Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Photonics for artificial intelligence and neuromorphic computing


Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Implementations of weights or photonic synapses.
Fig. 2: Photonic neurons incorporating weighting and nonlinearity.
Fig. 3: Excitable lasers and resonators for spiking.
Fig. 4: Photonic neural network implementations.
Fig. 5: Neuromorphic photonic processor architecture.


  1. 1.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    ADS  Google Scholar 

  2. 2.

    Wu, Y. et al. Google’s neural machine translation system: bridging the gap between human and machine translation. Preprint at (2016).

  3. 3.

    Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018).

    ADS  Google Scholar 

  4. 4.

    Merolla, P. A. et al. A million spiking-neuron integrated circuit with a scalable communication network and interface. Science 345, 668–673 (2014).

    ADS  Google Scholar 

  5. 5.

    Davies, M. et al. Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38, 82–99 (2018).

    Google Scholar 

  6. 6.

    Keyes, R. W. Optical logic-in the light of computer technology. Opt. Acta 32, 525–535 (1985).

    ADS  Google Scholar 

  7. 7.

    Prucnal, P. R. & Shastri, B. J. Neuromorphic Photonics (CRC, 2017).

  8. 8.

    Magesan, E., Gambetta, J. M., Corcoles, A. D. & Chow, J. M. Machine learning for discriminating quantum measurement trajectories and improving readout. Phys. Rev. Lett. 114, 200501 (2015).

    ADS  Google Scholar 

  9. 9.

    Radovic, A. et al. Machine learning at the energy and intensity frontiers of particle physics. Nature 560, 41–48 (2018).

    ADS  Google Scholar 

  10. 10.

    Duarte, J. et al. Fast inference of deep neural networks in FPGAs for particle physics. J. Instrum. 13, P07027 (2018).

    Google Scholar 

  11. 11.

    Kates-Harbeck, J., Svyatkovskiy, A. & Tang, W. Predicting disruptive instabilities in controlled fusion plasmas through deep learning. Nature 568, 526–531 (2019).

    ADS  Google Scholar 

  12. 12.

    Ferreira de Lima, T. et al. Machine learning with neuromorphic photonics. J. Lightwave Technol. 37, 1515–1534 (2019).

    ADS  Google Scholar 

  13. 13.

    Han, J., Jentzen, A. & Weinan, E. Solving high-dimensional partial differential equations using deep learning. Proc. Natl Acad. Sci. USA 115, 8505–8510 (2018).

    MathSciNet  MATH  Google Scholar 

  14. 14.

    Shen, Y. et al. Deep learning with coherent nanophotonic circuits. Nat. Photon. 11, 441–446 (2017).

    ADS  Google Scholar 

  15. 15.

    Jouppi, N. P. et al. In-datacenter performance analysis of a tensor processing unit. In Proc. 44th Annual International Symposium on Computer Architecture 1–12 (Association for Computing Machinery, 2017).

  16. 16.

    Hughes, T. W., Minkov, M., Shi, Y. & Fan, S. Training of photonic neural networks through in situ backpropagation and gradient measurement. Optica 5, 864–871 (2018).

    ADS  Google Scholar 

  17. 17.

    Tait, A. N. et al. Demonstration of multivariate photonics: blind dimensionality reduction with integrated photonics. J. Lightwave Technol. 37, 5996–6006 (2019).

    ADS  Google Scholar 

  18. 18.

    Huang, C. et al. Demonstration of photonic neural network for fiber nonlinearity compensation in long-haul transmission systems. In Optical Fiber Communication Conference Th4C–6 (Optical Society of America, 2020).

  19. 19.

    Zhang, S. et al. Field and lab experimental demonstration of nonlinear impairment compensation using neural networks. Nat. Commun. 10, 3033 (2019).

    ADS  Google Scholar 

  20. 20.

    Kravtsov, K. S., Fok, M. P., Prucnal, P. R. & Rosenbluth, D. Ultrafast all-optical implementation of a leaky integrate-and-fire neuron. Opt. Express 19, 2133–2147 (2011).

    ADS  Google Scholar 

  21. 21.

    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).

    Google Scholar 

  22. 22.

