Programmable phase-change metasurfaces on waveguides for multimode photonic convolutional neural network.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
04 Jan 2021
04 Jan 2021
Historique:
received:
16
04
2020
accepted:
25
11
2020
entrez:
5
1
2021
pubmed:
6
1
2021
medline:
6
1
2021
Statut:
epublish
Résumé
Neuromorphic photonics has recently emerged as a promising hardware accelerator, with significant potential speed and energy advantages over digital electronics for machine learning algorithms, such as neural networks of various types. Integrated photonic networks are particularly powerful in performing analog computing of matrix-vector multiplication (MVM) as they afford unparalleled speed and bandwidth density for data transmission. Incorporating nonvolatile phase-change materials in integrated photonic devices enables indispensable programming and in-memory computing capabilities for on-chip optical computing. Here, we demonstrate a multimode photonic computing core consisting of an array of programable mode converters based on on-waveguide metasurfaces made of phase-change materials. The programmable converters utilize the refractive index change of the phase-change material Ge
Identifiants
pubmed: 33398011
doi: 10.1038/s41467-020-20365-z
pii: 10.1038/s41467-020-20365-z
pmc: PMC7782756
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
96Subventions
Organisme : United States Department of Defense | United States Navy | Office of Naval Research (ONR)
ID : N00014-17-1-2661
Références
Marr, B., Degnan, B., Hasler, P. & Anderson, D. Scaling Energy Per Operation via an Asynchronous Pipeline. IEEE Transactions on Very Large Scale. Integration (VLSI) Systems. 21, 147–151 (IEEE, 2013).
Jones, N. How to stop data centres from gobbling up the world’s electricity. Nature 561, 163–166 (2018).
pubmed: 30209383
doi: 10.1038/d41586-018-06610-y
Athale, R. & Psaltis, D. Optical computing: past and future. Opt. Photon. News 27, 32–39 (2016).
doi: 10.1364/OPN.27.6.000032
Solli, D. R. & Jalali, B. Analog optical computing. Nat. Photon. 9, 704–706 (2015).
doi: 10.1038/nphoton.2015.208
Prucnal, P. R. & Shastri, B. J. Neuromorphic Photonics (CRC Press, 2017).
Caulfield, H. J. & Dolev, S. Why future supercomputing requires optics. Nat. Photon. 4, 261–263 (2010).
doi: 10.1038/nphoton.2010.94
Zhang, C., Zhang, S., Peters, J. D. & Bowers, J. E. 8 × 8 × 40 Gbps fully integrated silicon photonic network on chip. Optica 3, 785–786 (2016).
doi: 10.1364/OPTICA.3.000785
Shen, Y. W. et al. Silicon photonics for extreme scale systems. J. Lightwave Technol. 37, 245–259 (2019).
doi: 10.1109/JLT.2019.2897365
Wade, M. et al. In 2018 European Conference on Optical Communication (ECOC). 1–3 (IEEE, 2018).
Wuttig, M., Bhaskaran, H. & Taubner, T. Phase-change materials for non-volatile photonic applications. Nat. Photon. 11, 465–476 (2017).
doi: 10.1038/nphoton.2017.126
Yang, Z. & Ramanathan, S. Breakthroughs in photonics 2014: phase change materials for photonics. IEEE Photon. J. 7, 1–5 (2015).
doi: 10.1109/JPHOT.2015.2504960
Zhang, W., Mazzarello, R., Wuttig, M. & Ma, E. Designing crystallization in phase-change materials for universal memory and neuro-inspired computing. Nat. Rev. Mater. 4, 150–168 (2019).
doi: 10.1038/s41578-018-0076-x
Briggs, R. M., Pryce, I. M. & Atwater, H. A. Compact silicon photonic waveguide modulator based on the vanadium dioxide metal-insulator phase transition. Opt. Express 18, 11192–11201 (2010).
pubmed: 20588978
doi: 10.1364/OE.18.011192
Wang, Q. et al. Optically reconfigurable metasurfaces and photonic devices based on phase change materials. Nat. Photon. 10, 60–U75 (2016).
doi: 10.1038/nphoton.2015.247
Chu, C. H. et al. Active dielectric metasurface based on phase‐change medium. Laser Photon. Rev. 10, 986–994 (2016).
doi: 10.1002/lpor.201600106
Yin, X. et al. Beam switching and bifocal zoom lensing using active plasmonic metasurfaces. Light. Sci. Appl. 6, e17016 (2017).
pubmed: 30167272
pmcid: 6062225
doi: 10.1038/lsa.2017.16
Wu, C. et al. Low-loss integrated photonic switch using subwavelength patterned phase change material. ACS Photon. 6, 87–92 (2018).
doi: 10.1021/acsphotonics.8b01516
Cheng, Z. et al. Device-level photonic memories and logic applications using phase-change materials. Adv. Mater. 30, e1802435 (2018).
pubmed: 29940084
doi: 10.1002/adma.201802435
Xu, P., Zheng, J., Doylend, J. K. & Majumdar, A. Low-loss and broadband nonvolatile phase-change directional coupler switches. ACS Photon. 6, 553–557 (2019).
