Zheng ZH, Zhu SK, Chen Y, Chen HY, Chen JH. Towards integrated mode-division demultiplexing spectrometer by deep learning. Opto-Electron Sci 1, 220012 (2022). doi: 10.29026/oes.2022.220012
Citation: Zheng ZH, Zhu SK, Chen Y, Chen HY, Chen JH. Towards integrated mode-division demultiplexing spectrometer by deep learning. Opto-Electron Sci 1, 220012 (2022). doi: 10.29026/oes.2022.220012

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Towards integrated mode-division demultiplexing spectrometer by deep learning

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  • Miniaturized spectrometers have been widely researched in recent years, but few studies are conducted with on-chip multimode schemes for mode-division multiplexing (MDM) systems. Here we propose an ultracompact mode-division demultiplexing spectrometer that includes branched waveguide structures and graphene-based photodetectors, which realizes simultaneously spectral dispersing and light fields detecting. In the bandwidth of 1500–1600 nm, the designed spectrometer achieves the single-mode spectral resolution of 7 nm for each mode of TE1–TE4 by Tikhonov regularization optimization. Empowered by deep learning algorithms, the 15-nm resolution of parallel reconstruction for TE1–TE4 is achieved by a single-shot measurement. Moreover, by stacking the multimode response in TE1–TE4 to the single spectra, the 3-nm spectral resolution is realized. This design reveals an effective solution for on-chip MDM spectroscopy, and may find applications in multimode sensing, interconnecting and processing.
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  • [1] Ishio H, Minowa J, Nosu K. Review and status of wavelength-division-multiplexing technology and its application. J Lightwave Technol 2, 448–463 (1984). doi: 10.1109/JLT.1984.1073653

    CrossRef Google Scholar

    [2] Xu HN, Dai DX, Shi YC. Silicon Integrated nanophotonic devices for on-chip multi-mode interconnects. Appl Sci 10, 6365 (2020). doi: 10.3390/app10186365

    CrossRef Google Scholar

    [3] Yu Y, Sun CL, Zhang XL. Silicon chip-scale space-division multiplexing: from devices to system. Sci China Inf Sci 61, 080403 (2018). doi: 10.1007/s11432-017-9449-4

    CrossRef Google Scholar

    [4] Khonina SN, Kazanskiy NL, Butt MA, Karpeev SV. Optical multiplexing techniques and their marriage for on-chip and optical fiber communication: a review. Opto-Electron Adv 5, 210127 (2022). doi: 10.29026/oea.2022.210127

    CrossRef Google Scholar

    [5] Jiang WF, Miao JY, Li T. Compact silicon 10-mode multi/demultiplexer for hybrid mode- and polarisation-division multiplexing system. Sci Rep 9, 13223 (2019). doi: 10.1038/s41598-019-49763-0

    CrossRef Google Scholar

    [6] Dai DX, Li CL, Wang SP, Wu H, Shi YC et al. 10-channel mode (de)multiplexer with dual polarizations. Laser Photonics Rev 12, 1700109 (2018). doi: 10.1002/lpor.201700109

    CrossRef Google Scholar

    [7] Huang QD, Jin W, Chiang KS. Broadband mode switch based on a three-dimensional waveguide Mach–Zehnder interferometer. Opt Lett 42, 4877–4880 (2017). doi: 10.1364/OL.42.004877

    CrossRef Google Scholar

    [8] Zheng ZH, Chen Y, Chen HY, Chen JH. Ultra-compact reconfigurable device for mode conversion and dual-mode DPSK demodulation via inverse design. Opt Express 29, 17718–17725 (2021). doi: 10.1364/OE.420874

    CrossRef Google Scholar

    [9] Wang HW, Zhang Y, He Y, Zhu QM, Sun L et al. Compact silicon waveguide mode converter employing dielectric metasurface structure. Adv Opt Mater 7, 1801191 (2018).

    Google Scholar

    [10] Sun CL, Wu WH, Yu Y, Chen GY, Zhang XL et al. De-multiplexing free on-chip low-loss multimode switch enabling reconfigurable inter-mode and inter-path routing. Nanophotonics 7, 1571–1580 (2018). doi: 10.1515/nanoph-2018-0053

    CrossRef Google Scholar

    [11] Ashry I, Mao Y, Trichili A, Wang BW, Ng TK et al. A review of using few-mode fibers for optical sensing. IEEE Access 8, 179592–179605 (2020). doi: 10.1109/ACCESS.2020.3027965

    CrossRef Google Scholar

    [12] Su YK, He Y, Chen HS, Li XY, Li GF. Perspective on mode-division multiplexing. Appl Phys Lett 118, 200502 (2021). doi: 10.1063/5.0046071

    CrossRef Google Scholar

    [13] Yang ZY, Albrow-Owen T, Cai WW, Hasan T. Miniaturization of optical spectrometers. Science 371, eabe0722 (2021). doi: 10.1126/science.abe0722

