Integrated photonic neuromorphic computing: opportunities and challenges – Nature Reviews Electrical Engineering
Krizhevsky, A., Sutskever, I. & Hinton, G. E. ImageNet classification with deep convolutional neural networks. In Proc. 26th Annual Conference on Neural Information Processing Systems (NIPS), Advances in Neural Information Processing Systems 25 (eds Pereira, F. et al.) (NeurIPS, 2012).
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436444 (2015).
Google Scholar
Roy, K., Jaiswal, A. & Panda, P. Towards spike-based machine intelligence with neuromorphic computing. Nature 575, 607617 (2019).
Google Scholar
Wetzstein, G. et al. Inference in artificial intelligence with deep optics and photonics. Nature 588, 3947 (2020).
Google Scholar
Mead, C. Neuromorphic electronic systems. Proc. IEEE 78, 16291636 (1990).
Google Scholar
Moore, G. E. Cramming more components onto integrated circuits. Proc. IEEE 86, 8285 (1998).
Google Scholar
Waldrop, M. M. The chips are down for Moores law. Nature 530, 144 (2016).
Google Scholar
Koomey, J. et al. Implications of historical trends in the electrical efficiency of computing. IEEE Ann. Hist. Comput. 33, 4654 (2010).
Google Scholar
Brown, R. E. et al. Report to congress on server and data center energy efficiency: public law 109-431 (Lawrence Berkeley National Lab, 2008).
Shastri, B. J. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photon. 15, 102114 (2021).
Google Scholar
Davies, M. et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 8299 (2018).
Google Scholar
Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668673 (2014).
Google Scholar
Furber, S. B. et al. The SpiNNaker project. Proc. IEEE 102, 652665 (2014).
Google Scholar
Meng, X. et al. Compact optical convolution processing unit based on multimode interference. Nat. Commun. 14, 3000 (2023).
Google Scholar
Nahmias, M. A. et al. Photonic multiply-accumulate operations for neural networks. IEEE J. Sel. Top. Quantum Electron. 26, 118 (2019).
Google Scholar
Xiang, C. et al. 3D integration enables ultralow-noise isolator-free lasers in silicon photonics. Nature 620, 7885 (2023).
Google Scholar
He, M. et al. High-performance hybrid silicon and lithium niobate MachZehnder modulators for 100 Gbit s1 and beyond. Nat. Photon. 13, 359364 (2019).
Google Scholar
Rahim, A. et al. Taking silicon photonics modulators to a higher performance level: state-of-the-art and a review of new technologies. Adv. Photon. 3, 024003 (2021).
Google Scholar
Lischke, S. et al. Ultra-fast germanium photodiode with 3-dB bandwidth of 265GHz. Nat. Photon.15, 925931 (2021).
Google Scholar
Atabaki, A. H. et al. Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip. Nature 556, 349354 (2018).
Google Scholar
Liu, Y. et al. A photonic integrated circuit-based erbium-doped amplifier. Science 376, 13091313 (2022).
Google Scholar
Nozaki, K. et al. Femtofarad optoelectronic integration demonstrating energy-saving signal conversion and nonlinear functions. Nat. Photon.13, 454459 (2019).
Google Scholar
Youngblood, N., Ros Ocampo, C. A., Pernice, W. H. P. & Bhaskaran, H. Integrated optical memristors. Nat. Photon. 17, 561572 (2023).
Google Scholar
Farmakidis, N. et al. Plasmonic nanogap enhanced phase-change devices with dual electrical-optical functionality. Sci. Adv. 5, eaaw2687 (2019). This is the first implementation of an electronically programmable nanoscale photonic memory.
Google Scholar
Ros, C. et al. Integrated all-photonic non-volatile multi-level memory. Nat. Photon. 9, 725732 (2015).
Google Scholar
Tait, A. N. et al. Neuromorphic photonic networks using silicon photonic weight banks. Sci. Rep. 7, 7430 (2017).
Google Scholar
Dai, D. Silicon nanophotonic integrated devices for on-chip multiplexing and switching. J. Lightwave Technol. 35, 572587 (2016).