doi: 10.1021/acsphotonics.8b01628
Zhang, Y. et al. Broadband transparent optical phase change materials for high-performance nonvolatile photonics. Nat. Commun. 10, 4279 (2019).
pubmed: 31570710
pmcid: 6768866
doi: 10.1038/s41467-019-12196-4
de Galarreta, C. R. et al. Nonvolatile reconfigurable phase-change metadevices for beam steering in the near infrared. Adv. Funct. Mater. 28, 1704993 (2018).
doi: 10.1002/adfm.201704993
Stegmaier, M., Ríos, C., Bhaskaran, H., Wright, C. D. & Pernice, W. H. P. Nonvolatile all-optical 1 × 2 switch for chipscale photonic networks. Adv. Opt. Mater. 5, 1600346 (2017).
doi: 10.1002/adom.201600346
Zhang, Q. et al. Broadband nonvolatile photonic switching based on optical phase change materials: beyond the classical figure-of-merit. Opt. Lett. 43, 94 (2018).
pubmed: 29328204
doi: 10.1364/OL.43.000094
Li, X. et al. Fast and reliable storage using a 5-bit, non-volatile photonic memory cell. Optica 6, 1–6 (2019).
doi: 10.1364/OPTICA.6.000001
Martins, T. et al. Fiber-integrated phase-change reconfigurable optical attenuator. Appl. Photon. 4, 111301 (2019).
doi: 10.1063/1.5116000
Zheng, J. et al. Nonvolatile electrically reconfigurable integrated photonic switch enabled by a silicon PIN diode heater. Adv. Mater. 32, e2001218 (2020).
pubmed: 32588481
doi: 10.1002/adma.202001218
George, J. K. et al. Neuromorphic photonics with electro-absorption modulators. Opt. Express 27, 5181–5191 (2019).
pubmed: 30876120
doi: 10.1364/OE.27.005181
Tait, A. N. et al. Silicon photonic modulator neuron. Phys. Rev. Appl. 11, 064043 (2019).
doi: 10.1103/PhysRevApplied.11.064043
Hamerly, R., Bernstein, L., Sludds, A., Soljacic, M. & Englund, D. Large-scale optical neural networks based on photoelectric multiplication. Phys. Rev. X 9, 021032 (2019).
Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441–446 (2017).
doi: 10.1038/nphoton.2017.93
Sun, J., Timurdogan, E., Yaacobi, A., Hosseini, E. S. & Watts, M. R. Large-scale nanophotonic phased array. Nature 493, 195–199 (2013).
pubmed: 23302859
doi: 10.1038/nature11727
Ribeiro, A., Ruocco, A., Vanacker, L. & Bogaerts, W. Demonstration of a 4x4-port universal linear circuit. Optica 3, 1348–1357 (2016).
doi: 10.1364/OPTICA.3.001348
Bocker, R. P. Matrix multiplication using incoherent optical techniques. Appl. Opt. 13, 1670–1676 (1974).
pubmed: 20134529
doi: 10.1364/AO.13.001670
Ríos, C. et al. In-memory computing on a photonic platform. Sci. Adv. 5, eaau5759 (2019).
pubmed: 30793028
pmcid: 6377270
doi: 10.1126/sciadv.aau5759
Chakraborty, I., Saha, G. & Roy, K. Photonic in-memory computing primitive for spiking neural networks using phase-change materials. Phys. Rev. Appl. 11, 014063 (2019).
doi: 10.1103/PhysRevApplied.11.014063
Caulfield, H. J., Kinser, J. & Rogers, S. K. Optical neural networks. Proc. IEEE 77, 1573–1583 (1989).
doi: 10.1109/5.40669
Feldmann, J. et al. Parallel convolution processing using an integrated photonic tensor core. arXiv preprint arXiv:2002.00281 (2020).
Feldmann, J., Youngblood, N., Wright, C. D., Bhaskaran, H. & Pernice, W. H. P. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208–214 (2019).
pubmed: 31068721
pmcid: 6522354
doi: 10.1038/s41586-019-1157-8
Li, Z. et al. Controlling propagation and coupling of waveguide modes using phase-gradient metasurfaces. Nat. Nanotechnol. 12, 675–683 (2017).
pubmed: 28416817
doi: 10.1038/nnano.2017.50
Park, J.-W. et al. Optical properties of pseudobinary GeTe, Ge2Sb2Te5, GeSb2Te4, GeSb4Te7, and Sb2Te3 from ellipsometry and density functional theory. Phys. Rev. B 80, 115209 (2009).
doi: 10.1103/PhysRevB.80.115209
Liu, Y., Aziz, M. M., Shalini, A., Wright, C. D. & Hicken, R. J. Crystallization of Ge2Sb2Te5 films by amplified femtosecond optical pulses. J. Appl. Phys. 112, 123526 (2012).
