Machine learning enables design of on-chip integrated silicon T-junctions with footprint of 1.2 μm×1.2μm

Nano Communication Networks - Tập 25 - Trang 100312 - 2020
Sourangsu Banerji1, Apratim Majumder1, Alexander Hamrick2, Rajesh Menon1, Berardi Sensale-Rodriguez1
1Department of Electrical and Computer Engineering, The University of Utah, Salt Lake City, UT 84112, USA
2School of Computing, The University of Utah, Salt Lake City, UT 84112, USA

Tài liệu tham khảo

Reed, 2004 Chrostowski, 2015 Jia, 2017, Optical switch compatible with wavelength division multiplexing and mode division multiplexing for photonic networks-on-chip, Opt. Express, 25, 20698, 10.1364/OE.25.020698 Ren, 2017, Three-mode mode division-multiplexing passive optical network over 12-km low mode-crosstalk FMF using all-fiber mode MUX/DEMUX, Opt. Commun., 383, 525, 10.1016/j.optcom.2016.09.051 Cerutti, 2017, Engineering of closely packed silicon-on-isolator waveguide arrays for mode division multiplexing applications, J. Opt. Soc. Amer. B, 34, 497, 10.1364/JOSAB.34.000497 2006 Ashenden, 2008 Xu, 2005, Micrometre-scale silicon electro-optic modulator, Nature, 435, 325, 10.1038/nature03569 Luo, 2011, High quality factor etchless silicon photonic ring resonators, Opt. Express, 19, 6284, 10.1364/OE.19.006284 Miller, 2020, Large-scale optical phased array using a low-power multi-pass silicon photonic platform, Optica, 7, 3, 10.1364/OPTICA.7.000003 Yu, 2020, Raman Lasing and soliton mode-locking in lithium niobate microresonators, Light: Sci. Appl., 9, 1, 10.1038/s41377-019-0231-1 Leuthold, 2010, Nonlinear silicon photonics, Nat. Photonics, 4, 535, 10.1038/nphoton.2010.185 Bor, 2016, Differential evolution algorithm based photonic structure design: Numerical and experimental verification of subwavelength λ/5 focusing of light, Sci. Rep., 6, 30871, 10.1038/srep30871 Lu, 2013, Nanophotonic computational design, Opt. Express, 21, 13351, 10.1364/OE.21.013351 Piggott, 2014, Inverse design and implementation of a wavelength demultiplexing grating coupler, Sci. Rep., 4, 1, 10.1038/srep07210 Piggott, 2015, Inverse design and demonstration of a compact and broadband on-chip wavelength demultiplexer, Nat. Photon., 9, 374, 10.1038/nphoton.2015.69 Su, 2017, Inverse design and demonstration of a compact on-chip narrowband three-channel wavelength demultiplexer, ACS Photon., 5, 301, 10.1021/acsphotonics.7b00987 Borel, 2004, Topology optimization and fabrication of photonic crystal structures, Opt. Express, 12, 1996, 10.1364/OPEX.12.001996 Shen, 2014, Integrated metamaterials for efficient and compact free-space-to-waveguide coupling, Opt. Express, 22, 27175, 10.1364/OE.22.027175 Shen, 2015, Integrated digital metamaterials enables ultra-compact optical diodes, Opt. Express, 23, 10847, 10.1364/OE.23.010847 Shen, 2015, An integrated-nanophotonics polarization beamsplitter with 2.4×2.4μm2 footprint, Nat. Photonics, 9, 378, 10.1038/nphoton.2015.80 Majumder, 2019, Programmable metamaterials & metasurfaces for ultra-compact multi-functional photonics Majumder, 2017, Ultra-compact polarization rotation in integrated silicon photonics using digital metamaterials, Opt. Express, 25, 19721, 10.1364/OE.25.019721 Majumder, 2017 Shen, 2015, Integrated digital metamaterials enables ultra-compact optical diodes, Opt. Express, 23, 10847, 10.1364/OE.23.010847 Jia, 2018, Inverse-design and demonstration of ultracompact silicon meta-structure mode exchange device, ACS Photonics, 5, 1833, 10.1021/acsphotonics.8b00013 Piggott, 2017, Fabrication-constrained nanophotonic inverse design, Sci. Rep., 7, 1, 10.1038/s41598-017-01939-2 Callewaert, 2016, Inverse design of an ultra-compact broadband optical diode based on asymmetric spatial mode conversion, Sci. Rep., 6, 1, 10.1038/srep32577 L. Su, R. Trivedi, N.V. Sapra, A.Y. Piggott, D. Vercruysse, J. Vučković, Fully automated optimization of grating couplers, Opt. Express 26, 4023–4034. Lalau-Keraly, 2013, Adjoint shape optimization applied to electromagnetic design, Opt. Express, 21, 21693, 10.1364/OE.21.021693 Wang, 2018, Adjoint-based