    Shainline, J. M., Buckley, S. M., Mirin, R. P. & Nam, S. W. Superconducting optoelectronic circuits for neuromorphic computing. Phys. Rev. Appl. 7, 034013 (2017).

    ADS  Google Scholar 

  23. 23.

    Bangari, V. et al. Digital electronics and analog photonics for convolutional neural networks (DEAP-CNNs). IEEE J. Sel. Top. Quantum Electron. 26, 7701213 (2020).

    Google Scholar 

  24. 24.

    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).

    ADS  Google Scholar 

  25. 25.

    Goodman, J. W., Dias, A. R. & Woody, L. M. Fully parallel, high-speed incoherent optical method for performing discrete Fourier transforms. Opt. Lett. 2, 1–3 (1978).

    ADS  Google Scholar 

  26. 26.

    Goodman, J. W., Leonberger, F. J., Kung, S.-Y. & Athale, R. A. Optical interconnections for VLSI systems. Proc. IEEE 72, 850–866 (1984).

    ADS  Google Scholar 

  27. 27.

    Miller, D. A. B. Rationale and challenges for optical interconnects to electronic chips. Proc. IEEE 88, 728–749 (2000).

    Google Scholar 

  28. 28.

    Psaltis, D. & Farhat, N. Optical information processing based on an associative-memory model of neural nets with thresholding and feedback. Opt. Lett. 10, 98–100 (1985).

    ADS  Google Scholar 

  29. 29.

    Soref, R. & Bennett, B. Electrooptical effects in silicon. IEEE J. Quantum Electron. 23, 123–129 (1987).

    ADS  Google Scholar 

  30. 30.

    Tait, A. N. et al. Neuromorphic photonic networks using silicon photonic weight banks. Sci. Rep. 7, 7430 (2017).

    ADS  Google Scholar 

  31. 31.

    Bogaerts, W. & Chrostowski, L. Silicon photonics circuit design: methods, tools and challenges. Laser Photon. Rev. 12, 1700237 (2018).

    ADS  Google Scholar 

  32. 32.

    Nozaki, K. et al. Femtofarad optoelectronic integration demonstrating energy-saving signal conversion and nonlinear functions. Nat. Photon. 13, 454–459 (2019).

    ADS  Google Scholar 

  33. 33.

    Nahmias, M. A. et al. Photonic multiply-accumulate operations for neural networks. IEEE J. Sel. Top. Quantum Electron. 26, 7701518 (2020).

    Google Scholar 

  34. 34.

    Ríos, C. et al. In-memory computing on a photonic platform. Sci. Adv. 5, eaau5759 (2019).

    ADS  Google Scholar 

  35. 35.

    Ríos, C. et al. Integrated all-photonic non-volatile multi-level memory. Nat. Photon. 9, 725–732 (2015).

    ADS  Google Scholar 

  36. 36.

    Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998).

    Google Scholar 

  37. 37.

    Tait, A., Ferreira de Lima, T., Nahmias, M., Shastri, B. & Prucnal, P. Continuous calibration of microring weights for analog optical networks. Photon. Technol. Lett. 28, 887–890 (2016).

    ADS  Google Scholar 

  38. 38.

    Tait, A. N. et al. Microring weight banks. IEEE J. Sel. Top. Quantum Electron. 22, 312–325 (2016).

    ADS  Google Scholar 

  39. 39.

    Shi, B., Calabretta, N. & Stabile, R. Deep neural network through an InP SOA-based photonic integrated cross-connect. IEEE J. Sel. Top. Quantum Electron. 26, 7701111 (2020).

    Google Scholar 

  40. 40.

    Xu, X. et al. Photonic perceptron based on a Kerr microcomb for high-speed, scalable, optical neural networks. Laser Photon. Rev. 14, 2000070 (2020).

    ADS  Google Scholar 

  41. 41.

    Reck, M., Zeilinger, A., Bernstein, H. J. & Bertani, P. Experimental realization of any discrete unitary operator. Phys. Rev. Lett. 73, 58–61 (1994).

    ADS  Google Scholar 

  42. 42.