    CrossRef Google Scholar

    [14] Micó G, Gargallo B, Pastor D, Muñoz P. Integrated optic sensing spectrometer: concept and design. Sensors 19, 1018 (2019). doi: 10.3390/s19051018

    CrossRef Google Scholar

    [15] Subramanian AZ, Ryckeboer E, Dhakal A, Peyskens F, Malik A et al. Silicon and silicon nitride photonic circuits for spectroscopic sensing on-a-chip [Invited]. Photonics Res 3, B47–B59 (2015). doi: 10.1364/PRJ.3.000B47

    CrossRef Google Scholar

    [16] Xiong J, Cai XS, Cui KY, Huang YD, Yang JW et al. Dynamic brain spectrum acquired by a real-time ultraspectral imaging chip with reconfigurable metasurfaces. Optica 9, 461–468 (2022). doi: 10.1364/OPTICA.440013

    CrossRef Google Scholar

    [17] Zhang WY, Song HY, He X, Huang LQ, Zhang XY et al. Deeply learned broadband encoding stochastic hyperspectral imaging. Light Sci Appl 10, 108 (2021). doi: 10.1038/s41377-021-00545-2

    CrossRef Google Scholar

    [18] Wan NH, Meng F, Schröder T, Shiue RJ, Chen EH et al. High-resolution optical spectroscopy using multimode interference in a compact tapered fibre. Nat Commun 6, 7762 (2015). doi: 10.1038/ncomms8762

    CrossRef Google Scholar

    [19] Tian Y, Li JH, Wu ZY, Chen YX, Zhu PK et al. Wavelength-interleaved MDM-WDM transmission over weakly-coupled FMF. Opt Express 25, 16603–16617 (2017). doi: 10.1364/OE.25.016603

    CrossRef Google Scholar

    [20] Doerr CR, Zhang LM, Winzer PJ. Monolithic InP multiwavelength coherent receiver using a chirped arrayed waveguide grating. J Lightwave Technol 29, 536–541 (2011). doi: 10.1109/JLT.2010.2097240

    CrossRef Google Scholar

    [21] Yang ZY, Albrow-Owen T, Cui HX, Alexander-Webber J, Gu FX et al. Single-nanowire spectrometers. Science 365, 1017–1020 (2019). doi: 10.1126/science.aax8814

    CrossRef Google Scholar

    [22] Yuan SF, Naveh D, Watanabe K, Taniguchi T, Xia FN. A wavelength-scale black phosphorus spectrometer. Nat Photonics 15, 601–607 (2021). doi: 10.1038/s41566-021-00787-x

    CrossRef Google Scholar

    [23] Li A, Fainman Y. On-chip spectrometers using stratified waveguide filters. Nat Commun 12, 2704 (2021). doi: 10.1038/s41467-021-23001-6

    CrossRef Google Scholar

    [24] Ma W, Liu ZC, Kudyshev ZA, Boltasseva A, Cai WS et al. Deep learning for the design of photonic structures. Nat Photonics 15, 77–90 (2021). doi: 10.1038/s41566-020-0685-y

    CrossRef Google Scholar

    [25] Wetzstein G, Ozcan A, Gigan S, Fan SH, Englund D et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 39–47 (2020). doi: 10.1038/s41586-020-2973-6

    CrossRef Google Scholar

    [26] Zhou JJ, Huang BL, Yan Z, Bünzli JCG. Emerging role of machine learning in light-matter interaction. Light Sci Appl 8, 84 (2019). doi: 10.1038/s41377-019-0192-4

    CrossRef Google Scholar

    [27] Yang JW, Cui KY, Cai XS, Xiong J, Zhu HB et al. Ultraspectral imaging based on metasurfaces with freeform shaped meta‐atoms. Laser Photonics Rev 16, 2100663 (2022). doi: 10.1002/lpor.202100663

    CrossRef Google Scholar

    [28] Ma W, Cheng F, Xu YH, Wen QL, Liu YM. Probabilistic representation and inverse design of metamaterials based on a deep generative model with semi‐supervised learning strategy. Adv Mater 31, 1901111 (2019). doi: 10.1002/adma.201901111

    CrossRef Google Scholar

    [29] Ma W, Xu YH, Xiong B, Deng L, Peng RW et al. Pushing the limits of functionality‐multiplexing capability in metasurface design based on statistical machine learning. Adv Mater 34, 2110022 (2022). doi: 10.1002/adma.202110022

    CrossRef Google Scholar

    [30] Krasikov S, Tranter A, Bogdanov A, Kivshar Y. Intelligent metaphotonics empowered by machine learning. Opto-Electron Adv 5, 210147 (2022). doi: 10.29026/oea.2022.210147

    CrossRef Google Scholar

    [31] Cerjan B, Halas NJ. Toward a nanophotonic nose: a compressive sensing-enhanced, optoelectronic mid-Infrared spectrometer. ACS Photonics 6, 79–86 (2019).