Google Scholar
Hochberg, M. & Baehr-Jones, T. Towards fabless silicon photonics. Nat. Photon. 4, 492494 (2010).
Google Scholar
Xiang, S. et al. A review: photonics devices, architectures, and algorithms for optical neural computing. J. Semicond. 42, 023105 (2021).
Google Scholar
Prucnal, P. R. & Shastri, B. J. Neuromorphic Photonics (CRC Press, 2017).
Peserico, N., Shastri, B. J. & Sorger, V. J. Integrated photonic tensor processing unit for a matrix multiply: a review. J. Lightwave Technol. 41, 37043716 (2023).
Google Scholar
Al-Qadasi, M. A., Chrostowski, L., Shastri, B. J. & Shekhar, S. Scaling up silicon photonic-based accelerators: challenges and opportunities. APL Photon.7, 020902 (2022).
Google Scholar
Capmany, J. & Prez, D. Programmable Integrated Photonics (Oxford University Press, 2020).
Prucnal, P. R. & Shastri, B. J. (eds) Neuromorphic Photonics (CRC Press, 2017).
Xu, X. et al. 11 TOPS photonic convolutional accelerator for optical neural networks. Nature 589, 4451 (2021).
Google Scholar
Tait, A. N. et al. Microring weight banks. IEEE J. Sel. Top. Quantum Electron. 22, 312325 (2016).
Google Scholar
Feldmann, J. et al. Parallel convolutional processing using an integrated photonic tensor core. Nature 589, 5258 (2021).
Google Scholar
Ling, Q. et al. On-chip optical matrixvector multiplier based on mode division multiplexing. Chip 2, 100061 (2023).
Google Scholar
Ros, C. et al. In-memory computing on a photonic platform. Sci. Adv. https://doi.org/10.1126/sciadv.aau5759 (2019).
Zhou, W. et al. In-memory photonic dot-product engine with electrically programmable weight banks. Nat. Commun. 14, 2887 (2023).
Google Scholar
Tait, A. N. et al. Microring weight banks. IEEE J. Sel. Top. Quantum Electron. https://doi.org/10.1109/JSTQE.2016.2573583 (2016).
Yang, L. et al. On-chip CMOS-compatible optical signal processor. Opt. Expr.20, 1356013565 (2012).
Google Scholar
Tait, A. N. et al. Continuous calibration of microring weights for analog optical networks. IEEE Photon. Technol. Lett. 28, 887890 (2016).
Google Scholar
Marquez, B. A. et al. Fully-integrated photonic tensor core for image convolutions. Nanotechnology https://doi.org/10.1088/1361-6528/acde83 (2023).
Tait, A. N. et al. Broadcast and weight: an integrated network for scalable photonic spike processing. J. Lightwave Technol. 32, 34273439 (2014).
Google Scholar
Bai, B. et al. Microcomb-based integrated photonic processing unit. Nat. Commun. 14, 66 (2023).
Google Scholar
Farmakidis, N. et al. Scalable high-precision trimming of photonic resonances by polymer exposure to energetic beams. Nano Lett. 23, 48004806 (2023).
Google Scholar
Zhang, Y. et al. Transient tap couplers for wafer-level photonic testing based on optical phase change materials. ACS Photon.8, 19031908 (2021).
Google Scholar
Chen, R. et al. Non-volatile electrically programmable integrated photonics with a 5-bit operation. Nat. Commun. 14, 3465 (2023).
Google Scholar
Brckerhoff-Plckelmann, F. et al. Event-driven adaptive optical neural network. Sci. Adv. 9, eadi9127 (2023).
Google Scholar
Lee, J. S. et al. Spatio-spectral control of coherent nanophotonics. Nanophotonics https://doi.org/10.1515/nanoph-2023-0651 (2024).
Ohno, S. et al. Si microring resonator crossbar array for on-chip inference and training of the optical neural network. ACS Photon.9, 26142622 (2022).
Google Scholar
Li, X. et al. On-chip phase change optical matrix multiplication core. In Proc. 2020 IEEE International Electron Devices Meeting (IEDM) 7.5.17.5.4 (IEEE, 2020).