    Carolan, J. et al. Universal linear optics. Science 349, 711–716 (2015).

    MathSciNet  MATH  Google Scholar 

  43. 43.

    Shainline, J. M. et al. Superconducting optoelectronic loop neurons. J. Appl. Phys. 126, 044902 (2019).

    ADS  Google Scholar 

  44. 44.

    Chiles, J., Buckley, S. M., Nam, S. W., Mirin, R. P. & Shainline, J. M. Design, fabrication, and metrology of 10 × 100 multi-planar integrated photonic routing manifolds for neural networks. APL Photon. 3, 106101 (2018).

    ADS  Google Scholar 

  45. 45.

    Buckley, S. et al. All-silicon light-emitting diodes waveguide-integrated with superconducting single-photon detectors. Appl. Phys. Lett. 111, 141101 (2017).

    ADS  Google Scholar 

  46. 46.

    Harris, N. C. et al. Efficient, compact and low loss thermo-optic phase shifter in silicon. Opt. Express 22, 10487–10493 (2014).

    ADS  Google Scholar 

  47. 47.

    Jayatilleka, H. et al. Wavelength tuning and stabilization of microring-based filters using silicon in-resonator photoconductive heaters. Opt. Express 23, 25084–25097 (2015).

    ADS  Google Scholar 

  48. 48.

    Tait, A. N. et al. Feedback control for microring weight banks. Opt. Express 26, 26422–26443 (2018).

    ADS  Google Scholar 

  49. 49.

    Patel, D. et al. Design, analysis, and transmission system performance of a 41 GHz silicon photonic modulator. Opt. Express 23, 14263 (2015).

    ADS  Google Scholar 

  50. 50.

    Komljenovic, T. et al. Heterogeneous silicon photonic integrated circuits. J. Lightwave Technol. 34, 20–35 (2016).

    ADS  Google Scholar 

  51. 51.

    He, M. et al. High-performance hybrid silicon and lithium niobate Mach–Zehnder modulators for 100 Gbit s−1 and beyond. Nat. Photon. 13, 359–364 (2019).

    ADS  Google Scholar 

  52. 52.

    Sorianello, V. et al. Graphene–silicon phase modulators with gigahertz bandwidth. Nat. Photon. 12, 40–44 (2018).

    ADS  Google Scholar 

  53. 53.

    Gholipour, B. et al. Amorphous metal-sulphide microfibers enable photonic synapses for brain-like computing. Adv. Opt. Mater. 3, 635–641 (2015).

    MathSciNet  Google Scholar 

  54. 54.

    Goodman, J. W. Fan-in and fan-out with optical interconnections. Opt. Acta 32, 1489–1496 (1985).

    ADS  MathSciNet  Google Scholar 

  55. 55.

    Nahmias, M. A., Shastri, B. J., Tait, A. N. & Prucnal, P. R. A leaky integrate-and-fire laser neuron for ultrafast cognitive computing. IEEE J. Sel. Top. Quantum Electron. 19, 1800212 (2013).

    Google Scholar 

  56. 56.

    Romeira, B. et al. Excitability and optical pulse generation in semiconductor lasers driven by resonant tunneling diode photo-detectors. Opt. Express 21, 20931–20940 (2013).

    ADS  Google Scholar 

  57. 57.

    Tait, A. N. et al. Silicon photonic modulator neuron. Phys. Rev. Appl. 11, 064043 (2019).

    ADS  Google Scholar 

  58. 58.

    Amin, R. et al. ITO-based electro-absorption modulator for photonic neural activation function. APL Mater. 7, 081112 (2019).

    ADS  Google Scholar 

  59. 59.

    George, J. K. et al. Neuromorphic photonics with electro-absorption modulators. Opt. Express 27, 5181–5191 (2019).

    ADS  Google Scholar 

  60. 60.

    Nahmias, M. A. et al. An integrated analog O/E/O link for multi-channel laser neurons. Appl. Phys. Lett. 108, 151106 (2016).

    ADS  Google Scholar 

  61. 61.

    Williamson, I. A. D. et al. Reprogrammable electro-optic nonlinear activation functions for optical neural networks. IEEE J. Sel. Top. Quantum Electron. 26, 7700412 (2020).

