logic gates based on optical diffraction neural network. In: International Conference on Optics and Machine Vision (ICOMV 2023) (SPIE). 1 Vol. 12634,. pp. 13- 18 (2023)

[48] Lin, X. , Zhang, K. , Liao, K. , Huang, H. , Fu, Y. , Zhang, X. , Feng, S. , Hu, X. : Polarization-based all-optical logic gates using diffractive neural networks. J. Opt. 26 (3), 035701 (2024)

[49] Veli, M. , Mengu, D. , Yardimci, N.T. , Luo, Y. , Li, J. , Rivenson, Y. , Jarrahi, M. , Ozcan, A. : Terahertz pulse shaping using diffractive surfaces. Nat. Commun. 12 (1), 37 (2021)

[50] Goi, E. , Schoenhardt, S. , Gu, M. : Direct retrieval of Zernike-based pupil functions using integrated diffractive deep neural networks. Nat. Commun. 13 (1), 7531 (2022)

[51] Luo, Y. , Zhao, Y. , Li, J. , Çetintaş, E. , Rivenson, Y. , Jarrahi, M. , Ozcan, A. : Computational imaging without a computer: seeing through random diffusers at the speed of light. eLight 2, 4 (2022)

[52] Li, J. , Mengu, D. , Yardimci, N.T. , Luo, Y. , Li, X. , Veli, M. , Rivenson, Y. , Jarrahi, M. , Ozcan, A. : Spectrally encoded singlepixel machine vision using diffractive networks. Sci. Adv. 7 (13), eabd7690 (2021)

[53] Mengu, D. , Tabassum, A. , Jarrahi, M. , Ozcan, A. : Snapshot multispectral imaging using a diffractive optical network. Light Sci. Appl. 12 (1), 86 (2023)

[54] Arnold, W.H. : Toward 3 nm overlay and critical dimension uniformity: an integrated error budget for double patterning lithography. Optical Microlithography XXI (SPIE) 6924, 50- 58 (2008)

[55] Mulkens, J. , Slachter, B. , Kubis, M. , Tel, W. , Hinnen, P. , Maslow, M. , Dillen, H. , Ma, E. , Chou, K. , Liu, X. : Holistic approach for overlay and edge placement error to meet the 5nm technology node requirements. In: Metrology, Inspection, and Process Control for Microlithography XXXII (SPIE), Vol. 10585,. pp. 375- 388 (2018)

RIGHTS & PERMISSIONS

2024 The Author(s)