Youngblood, N. et al. Phase change photonics for brain-inspired computing (Conference Presentation). In Proc. Micro- and Nanotechnology Sensors, Systems, and Applications XI (eds George, T. & Islam, M. S.) 109820P (SPIE, 2019).
Farmakidis, N. et al. Electronically reconfigurable photonic switches incorporating plasmonic structures and phase change materials. Adv. Sci. 9, 2200383 (2022).
Google Scholar
Brckerhoff-Plckelmann, F. et al. Broadband photonic tensor core with integrated ultra-low crosstalk wavelength multiplexers. Nanophotonics 11, 40634072 (2022).
Google Scholar
Meng, J. et al. Electrical programmable multilevel nonvolatile photonic random-access memory. Light Sci. Appl. 12, 189 (2023).
Google Scholar
Wendland, D. et al. Coherent dimension reduction with integrated photonic circuits exploiting tailored disorder. J. Opt. Soc. Am. B 40, B35B40 (2023).
Google Scholar
Aggarwal, S. et al. Reduced rank photonic computing accelerator. Optica 10, 10741080 (2023).
Google Scholar
Wei, M. et al. Electrically programmable phase-change photonic memory for optical neural networks with nanoseconds in situ training capability. Adv. Photon. 5, 046004 (2023).
Google Scholar
Brckerhoff-Plckelmann, F. et al. Hybrid electro-optic crossbar array for matrixvector multiplications. in CLEO: Science and Innovations (Optica Publishing Group, 2023).
Qu, Y. et al. Inverse design of an integrated-nanophotonics optical neural network. Sci. Bull. 65, 11771183 (2020).
Google Scholar
Lee, J. S. et al. Polarization-selective reconfigurability in hybridized-active-dielectric nanowires. Sci. Adv. 8, eabn9459 (2022).
Google Scholar
Alam, M. S. et al. Photonic integrated circuit for rapidly tunable orbital angular momentum generation using Sb2Se3ultralowloss phase change material. Adv. Opt. Mater. 10, 2200098 (2022).
Google Scholar
Miller, D. A. Self-configuring universal linear optical component. Photon. Res. 1, 115 (2013).
Google Scholar
Reck, M. et al. Experimental realization of any discrete unitary operator. Phys. Rev. Lett. 73, 58 (1994).
Google Scholar
Ribeiro, A. et al. Demonstration of a 44-port universal linear circuit. Optica 3, 13481357 (2016).
Google Scholar
Clements, W. R. et al. Optimal design for universal multiport interferometers. Optica 3, 14601465 (2016).
Google Scholar
Miller, D. A. Self-aligning universal beam coupler. Opt. Expr. 21, 63606370 (2013).
Google Scholar
Mennea, P. L. et al. Modular linear optical circuits. Optica 5, 10871090 (2018).
Google Scholar
Harris, N. C. et al. Linear programmable nanophotonic processors. Optica 5, 16231631 (2018).
Google Scholar
Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441 (2017). In this article, the scalable coherent photonic accelerator makes a significant impact on the field by showing on-chip deep learning.
Google Scholar
Pai, S. et al. Parallel programming of an arbitrary feedforward photonic network. IEEE J. Sel. Top. Quantum Electron. 26, 113 (2020).
Google Scholar
Bagherian, H. et al. On-chip optical convolutional neural networks. Preprint at arXiv https://doi.org/10.48550/arXiv.1808.03303 (2018).
Demirkiran, C. et al. An electro-photonic system for accelerating deep neural networks. ACM J. Emerg. Technol. Comput. Syst. 19, 131 (2023).
Google Scholar
Giamougiannis, G. et al. Analog nanophotonic computing going practical: silicon photonic deep learning engines for tiled optical matrix multiplication with dynamic precision. Nanophotonics 12, 963973 (2023).
Google Scholar
Moralis-Pegios, M. et al. Neuromorphic silicon photonics and hardware-aware deep learning for high-speed inference. J. Lightwave Technol. 40, 32433254 (2022).