    Google Scholar 

  62. 62.

    McCaughan, A. N. et al. A superconducting thermal switch with ultrahigh impedance for interfacing superconductors to semiconductors. Nat. Electron. 2, 451–456 (2019).

    Google Scholar 

  63. 63.

    Mourgias-Alexandris, G. et al. An all-optical neuron with sigmoid activation function. Opt. Express 27, 9620–9630 (2019).

    ADS  Google Scholar 

  64. 64.

    Hill, M., Frietman, E. E. E., de Waardt, H., Khoe, G.-D. & Dorren, H. All fiber-optic neural network using coupled SOA based ring lasers. IEEE Trans. Neural Netw. 13, 1504–1513 (2002).

    Google Scholar 

  65. 65.

    Rosenbluth, D., Kravtsov, K., Fok, M. P. & Prucnal, P. R. A high performance photonic pulse processing device. Opt. Express 17, 22767–22772 (2009).

    ADS  Google Scholar 

  66. 66.

    Sebastian, A. et al. Tutorial: brain-inspired computing using phase-change memory devices. J. Appl. Phys. 124, 111101 (2018).

    ADS  Google Scholar 

  67. 67.

    Selmi, F. et al. Relative refractory period in an excitable semiconductor laser. Phys. Rev. Lett. 112, 183902 (2014).

    ADS  Google Scholar 

  68. 68.

    Peng, H. T. et al. Neuromorphic photonic integrated circuits. IEEE J. Sel. Top. Quant. Electron. 24, 6101715 (2018).

    Google Scholar 

  69. 69.

    Romeira, B., Avo, R., Figueiredo, J. M. L., Barland, S. & Javaloyes, J. Regenerative memory in time-delayed neuromorphic photonic resonators. Sci. Rep. 6, 19510 (2016).

    ADS  Google Scholar 

  70. 70.

    Shastri, B. J. et al. Spike processing with a graphene excitable laser. Sci. Rep. 6, 19126 (2016).

    ADS  Google Scholar 

  71. 71.

    Coomans, W., Gelens, L., Beri, S., Danckaert, J. & Van der Sande, G. Solitary and coupled semiconductor ring lasers as optical spiking neurons. Phys. Rev. E 84, 036209 (2011).

    ADS  Google Scholar 

  72. 72.

    Brunstein, M. et al. Excitability and self-pulsing in a photonic crystal nanocavity. Phys. Rev. A 85, 031803 (2012).

    ADS  Google Scholar 

  73. 73.

    Robertson, J., Deng, T., Javaloyes, J. & Hurtado, A. Controlled inhibition of spiking dynamics in VCSELS for neuromorphic photonics: theory and experiments. Opt. Lett. 42, 1560–1563 (2017).

    ADS  Google Scholar 

  74. 74.

    Hopfield, J. J. Neural networks and physical systems with emergent collective computational abilities. Proc. Natl Acad. Sci. USA 79, 2554–2558 (1982).

    ADS  MathSciNet  MATH  Google Scholar 

  75. 75.

    Stewart, T. C. & Eliasmith, C. Large-scale synthesis of functional spiking neural circuits. Proc. IEEE 102, 881–898 (2014).

    Google Scholar 

  76. 76.

    Bueno, J. et al. Reinforcement learning in a large-scale photonic recurrent neural network. Optica 5, 756–760 (2018).

    ADS  Google Scholar 

  77. 77.

    Lin, X. et al. All-optical machine learning using diffractive deep neural networks. Science 361, 1004–1008 (2018).

    ADS  MathSciNet  MATH  Google Scholar 

  78. 78.

    Zuo, Y. et al. All-optical neural network with nonlinear activation functions. Optica 6, 1132–1137 (2019).

    ADS  Google Scholar 

  79. 79.

    Xu, S., Wang, J., Wang, R., Chen, J. & Zou, W. High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays. Opt. Express 27, 19778–19787 (2019).

    ADS  Google Scholar 

  80. 80.