optimization of active nanophotonic devices, Opt. Express, 26, 3236, 10.1364/OE.26.003236 Meem, 2019, Broadband lightweight flat lenses for long-wave infrared imaging, Proc. Nat. Acad. Sci., 116, 21375, 10.1073/pnas.1908447116 Biswas, 2013, Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data, Phys. Rev. D, 88, 10.1103/PhysRevD.88.062003 Kalinin, 2015, Big-deep smart data in imaging for guiding materials design, Nat. Mater., 14, 973, 10.1038/nmat4395 Carrasquilla, 2017, Machine learning phases of matter, Nat. Phys., 13, 431, 10.1038/nphys4035 Wang, 2016, Discovering phase transitions with unsupervised learning, Phys. Rev. B, 94, 10.1103/PhysRevB.94.195105 Deng, 2017, Machine learning topological states, Phys. Rev. B, 96, 10.1103/PhysRevB.96.195145 Turduev, 2018, Ultracompact photonic structure design for strong light confinement and coupling into nanowaveguide, J. Lightwave Technol., 36, 2812, 10.1109/JLT.2018.2821361 Yao, 2019, Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale, Nanophotonics, 8, 339, 10.1515/nanoph-2018-0183 Silver, 2017, Mastering the game of go without human knowledge, Nature, 550, 354, 10.1038/nature24270 Sutton, 2018 Kaelbling, 1996, Reinforcement learning: A survey, J. Artif. Intell. Res., 4, 237, 10.1613/jair.301 D.Silver T. Hubert, 2018, A general reinforcement learning algorithm that masters chess, shogi, and go through self-play, Science, 362, 1140, 10.1126/science.aar6404 Liu, 2018, Training deep neural networks for the inverse design of nanophotonic structures, ACS Photon., 5, 1365, 10.1021/acsphotonics.7b01377 Kiarashinejad, 2019, Knowledge discovery in nanophotonics using geometric deep learning, Adv. Intell. Syst. So, 2019, Designing nanophotonic structures using conditional deep convolutional generative adversarial networks, Nanophotonics, 8, 1255, 10.1515/nanoph-2019-0117 Chugh, 2019, Machine learning regression approach to the nanophotonic waveguide analyses, J. Lightwave Technol., 37, 6080, 10.1109/JLT.2019.2946572 So, 2020, Deep learning enabled inverse design in nanophotonics, Nanophotonics, 10.1515/nanoph-2019-0474 Tahersima, 2019, Deep neural network inverse design of integrated photonic power splitters, Sci. Rep., 9, 1, 10.1038/s41598-018-37952-2 Cai, 2008, Application of optical proximity correction technology, Sci. China Ser. F-Inf. Sci., 51, 213, 10.1007/s11432-008-0006-4 Chang, 2018, Ultracompact dual-mode waveguide crossing based on subwavelength multimode-interference couplers, Photon. Res., 6, 660, 10.1364/PRJ.6.000660 Zhang, 2013, A compact and low loss Y-junction for submicron silicon waveguide, Opt. Express, 21, 1310, 10.1364/OE.21.001310 Kurt, 2011, Design of T-shaped nanophotonic wire waveguide for optical interconnection in H-tree network, Opt. Express, 19, 26827, 10.1364/OE.19.026827 Alpkılıç, 2019, Parametric study of multi-outputs T-junction spatial mode demultiplexers design with an objective-first algorithm Xu, 2017, Integrated photonic power divider with arbitrary power ratios, Optim. Lett., 42, 855, 10.1364/OL.42.000855 Lin, 2019, Broadband, low-loss silicon photonic y-junction with an arbitrary power splitting ratio, Opt. Express, 27, 14338, 10.1364/OE.27.014338 Ren, 2019 Xie, 2020, An ultra-compact 3-dB power splitter for three modes based on pixelated meta-structure, IEEE Photon. Technol. Lett., 32, 341, 10.1109/LPT.2020.2975128 Chang, 2018, Inverse design and demonstration of an ultracompact broadband dual-mode 3 dB power splitter, Opt. Express, 26, 24135, 10.1364/OE.26.024135 Latifoğlu, 2017 https://www.lumerical.com/learn/whitepapers/lumericals-2-5d-fdtd-propagation-method/. M. Smit, 2012, Moore’s law in photonics, Laser Photonics Rev., 6, 1, 10.1002/lpor.201100001 Punch, 2012, Thermal challenges in photonic integrated circuits, 1 Hudgings, 2003, Thermal profiling for optical characterization of waveguide devices, Appl. Phys. Lett., 83, 3882, 10.1063/1.1625790 Gilardi, 2014, Deep trenches for thermal crosstalk reduction in InP-based photonic integrated circuits, J. Lightwave Technol., 32, 4864, 10.1109/JLT.2014.2366781