Google Scholar
Zhou, H. et al. Self-configuring and reconfigurable silicon photonic signal processor. ACS Photon. 7, 792799 (2020).
Google Scholar
Totovic, A. et al. Programmable photonic neural networks combining WDM with coherent linear optics. Sci. Rep. 12, 5605 (2022).
Google Scholar
Choutagunta, K. et al. Adapting MachZehnder mesh equalizers in direct-detection mode-division-multiplexed links. J. Lightwave Technol. 38, 723735 (2019).
Google Scholar
Youngblood, N. Coherent photonic crossbar arrays for large-scale matrix-matrix multiplication. IEEE J. Sel. Top. Quantum Electron. https://doi.org/10.1109/JSTQE.2022.3171167 (2022).
Ding, C. et al. CirCNN: accelerating and compressing deep neural networks using block-circulant weight matrices. In Proc. 50th Annual IEEE/ACM International Symposium on Microarchitecture (2017).
Nakajima, M. et al. Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware. Nat. Commun. 13, 7847 (2022).
Google Scholar
Cruz-Cabrera, A. A. et al. Reinforcement and backpropagation training for an optical neural network using self-lensing effects. IEEE Trans. Neural Netw. 11, 14501457 (2000).
Google Scholar
Hughes, T. W. et al. Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica 5, 864871 (2018).
Google Scholar
Pai, S. et al. Experimentally realized in situ backpropagation for deep learning in photonic neural networks. Science 380, 398404 (2023).
Google Scholar
Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 10041008 (2018).
Google Scholar
Qian, C. et al. Performing optical logic operations by a diffractive neural network. Light Sci. Appl. 9, 59 (2020).
Google Scholar
Zhou, T. et al. Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit. Nat. Photon. 15, 367373 (2021).
Google Scholar
Fu, T. et al. Photonic machine learning with on-chip diffractive optics. Nat. Commun. 14, 70 (2023). This article shows an important implementation of optical machine learning with on-chip diffractive optics.
Google Scholar
Wang, Z. et al. Integrated photonic metasystem for image classifications at telecommunication wavelength. Nat. Commun. 13, 2131 (2022).
Google Scholar
Chen, Z. et al. Deep learning with coherent VCSEL neural networks. Nat. Photon. https://doi.org/10.48550/arXiv.2207.05329 (2023).
Zhu, H. et al. Space-efficient optical computing with an integrated chip diffractive neural network. Nat. Commun. 13, 1044 (2022). This article has been highly influential for neuromorphic hardware, which demonstrates a space-efficient optical computational unit that realizes on-chip diffractive neural network.
Google Scholar
Liao, K., Dai, T., Yan, Q., Hu, X. & Gong, Q. Integrated photonic neural networks: opportunities and challenges. ACS Photon. 10, 20012010 (2023).
Google Scholar
Seok, T. J. et al. Large-scale broadband digital silicon photonic switches with vertical adiabatic couplers. Optica 3, 6470 (2016).
Google Scholar
Sattari, H. et al. Silicon photonic MEMS phase-shifter. Opt. Expr. 27, 1895918969 (2019).
Google Scholar
Sun, H. et al. Silicon photonic phase shifters and their applications: a review. Micromachines 13, 1509 (2022).
Google Scholar
Qi, Y. & Li, Y. Integrated lithium niobate photonics. Nanophotonics 9, 12871320 (2020).
Google Scholar
Giamougiannis, G. et al. A coherent photonic crossbar for scalable universal linear optics. J. Lightwave Technol. 41, 24252442 (2023).
Google Scholar
Wu, T. et al. Lithography-free reconfigurable integrated photonic processor. Nat. Photon. 17, 644645 (2023).
Google Scholar
Fan, L. et al. Experimental realization of convolution processing in photonic synthetic frequency dimensions. Sci. Adv. 9, eadi4956 (2023).
Google Scholar
Zhao, H. et al. Enabling scalable optical computing in synthetic frequency dimension using integrated cavity acousto-optics. Nat. Commun. 13, 5426 (2022).