    Mehrabian, A., Miscuglio, M., Alkabani, Y., Sorger, V. J. & El-Ghazawi, T. A Winograd-based integrated photonics accelerator for convolutional neural networks. IEEE J. Sel. Top. Quantum Electron. 26, 6100312 (2020).

    Google Scholar 

  81. 81.

    McMahon, P. L. et al. A fully programmable 100-spin coherent Ising machine with all-to-all connections. Science 354, 614–617 (2016).

    ADS  Google Scholar 

  82. 82.

    Roques-Carmes, C. et al. Heuristic recurrent algorithms for photonic Ising machines. Nat. Commun. 11, 249 (2020).

    ADS  Google Scholar 

  83. 83.

    Tang, P. T. P., Lin, T.-H. & Davies, M. Sparse coding by spiking neural networks: convergence theory and computational results. Preprint at (2017).

  84. 84.

    Davies, M. Benchmarks for progress in neuromorphic computing. Nat. Mach. Intell. 1, 386–388 (2019).

    Google Scholar 

  85. 85.

    Maass, W. Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10, 1659–1671 (1997).

    Google Scholar 

  86. 86.

    Peng, H. et al. Temporal information processing with an integrated laser neuron. IEEE J. Sel. Top. Quantum Electron. 26, 5100209 (2020).

    Google Scholar 

  87. 87.

    Robertson, J., Hejda, M., Bueno, J. & Hurtado, A. Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons. Sci. Rep. 10, 6098 (2020).

    ADS  Google Scholar 

  88. 88.

    Chakraborty, I., Saha, G., Sengupta, A. & Roy, K. Toward fast neural computing using all-photonic phase change spiking neurons. Sci. Rep. 8, 12980 (2018).

    ADS  Google Scholar 

  89. 89.

    Fok, M. P., Tian, Y., Rosenbluth, D. & Prucnal, P. R. Pulse lead/lag timing detection for adaptive feedback and control based on optical spike-timing-dependent plasticity. Opt. Lett. 38, 419–421 (2013).

    ADS  Google Scholar 

  90. 90.

    Toole, R. et al. Photonic implementation of spike-timing-dependent plasticity and learning algorithms of biological neural systems. J. Lightwave Technol. 34, 470–476 (2016).

    ADS  Google Scholar 

  91. 91.

    Xiang, S. et al. STDP-based unsupervised spike pattern learning in a photonic spiking neural network with VCSELs and VCSOAs. IEEE J. Sel.Top. Quantum Electron. 25, 1700109 (2019).

    Google Scholar 

  92. 92.

    Larger, L. et al. Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing. Opt. Express 20, 3241–3249 (2012).

    ADS  Google Scholar 

  93. 93.

    Paquot, Y. et al. Optoelectronic reservoir computing. Sci. Rep. 2, 287 (2012).

    Google Scholar 

  94. 94.

    Duport, F., Schneider, B., Smerieri, A., Haelterman, M. & Massar, S. All-optical reservoir computing. Opt. Express 20, 22783–22795 (2012).

    ADS  Google Scholar 

  95. 95.

    Brunner, D., Soriano, M. C., Mirasso, C. R. & Fischer, I. Parallel photonic information processing at gigabyte per second data rates using transient states. Nat. Commun. 4, 1364 (2013).

    ADS  Google Scholar 

  96. 96.

    Vandoorne, K. et al. Experimental demonstration of reservoir computing on a silicon photonics chip. Nat. Commun. 5, 3541 (2014).

    ADS  Google Scholar 

  97. 97.

    Brunner, D. et al. Tutorial: Photonic neural networks in delay systems. J. Appl. Phys. 124, 152004 (2018).

    ADS  Google Scholar 

  98. 98.

    Brunner, D., Soriano, M. C. & der Sande, G. V. Photonic Reservoir Computing (De Gruyter, 2019).

  99. 99.

    Antonik, P., Marsal, N., Brunner, D. & Rontani, D. Human action recognition with a large-scale brain-inspired photonic computer. Nat. Mach. Intell. 1, 530–537 (2019).

    Google Scholar 

  100. 100.

    Sun, C. et al. Single-chip microprocessor that communicates directly using light. Nature 528, 534–538 (2015).