Farmakidis, N. et al. Plasmonic nanogap enhanced phase-change devices with dual electrical-optical functionality. Sci. Adv. 5, eaaw2687 (2019).
pubmed: 31819898
pmcid: 6884412
doi: 10.1126/sciadv.aaw2687
Zhang, H. et al. Miniature multilevel optical memristive switch using phase change material. ACS Photon. 6, 2205–2212 (2019).
doi: 10.1021/acsphotonics.9b00819
Rodriguez-Hernandez, G., Hosseini, P., Ríos, C., Wright, C. D. & Bhaskaran, H. Mixed-mode electro-optical operation of Ge2Sb2Te5 nanoscale crossbar devices. Adv. Electron. Mater. 3, 1700079 (2017).
doi: 10.1002/aelm.201700079
Ríos, C. et al. Integrated all-photonic non-volatile multi-level memory. Nat. Photon. 9, 725–732 (2015).
doi: 10.1038/nphoton.2015.182
Giannopoulos, I. et al. In 2018 IEEE International Electron Devices Meeting (IEDM). 27.27.21–27.27.24 (IEEE, 2018).
Le Gallo, M. et al. Mixed-precision in-memory computing. Nat. Electron. 1, 246–253 (2018).
doi: 10.1038/s41928-018-0054-8
Nahmias, M. A. et al. Photonic multiply-accumulate operations for neural networks. IEEE J. Sel. Top. Quant. 26, 1–18 (2020).
doi: 10.1109/JSTQE.2019.2941485
Convolution Neural Network - simple code - simple to use (MATLAB Central File Exchange, 2020).
Xiong, C. et al. Monolithic 56 Gb/s silicon photonic pulse-amplitude modulation transmitter. Optica 3, 1060–1065 (2016).
doi: 10.1364/OPTICA.3.001060
Moazeni, S. et al. A 40-Gb/s PAM-4 Transmitter Based on a Ring-Resonator Optical DAC in 45-nm SOI CMOS. IEEE J. Solid-State Circuits 52, 3503–3516 (2017).
doi: 10.1109/JSSC.2017.2748620
Sawchuk, A. A. & Jenkins, B. K. In Optical Computing. 143–153 (International Society for Optics and Photonics 1986).
Joshi, A. et al. In 2009 3rd ACM/IEEE International Symposium on Networks-on-Chip. 124-133 (IEEE, 2009).
Khope, A. S. P. et al. Multi-wavelength selective crossbar switch. Opt. Express 27, 5203–5216 (2019).
pubmed: 30876122
doi: 10.1364/OE.27.005203
Ohno, S., Toprasertpong, K., Takagi, S. & Takenaka, M. Si microring resonator crossbar arrays for deep learning accelerator. Jpn J. Appl. Phys. 59, SGGE04 (2020).
doi: 10.35848/1347-4065/ab6d82
Nahmias, M. A. et al. Photonic multiply-accumulate operations for neural networks. IEEE J. Sel. Top. Quant. Electron. 26, 1–18 (2019).
doi: 10.1109/JSTQE.2019.2941485
Han, S., Seok, T. J., Quack, N., Yoo, B.-W. & Wu, M. C. Large-scale silicon photonic switches with movable directional couplers. Optica 2, 370–375 (2015).
doi: 10.1364/OPTICA.2.000370
Tait, A. N., Chang, J., Shastri, B. J., Nahmias, M. A. & Prucnal, P. R. Demonstration of WDM weighted addition for principal component analysis. Opt. Express 23, 12758 (2015).
pubmed: 26074530
doi: 10.1364/OE.23.012758
Tait, A. N., Nahmias, M. A., Shastri, B. J. & Prucnal, P. R. Broadcast and weight: an integrated network for scalable photonic spike processing. J. Lightwave Technol. 32, 4029–4041 (2014).
doi: 10.1109/JLT.2014.2345652
Silver, D. et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science 362, 1140–114 (2018).
pubmed: 30523106
doi: 10.1126/science.aar6404
Jouppi, N. P. et al. In Proceedings of the 44th Annual International Symposium on Computer Architecture - ISCA ‘17 1-12 (ACM Press, New York, New York, USA, 2017).
Li, X. et al. Experimental investigation of silicon and silicon nitride platforms for phase-change photonic in-memory computing. Optica 7, 218–225 (2020).
doi: 10.1364/OPTICA.379228
Gayen, D. K., Chattopadhyay, T., Pal, R. K. & Roy, J. N. All-optical Multiplication with the help of Semiconductor Optical Amplifier—assisted Sagnac Switch. J. Comput. Electron. 9, 57–67 (2010).
doi: 10.1007/s10825-010-0305-z
Atabaki, A. H. et al. Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip. Nature 556, 349–354 (2018).
pubmed: 29670262
doi: 10.1038/s41586-018-0028-z
Bangari, V. et al. Digital electronics and analog photonics for convolutional neural networks (DEAP-CNNs). IEEE J. Sel. Top. Quant. 26, 1–13 (2020).
doi: 10.1109/JSTQE.2019.2945540