    Google Scholar

    [32] Wang Z, Yu ZF. Spectral analysis based on compressive sensing in nanophotonic structures. Opt Express 22, 25608–25614 (2014). doi: 10.1364/OE.22.025608

    CrossRef Google Scholar

    [33] Kwak Y, Park SM, Ku Z, Urbas A, Kim YL. A pearl spectrometer. Nano Lett 21, 921–930 (2021). doi: 10.1021/acs.nanolett.0c03618

    CrossRef Google Scholar

    [34] Zhang JH, Cheng ZW, Dong JJ, Zhang XL. Cascaded nanobeam spectrometer with high resolution and scalability. Optica 9, 517–521 (2022). doi: 10.1364/OPTICA.453483

    CrossRef Google Scholar

    [35] Bao J, Bawendi MG. A colloidal quantum dot spectrometer. Nature 523, 67–70 (2015). doi: 10.1038/nature14576

    CrossRef Google Scholar

    [36] Chang CC, Lee HN. On the estimation of target spectrum for filter-array based spectrometers. Opt Express 16, 1056–1061 (2008). doi: 10.1364/OE.16.001056

    CrossRef Google Scholar

    [37] Cheng ZW, Zhao YH, Zhang JH, Zhou HL, Gao DS et al. Generalized modular spectrometers combining a compact nanobeam microcavity and computational reconstruction. ACS Photonics 9, 74–81 (2022).

    Google Scholar

    [38] Zheng BJ, Li LF, Wang JZ, Zhuge MH, Su X et al. On‐chip measurement of photoluminescence with high sensitivity monolithic spectrometer. Adv Opt Mater 8, 2000191 (2020). doi: 10.1002/adom.202000191

    CrossRef Google Scholar

    [39] Liu CY, Guo JS, Yu LW, Li J, Zhang M et al. Silicon/2D-material photodetectors: from near-infrared to mid-infrared. Light Sci Appl 10, 123 (2021). doi: 10.1038/s41377-021-00551-4

    CrossRef Google Scholar

    [40] Xia FN, Mueller T, Lin YM, Valdes-Garcia A, Avouris P. Ultrafast graphene photodetector. Nat Nanotechnol 4, 839–843 (2009). doi: 10.1038/nnano.2009.292

    CrossRef Google Scholar

    [41] Romagnoli M, Sorianello V, Midrio M, Koppens FHL, Huyghebaert C et al. Graphene-based integrated photonics for next-generation datacom and telecom. Nat Rev Mater 3, 392–414 (2018). doi: 10.1038/s41578-018-0040-9

    CrossRef Google Scholar

    [42] Yan SQ, Zuo Y, Xiao SS, Oxenløwe LK, Ding YH. Graphene photodetector employing double slot structure with enhanced responsivity and large bandwidth. Opto-Electron Adv 5, 210159 (2022). doi: 10.29026/oea.2022.210159

    CrossRef Google Scholar

    [43] Gan XT, Shiue RJ, Gao YD, Meric I, Heinz TF et al. Chip-integrated ultrafast graphene photodetector with high responsivity. Nat Photonics 7, 883–887 (2013). doi: 10.1038/nphoton.2013.253

    CrossRef Google Scholar

    [44] Shiue RJ, Gao YD, Wang YF, Peng C, Robertson AD et al. High-responsivity graphene–boron nitride photodetector and autocorrelator in a silicon photonic integrated circuit. Nano Lett 15, 7288–7293 (2015). doi: 10.1021/acs.nanolett.5b02368

    CrossRef Google Scholar

    [45] Buscema M, Island JO, Groenendijk DJ, Blanter SI, Steele GA et al. Photocurrent generation with two-dimensional van der Waals semiconductors. Chem Soc Rev 44, 3691–3718 (2015). doi: 10.1039/C5CS00106D

    CrossRef Google Scholar

    [46] Konstantatos G, Badioli M, Gaudreau L, Osmond J, Bernechea M et al. Hybrid graphene–quantum dot phototransistors with ultrahigh gain. Nat Nanotechnol 7, 363–368 (2012). doi: 10.1038/nnano.2012.60

    CrossRef Google Scholar

    [47] Li H, Anugrah Y, Koester SJ, Li M. Optical absorption in graphene integrated on silicon waveguides. Appl Phys Lett 101, 111110 (2012). doi: 10.1063/1.4752435

    CrossRef Google Scholar

    [48] Edelman A. Eigenvalues and condition numbers of random matrices. SIAM J Matrix Anal Appl 9, 543–560 (1988). doi: 10.1137/0609045

    CrossRef Google Scholar

    [49] Redding B, Liew SF, Sarma R, Cao H. Compact spectrometer based on a disordered photonic chip. Nat Photonics 7, 746–751 (2013). doi: 10.1038/nphoton.2013.190

    CrossRef Google Scholar

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