    ADS  Google Scholar 

  101. 101.

    Stojanovic, V. et al. Monolithic silicon-photonic platforms in state-of-the-art CMOS SOI processes [Invited]. Opt. Express 26, 13106 (2018).

    ADS  Google Scholar 

  102. 102.

    Jha, A. et al. Lateral bipolar junction transistor on a silicon photonics platform. Opt. Express 28, 11692–11704 (2020).

    ADS  Google Scholar 

  103. 103.

    Giewont, K. et al. 300-mm monolithic silicon photonics foundry technology. IEEE J. Sel. Top. Quantum Electron. 25, 8200611 (2019).

    Google Scholar 

  104. 104.

    Zhou, Z., Yin, B. & Michel, J. On-chip light sources for silicon photonics. Light Sci. Appl. 4, e358 (2015).

    ADS  Google Scholar 

  105. 105.

    Song, B., Stagarescu, C., Ristic, S., Behfar, A. & Klamkin, J. 3d integrated hybrid silicon laser. Opt. Express 24, 10435–10444 (2020).

    ADS  Google Scholar 

  106. 106.

    Mack, M. et al. Luxtera’s silicon photonics platform for transceiver manufacturing. In 2014 International Conference on Solid State Devices and Materials 506–507 (Luxtera, Inc., 2014).

  107. 107.

    Billah, M. R. et al. Hybrid integration of silicon photonics circuits and inp lasers by photonic wire bonding. Optica 5, 876–883 (2018).

    ADS  Google Scholar 

  108. 108.

    Liang, D. & Bowers, J. E. Recent progress in lasers on silicon. Nat. Photon. 4, 511–517 (2010).

    ADS  Google Scholar 

  109. 109.

    Chen, S. et al. Electrically pumped continuous-wave III–V quantum dot lasers on silicon. Nat. Photon. 10, 307–311 (2016).

    ADS  Google Scholar 

  110. 110.

    Berggren, K. et al. Roadmap on emerging hardware and technology for machine learning. Nanotechnology 32, 012002 (2021).

    ADS  Google Scholar 

  111. 111.

    Ambrogio, S. et al. Equivalent-accuracy accelerated neural-network training using analogue memory. Nature 558, 60–67 (2018).

    ADS  Google Scholar 

  112. 112.

    Li, C. et al. Long short-term memory networks in memristor crossbar arrays. Nat. Mach. Intell. 1, 49–57 (2019).

    Google Scholar 

  113. 113.

    Sze, V., Chen, Y., Yang, T. & Emer, J. S. Efficient processing of deep neural networks: a tutorial and survey. Proc. IEEE 105, 2295–2329 (2017).

    Google Scholar 

  114. 114.

    Cheng, Z. et al. Device-level photonic memories and logic applications using phase-change materials. Adv. Mater. 30, 1802435 (2018).

    Google Scholar 

  115. 115.

    Zhang, Y. et al. Broadband transparent optical phase change materials for high-performance nonvolatile photonics. Nat. Commun. 10, 4279 (2019).

    ADS  Google Scholar 

  116. 116.

    Cheng, Z., Ríos, C., Pernice, W. H. P., Wright, C. D. & Bhaskaran, H. On-chip photonic synapse. Sci. Adv. 3, e1700160 (2017).

    ADS  Google Scholar 

  117. 117.

    Bogaerts, W. et al. Silicon microring resonators. Laser Photon. Rev. 6, 47–73 (2012).

    ADS  Google Scholar 

  118. 118.

    Schrauwen, J., Van Thourhout, D. & Baets, R. Trimming of silicon ring resonator by electron beam induced compaction and strain. Opt. Express 16, 3738 (2008).

    ADS  Google Scholar 

  119. 119.

    Prorok, S., Petrov, A. Y., Eich, M., Luo, J. & Jen, A. K.-Y. Trimming of high-Q-factor silicon ring resonators by electron beam bleaching. Opt. Lett. 37, 3114 (2012).

    ADS  Google Scholar 

  120. 120.

    Milosevic, M. M. et al. Ion implantation in silicon for trimming the operating wavelength of ring resonators. IEEE J. Sel. Top. Quantum Electron. 24, 8200107 (2018).