Google Scholar
Mourgias-Alexandris, G. et al. Noise-resilient and high-speed deep learning with coherent silicon photonics. Nat. Commun. 13, 5572 (2022).
Google Scholar
Passalis, N. et al. Training noise-resilient recurrent photonic networks for financial time series analysis. In Proc. 2020 28th European Signal Processing Conference (EUSIPCO) 15561560 (European Association for Signal Processing, 2021).
Varri, A. et al. Scalable nonvolatile tuning of photonic computational memories by automated silicon ion implantation. Adv. Mater. 36, 2310596 (2023).
Google Scholar
Jayatilleka, H. et al. Post-fabrication trimming of silicon photonic ring resonators at wafer-scale. J. Lightwave Technol. 39, 50835088 (2021).
Google Scholar
Giamougiannis, G. et al. Universal linear optics revisited: new perspectives for neuromorphic computing with silicon photonics. IEEE J. Sel. Top. Quantum Electron. 29, 116 (2022).
Google Scholar
Jha, A., Huang, C. & Prucnal, P. R. Reconfigurable all-optical nonlinear activation functions for neuromorphic photonics. Opt. Lett. 45, 48194822 (2020).
Google Scholar
Mourgias-Alexandris, G. et al. An all-optical neuron with sigmoid activation function. Opt. Expr. 27, 96209630 (2019).
Google Scholar
Hurtado, A. et al. Investigation of vertical cavity surface emitting laser dynamics for neuromorphic photonic systems. Appl. Phys. Lett. 100, 103703 (2012).
Google Scholar
Crnjanski, J. et al. Adaptive sigmoid-like and PReLU activation functions for all-optical perceptron. Opt. Lett. 46, 20032006 (2021).
Google Scholar
Rasmussen, T. S., Yu, Y. & Mork, J. All-optical non-linear activation function for neuromorphic photonic computing using semiconductor Fano lasers. Opt. Lett. 45, 38443847 (2020).
Google Scholar
Rnn, J. et al. Ultra-high on-chip optical gain in erbium-based hybrid slot waveguides. Nat. Commun. 10, 432 (2019).
Google Scholar
Cheng, Z. et al. On-chip photonic synapse. Sci. Adv. 3, e1700160 (2017).
Google Scholar
Zhou, W. et al. Phase-change materials for energy-efficient photonic memory and computing. MRS Bull. 47, 502510 (2022).
Google Scholar
Feldmann, J. et al. All-optical spiking neurosynaptic networks with self-learning capabilities. Nature 569, 208214 (2019).
Google Scholar
Zhou, W. et al. Artificial biphasic synapses based on nonvolatile phasechange photonic memory cells. Phys. Status Sol. Rapid Res. Lett. 16, 2100487 (2022).
Google Scholar
Aggarwal, S. et al. All optical tunable RF filter using elemental antimony. Nanophotonics https://doi.org/10.1515/nanoph-2023-0654 (2024).
Pappas, C. et al. Programmable Tanh-, ELU-, sigmoid-, and sin-based nonlinear activation functions for neuromorphic photonics. IEEE J. Sel. Top. Quantum Electron. 29, 110 (2023).
Fard, M. M. P. et al. Experimental realization of arbitrary activation functions for optical neural networks. Opt. Expr.28, 1213812148 (2020).
Google Scholar
Tait, A. N. et al. Silicon photonic modulator neuron. Phys. Rev. Appl. 11, 064043 (2019). This article is one of the pivotal realizations of a silicon photonic modulator neuron.
Google Scholar
George, J. K. et al. Neuromorphic photonics with electro-absorption modulators. Opt. Expr. 27, 51815191 (2019).
Google Scholar
Amin, R. et al. ITO-based electro-absorption modulator for photonic neural activation function. APL Mater. 7, 081112 (2019).
Google Scholar
Skalli, A. et al. Photonic neuromorphic computing using vertical cavity semiconductor lasers. Opt. Mater. Expr. 12, 23952414 (2022).
Google Scholar
Jaeger, H. & Haas, H. Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304, 7880 (2004).
Google Scholar
Jaeger, H. Short term memory in echo state networks. GMD Report 152 (GMD Forschungszentrum Informationstechnik, 2002).
Van der Sande, G., Brunner, D. & Soriano, M. C. Advances in photonic reservoir computing. Nanophotonics 6, 561576 (2017).
Google Scholar
Brunner, D. & Fischer, I. Reconfigurable semiconductor laser networks based on diffractive coupling. Opt. Lett. 40, 38543857 (2015).
Google Scholar
Rafayelyan, M. et al. Large-scale optical reservoir computing for spatiotemporal chaotic systems prediction. Phys. Rev. X 10, 041037 (2020).
Larger, L. et al. High-speed photonic reservoir computing using a time-delay-based architecture: million words per second classification. Phys. Rev. X 7, 011015 (2017).
Paquot, Y. et al. Optoelectronic reservoir computing. Sci. Rep. 2, 287 (2012).
Google Scholar
Larger, L. et al. Photonic information processing beyond turing: an optoelectronic implementation of reservoir computing. Opt. Expr. 20, 32413249 (2012).
Google Scholar
Vandoorne, K. et al. Parallel reservoir computing using optical amplifiers. IEEE Trans. Neural Netw. 22, 14691481 (2011).
Google Scholar
Vandoorne, K. et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat. Commun. 5, 3541 (2014).
Google Scholar
Boikov, I. K., Brunner, D. & De Rossi, A. Evanescent coupling of nonlinear integrated cavities for all-optical reservoir computing. New J. Phys. 25, 093056 (2023).
Google Scholar
Staffoli, E. et al. Nonlinear response of silicon photonics microresonators for reservoir computing neural network. Preprint at arXiv https://doi.org/10.48550/arXiv.2302.10203 (2023).
Denis-Le Coarer, F. et al. All-optical reservoir computing on a photonic chip using silicon-based ring resonators. IEEE J. Sel. Top. Quantum Electron. 24, 18 (2018).
Google Scholar
Mesaritakis, C., Papataxiarhis, V. & Syvridis, D. Micro ring resonators as building blocks for an all-optical high-speed reservoir-computing bit-pattern-recognition system. J. Opt. Soc. Am. B 30, 30483055 (2013).
Google Scholar
Takano, K. et al. Compact reservoir computing with a photonic integrated circuit. Opt. Expr. 26, 2942429439 (2018).
Google Scholar
Nakajima, M., Tanaka, K. & Hashimoto, T. Scalable reservoir computing on coherent linear photonic processor. Commun. Phys. 4, 20 (2021).
Google Scholar
McMahon, P. L. et al. A fully programmable 100-spin coherent Ising machine with all-to-all connections. Science 354, 614617 (2016).
Google Scholar
Roques-Carmes, C. & Soljai, M. Photonic Ising machines go big. Physics 12, 61 (2019).
Google Scholar
Roques-Carmes, C. et al. Heuristic recurrent algorithms for photonic Ising machines. Nat. Commun. 11, 249 (2020).
Google Scholar
Hamerly, R. et al. Experimental investigation of performance differences between coherent Ising machines and a quantum annealer. Sci. Adv. 5, eaau0823 (2019).
Google Scholar
Marandi, A. et al. Network of time-multiplexed optical parametric oscillators as a coherent Ising machine. Nat. Photon. 8, 937942 (2014).
Google Scholar
Inagaki, T. et al. A coherent Ising machine for 2000-node optimization problems. Science 354, 603606 (2016).
Google Scholar
Inagaki, T. et al. Large-scale Ising spin network based on degenerate optical parametric oscillators. Nat. Photon. 10, 415419 (2016).
Google Scholar
Prabhu, M. et al. Accelerating recurrent Ising machines in photonic integrated circuits. Optica 7, 551558 (2020).
Google Scholar
Vzquez, M. R. et al. Optical NP problem solver on laser-written waveguide platform. Opt. Expr. 26, 702710 (2018).
Google Scholar
Pierangeli, D. et al. Noise-enhanced spatial-photonic Ising machine. Nanophotonics 9, 41094116 (2020).
Google Scholar
Pierangeli, D., Marcucci, G. & Conti, C. Adiabatic evolution on a spatial-photonic Ising machine. Optica 7, 15351543 (2020).
Google Scholar
Pierangeli, D., Marcucci, G. & Conti, C. Large-scale photonic Ising machine by spatial light modulation. Phys. Rev. Lett. 122, 213902 (2019).
Google Scholar
Owen-Newns, D. et al. Photonic spiking neural networks with highly efficient training protocols for ultrafast neuromorphic computing systems. Intell. Comput. 2, 0031 (2023).
Google Scholar
Shastri, B. J. et al. Spike processing with a graphene excitable laser. Sci. Rep. 6, 19126 (2016).
Google Scholar
Robertson, J. et al. Controlled inhibition of spiking dynamics in VCSELs for neuromorphic photonics: theory and experiments. Opt. Lett. 42, 15601563 (2017).
Google Scholar
Jha, A. et al. Photonic spiking neural networks and graphene-on-silicon spiking neurons. J. Lightwave Technol. 40, 29012914 (2022).
Google Scholar
Hurtado, A. et al. Nonlinear dynamics induced by parallel and orthogonal optical injection in 1550nm vertical-cavity surface-emitting lasers (VCSELs). Opt. Expr. 18, 94239428 (2010).
Google Scholar
Al-Seyab, R. et al. Controlled single-and multiple-pulse excitability in VCSELs for novel spiking photonic neurons. In 2014 International Semiconductor Laser Conf. (IEEE, 2014).
Hurtado, A. & Javaloyes, J. Controllable spiking patterns in long-wavelength vertical cavity surface emitting lasers for neuromorphic photonics systems. Appl. Phys. Lett. 107, 241103 (2015).
Google Scholar
Pammi, V. A. et al. Photonic computing with single and coupled spiking micropillar lasers. IEEE J. Sel. Top. Quantum Electron. 26, 17 (2019).
Google Scholar
Selmi, F. et al. Spike latency and response properties of an excitable micropillar laser. Phys. Rev. E 94, 042219 (2016).
Google Scholar
Barbay, S., Kuszelewicz, R. & Yacomotti, A. M. Excitability in a semiconductor laser with saturable absorber. Opt. Lett. 36, 44764478 (2011).
Google Scholar
Robertson, J. et al. Image edge detection with a photonic spiking VCSEL-neuron. Opt. Expr.28, 3752637537 (2020).
Google Scholar
Hejda, M. et al. Neuromorphic encoding of image pixel data into rate-coded optical spike trains with a photonic VCSEL-neuron. APL Photon.6, 060802 (2021).
Google Scholar
Moughames, J. et al. 3D printed multimode-splitters for photonic interconnects. Opt. Mater. Expr. 10, 29522961 (2020). This article showed that 3D-printed components could form highly efficient multimode photonic interconnects and couplers.
Google Scholar
Grabulosa, A. et al. Additive 3D photonic integration that is CMOS compatible. Nanotechnology 34, 322002 (2023).
Google Scholar
Billah, M. et al. Multi-chip integration of lasers and silicon photonics by photonic wire bonding. in CLEO: Science and Innovations https://doi.org/10.1364/CLEO_SI.2015.STu2F.2 (Optica Publishing Group, 2015).
Lindenmann, N. et al. Photonic wire bonding: a novel concept for chip-scale interconnects. Opt. Expr. 20, 1766717677 (2012).
Google Scholar
Lindenmann, N. et al. Connecting silicon photonic circuits to multicore fibers by photonic wire bonding. J. Lightwave Technol. 33, 755760 (2015).
Google Scholar
Crosnier, G. et al. Hybrid indium phosphide-on-silicon nanolaser diode. Nat. Photon.11, 297300 (2017).
Google Scholar
Inagaki, T. et al. Collective and synchronous dynamics of photonic spiking neurons. Nat. Commun. 12, 2325 (2021).
Google Scholar
Romeira, B. et al. Excitability and optical pulse generation in semiconductor lasers driven by resonant tunneling diode photo-detectors. Opt. Expr.21, 2093120940 (2013).
Google Scholar
Hejda, M. et al. Artificial optoelectronic spiking neuron based on a resonant tunnelling diode coupled to a vertical cavity surface emitting laser. Nanophotonics 12, 857867 (2022).
Google Scholar
Hejda, M. et al. Resonant tunneling diode nano-optoelectronic excitable nodes for neuromorphic spike-based information processing. Phys. Rev. Appl. 17, 024072 (2022).
Google Scholar
Lourenco, J. et al. Resonant tunnelling diodephotodetectors for spiking neural networks. J. Phys. Conf. Ser. 2407, 012047 (2022) .
Google Scholar
Ryckman, J. D. et al. Ultra-compact silicon photonic devices reconfigured by an optically induced semiconductor-to-metal transition. Opt. Expr.21, 1075310763 (2013).
Google Scholar
Aggarwal, S. et al. Antimony as a programmable element in integrated nanophotonics. Nano Lett. 22, 35323538 (2022).
Google Scholar
Dong, B. et al. Higher-dimensional processing using a photonic tensor core with continuous-time data. Nat. Photon.17, 10801088 (2023). This article demonstrates a crossbar array architecture in which an additional degree of parallelization was added using radiofrequency time-division multiplexing.
Google Scholar
Bandyopadhyay, S. et al. Single chip photonic deep neural network with accelerated training. Preprint at https://arxiv.org/abs/2208.01623 (2022).
Shastri, B. J. et al. Graphene excitable laser for photonic spike processing. In Proc. 2013 IEEE Photonics Conference (IEEE, 2013). This article presents a unified platform for spike processing with a graphene-coupled laser system that can simultaneously exhibit logic-level restoration, cascadability and inputoutput isolation.
Robertson, J. et al. Ultrafast neuromorphic photonic image processing with a VCSEL neuron. Sci. Rep. 12, 4874 (2022).
Google Scholar
Li, H.-Y. S., Qiao, Y. & Psaltis, D. Optical network for real-time face recognition. Appl. Opt. 32, 50265035 (1993).
Google Scholar
Psaltis, D. et al. Holography in artificial neural networks. Nature 343, 325330 (1990).
Google Scholar
Farhat, N. H. et al. Optical implementation of the Hopfield model. Appl. Opt. 24, 14691475 (1985).
Google Scholar
Gissibl, T. et al. Two-photon direct laser writing of ultracompact multi-lens objectives. Nat. Photon. 10, 554560 (2016).
Google Scholar
Ristok, S. et al. Stitching-free 3D printing of millimeter-sized highly transparent spherical and aspherical optical components. Opt. Mater. Expr. 10, 23702378 (2020).
Google Scholar
Wang, T. et al. An optical neural network using less than 1 photon per multiplication. Nat. Commun. 13, 123 (2022).
Google Scholar
Dinc, N. U. et al. Computer generated optical volume elements by additive manufacturing. Nanophotonics 9, 41734181 (2020).
Google Scholar
Porte, X. et al. Direct (3+1) D laser writing of graded-index optical elements. Optica 8, 12811287 (2021).
Google Scholar
Dinc, N. U. et al. From 3D to 2D and back again. Nanophotonics 12, 777793 (2023).
Google Scholar
Dietrich, P.-I. et al. In situ 3D nanoprinting of free-form coupling elements for hybrid photonic integration. Nat. Photon. 12, 241247 (2018).
Google Scholar
Moughames, J. et al. Three-dimensional waveguide interconnects for scalable integration of photonic neural networks. Optica 7, 640646 (2020).
Google Scholar
MadridWolff, J. et al. Controlling light in scattering materials for volumetric additive manufacturing. Adv. Sci. 9, 2105144 (2022).
Google Scholar
Hunter, S. et al. Potentials of two-photon based 3-D optical memories for high performance computing. Appl. Opt. 29, 20582066 (1990).
Google Scholar
Li, Q. et al. Direct 3D-printing of microlens on single mode polarization-stable VCSEL chip for miniaturized optical spectroscopy. J. Opt. Microsyst. 3, 033501 (2023).
Google Scholar