    Google Scholar 

  121. 121.

    Harris, N. C. et al. Linear programmable nanophotonic processors. Optica 5, 1623–1631 (2018).

    ADS  Google Scholar 

  122. 122.

    Perez, D., Gasulla, I., Mahapatra, P. D. & Capmany, J. Principles, fundamentals, and applications of programmable integrated photonics. Adv. Opt. Photon. 12, 709–786 (2020).

    Google Scholar 

  123. 123.

    Gaeta, A. L., Lipson, M. & Kippenberg, T. J. Photonic-chip-based frequency combs. Nat. Photon. 13, 158–169 (2019).

    ADS  Google Scholar 

  124. 124.

    Kippenberg, T. J., Holzwarth, R. & Diddams, S. A. Microresonator-based optical frequency combs. Science 332, 555–559 (2011).

    ADS  Google Scholar 

  125. 125.

    Del’Haye, P. et al. Optical frequency comb generation from a monolithic microresonator. Nature 450, 1214–1217 (2007).

    ADS  Google Scholar 

  126. 126.

    Turner, E. H. High-frequency electro-optic coefficients of lithium niobate. Appl. Phys. Lett. 8, 303–304 (1966).

    ADS  Google Scholar 

  127. 127.

    Wang, C., Zhang, M., Stern, B., Lipson, M. & Loncar, M. Nanophotonic lithium niobate electro-optic modulators. Opt. Express 26, 1547–1555 (2018).

    ADS  Google Scholar 

  128. 128.

    Mercante, A. J. et al. 110 GHz CMOS compatible thin film LiNbO3 modulator on silicon. Opt. Express 24, 15590–15595 (2016).

    ADS  Google Scholar 

  129. 129.

    Wang, C. et al. Integrated lithium niobate electro-optic modulators operating at CMOS-compatible voltages. Nature 562, 101–104 (2018).

    ADS  Google Scholar 

  130. 130.

    Sun, J. et al. A 128 Gb/s PAM4 silicon microring modulator with integrated thermo-optic resonance tuning. J. Lightwave Technol. 37, 110–115 (2019).

    ADS  Google Scholar 

  131. 131.

    Patel, D., Samani, A., Veerasubramanian, V., Ghosh, S. & Plant, D. V. Silicon photonic segmented modulator-based electro-optic dac for 100 gb/s pam-4 generation. IEEE Photon. Technol. Lett. 27, 2433–2436 (2015).

    ADS  Google Scholar 

  132. 132.

    Meng, J., Miscuglio, M., George, J. K., Babakhani, A. & Sorger, V. J. Electronic bottleneck suppression in next-generation networks with integrated photonic digital-to-analog converters. Adv. Photon. Res. (2021).

  133. 133.

    Gelens, L et al. Excitability in semiconductor microring lasers: experimental and theoretical pulse characterization. Phys. Rev. A 82, 063841 (2010).

    ADS  Google Scholar 

  134. 134.

    Beri, S. et al. Excitability in optical systems close to Z2-symmetry. Phys. Lett. A 374, 739–743 (2010).

    ADS  MATH  Google Scholar 

  135. 135.

    Bogaerts, W. & Rahim, A. Programmable photonics: an opportunity for an accessible large-volume PIC ecosystem. IEEE J. Sel. Top. Quantum Electron. 26, 8302517 (2020).

    Google Scholar 

Download references


B.J.S. acknowledges support from the Natural Sciences and Engineering Research Council of Canada (NSERC). T.F.d.L. and P.R.P. acknowledge support from the Office of Naval Research (ONR), Defense Advanced Research Projects Agency (DARPA) and National Science Foundation (NSF). We thank J. Shainline, P. Kuo and N. Sanford for editorial contributions.

Author information



Corresponding authors

Correspondence to Bhavin J. Shastri or Alexander N. Tait.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Photonics thanks Yichen Shen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Shastri, B.J., Tait, A.N., Ferreira de Lima, T. et al. Photonics for artificial intelligence and neuromorphic computing. Nat. Photonics 15, 102–114 (2021).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing