Skip to main content
SpringerLink
Log in

Introductory Review on All-Optical Machine Learning Leap in Photonic Integrated Circuits

  • Published:
Optical Memory and Neural Networks Aims and scope Submit manuscript

Abstract

The human brain is the most complex circuit on the planet and the circuits inspired by the operation of the biological neuron are the most desired computing need. Artificial neural networks (ANN) are circuits that can replicate the biological neuron. Optical computing already doing wonders in integrated circuit technology and therefore the photonic implementation of neural networks is one of the most appealing technologies of the current era due to its low power consumption and high bandwidth. The ANN models are designed as per the signal processing of the human brain therefore they can be used to improve the analytic power of any system. This article reviews the advancement in optical neural networks and their application for future perspective.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.

Similar content being viewed by others

REFERENCES

  1. LeCun, Y., Bengio, Y., and Hinton, G., Deep learning, Nature, 2015, vol. 521, pp. 436–444.

    Article  Google Scholar 

  2. Silver, D. et al., Mastering the game of go with deep neural networks and tree search, Nature, 2016, vol. 529, pp. 484–489.

    Article  Google Scholar 

  3. Mnih, V. et al., Human-level control through deep reinforcement learning, Nature, 2015, vol. 518, pp. 529–533.

    Article  Google Scholar 

  4. Krizhevsky, A., Sutskever, I., and Hinton, G.E., ImageNet classification with deep convolutional neural networks, Proc. NIPS, 2012, pp. 1097–1105.

  5. Esser, S.K. et al., Convolutional networks for fast, energy efficient neuromorphic computing, Proc. Natl. Acad. Sci. U. S. A., 2016, vol. 113, pp. 11441–11446.

    Article  Google Scholar 

  6. Tait, A.N., Nahmias, M.A., Shastri, B.J., and Prucnal, P.R., Broadcast and weight: an integrated network for scalable photonic spike processing, J. Lightwave Technol., 2014, vol. 32, pp. 3427–3439.

    Article  Google Scholar 

  7. Prucnal, P.R., Shastri, B.J., de Lima, T.F., Nahmias, M.A., and Tait, A.N., Recent progress in semiconductor excitable lasers for photonic spike processing, Adv. Opt. Photonics, 2016, vol. 8, pp. 228–299.

    Article  Google Scholar 

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

    Article  Google Scholar 

  9. Paquot, Y. et al., Optoelectronic reservoir computing, Sci. Rep., 2011, vol. 2, p. 287. Harris, N.C. et al., Bosonic transport simulations in a large-scale programmable nanophotonic processor, 2015. Preprint at http://arXiv.org/abs/1507.03406.

  10. Psaltis, D. et al., Holography in artificial neural networks, Nature, 1990, vol. 343, pp. 325–330.

    Article  Google Scholar 

  11. Mead, C., Neuromorphic electronic systems, Proc. IEEE, 1990, vol. 78, pp. 1629–1636.

    Article  Google Scholar 

  12. Joannopoulos, J.D., Johnson, S.G., Winn, J.N., and Meade, R.D., Photonic Crystals: Molding the Flow of Light, Princeton: Princeton Univ. Press, 2008.

    MATH  Google Scholar 

  13. Zayats, A.V., Smolyaninov, I.I., and Maradudin, A.A., Nano-optics of surface plasmon polaritons, Phys. Rep., 2005, vol. 408, pp. 131–314.

    Article  Google Scholar 

  14. Cai, W. and Shalaev, V., Optical Metamaterials: Fundamentals and Applications, New YorkL Springer Science+Business Media, 2010.

  15. Yu, N. and Capasso, F., Flat optics with designer metasurfaces, Nat. Mater., 2014, vol. 13, pp. 139–150.

    Article  Google Scholar 

  16. Kildishev, A.V., Boltasseva, A., and Shalaev, V.M., Planar photonics with metasurfaces, Science, 2013, vol. 339, p. 1232009.

    Article  Google Scholar 

  17. Yao, K. and Liu, Y., Plasmonic metamaterials, Nanotechnol. Rev., 2014, vol. 3, pp. 177–210.

    Article  Google Scholar 

  18. Hasler, J. and Bo Marr, Finding a roadmap to achieve large neuromorphic hardware systems, Front. Neurosci., 2013, vol. 7, p. 118. https://doi.org/10.3389/fnins.2013.00118

    Article  Google Scholar 

  19. Wen, U.-P., Lan, K.-M., and Shih, H., A review of Hopheld neural networks or solving mathematical programming problems, Eur. J. Oper. Res., 2009, vol. 98, pp. 675–687.

    Article  Google Scholar 

  20. Lee, T. and Theunissen, F., A single microphone noise reduction algorithm based on the detection and reconstruction of spectro-emporal features, Proc. R. Soc. London, Ser. A, 2015, p. 471.

  21. Eliasmith, C. and Anderson, C.H., Neural Engineering Computation, Representation, and Dynamics in Neurobiological Systems, MIT Press, 2004.

    Google Scholar 

  22. Donnarumma, F., Prevete, R., def Giorgio, A., Montone, G., and Pezzulo, G., Learning programs is better than learning dynamics: A programmable neural network hierarchical architecture infamulti-task scenario, Adapt. Behav., 2016, vol. 24, pp. 27–51.

    Article  Google Scholar 

  23. Diamond, A., Nowotny, T., and Schmuker, M., Comparing neuromorphic solutions infaction: implementing a bio-inspired solution to A benchmark classification task on three parallel-computing platforms, Front. Neurosci., 2016.

  24. Zhang, K.W. et al., A fiber optic sensor for the measurement of surface roughness and displacement using artificial neural networks, IEEE Trans. Instrum. Meas., 1997, vol. 46, pp. 899–902.

    Article  Google Scholar 

  25. Khan, J. et al., Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks, Nat. Med., 2001, vol. 7, pp. 673–679.

    Article  Google Scholar 

  26. Peurifoy, J. et al., Nanophotonic particle simulation and inverse design using artificial neural networks, Sci. Adv., 2018, vol. 4, eaar4206.

  27. Markram, H., The blue brain project, Nat. Rev. Neurosci., 2006, vol. 7, pp. 153–160.

    Article  Google Scholar 

  28. Hines, M.L. and Carnevale, N.T., The NEURON simulation environment, Neural Comput., 1997, vol. 9, pp. 1179–1209.

    Article  Google Scholar 

  29. Zhang, Q., Yu, H., Barbiero, M., et al., Artificial neural networks enabled by nanophotonics, Light Sci. Appl., 2019, vol. 8, p. 42. https://doi.org/10.1038/s41377-019-0151-0

    Article  Google Scholar 

  30. Uhrig, R.E., Introduction to artificial neural networks, Proceedings of IECON 95–21st Annual Conference on IEEE Industrial Electronics (Orlando, FL), USA: IEEE, 1995, pp 33–37.

  31. McCulloch, W.S. and Pitts, W., A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophys., 1943, vol. 5, pp. 115–133.

    Article  MathSciNet  MATH  Google Scholar 

  32. Tait, A.N. et al., Neuromorphic photonic networks using silicon photonic weight banks, Sci. Rep., 2017, vol. 7, p. 7430.

    Article  Google Scholar 

  33. Shen, Y.C. et al., Deep learning with coherent nanophotonic circuits, Nat. Photonics, 2017, vol. 11, pp. 441–446.

    Article  Google Scholar 

  34. Rosenbluth, D. et al., A high performance photonic pulse processing device, Opt. Express, 2009, vol. 17, pp. 22767–22772.

    Article  Google Scholar 

  35. Li, S.H. and Cai, X.H., High-contrast all optical bistable switching in coupled nonlinear photonic crystal microcavities, Appl. Phys. Lett., 2010, vol. 96, p. 131114.

    Article  Google Scholar 

  36. Ríos, C. et al., Integrated all-photonic non-volatile multi-level memory, Nat. Photonics, 2015, vol. 9, pp. 725–732.

    Article  Google Scholar 

  37. Deng, R.R. and Liu, X.G., Optical multiplexing: Tunable lifetime nanocrystals, Nat. Photonics, 2014, vol. 8, pp. 10–12.

    Article  Google Scholar 

  38. Zijlstra, P., Chon, J.W.M., and Gu, M., Five-dimensional optical recording mediated by surface plasmons in gold nanorods, Nature, 2009, vol. 459, pp. 410–413.

    Article  Google Scholar 

  39. Li, X.P. et al., A thermally photo reduced graphene oxides for three dimensional holographic images, Nat. Commun., 2015, vol. 6, p. 6984.

    Article  Google Scholar 

  40. Ren, H.R. et al., On-chip on interference angular momentum multiplexing of broadband light, Science, 2016, vol. 352, pp. 805–809.

    Article  Google Scholar 

  41. Deng, R.R. et al., Temporal full-colour tuning through non-steady-state upconversion, Nat. Nanotechnol., 2015, vol. 10, pp. 237–242.

    Article  Google Scholar 

  42. Appeltant, L. et al., Information processing using a single dynamical node as complex system, Nat. Commun., 2011, vol. 2, p. 468.

    Article  Google Scholar 

  43. Paquot, Y., Duport, F., Smerieri, A., Dambre, J., Schrauwen, B., Haelterman, M., et al., Optoelectronic reservoir computing, Sci Rep., 2012, vol. 2, p. 287.

    Article  Google Scholar 

  44. Duport, F., Schneider, B., Smerieri, A., Haelterman, M., and Massar, S., All-optical reservoir computing, Opt. Express, 2012, vol. 20, no. 20, pp. 22783–22795. https://doi.org/10.1364/OE.20.022783

    Article  Google Scholar 

  45. Zhang, H., Feng, X., Li, B., Wang, Y., Cui, K., Liu, F., et al., Integrated photonic reservoir computing based on hierarchical time multiplexing structure, Opt. Express, 2014, vol. 22, no. 25, pp. 31356–31370. https://doi.org/10.1364/OE.22.031356

    Article  Google Scholar 

  46. Nguimdo, R.M., Verschaffelt, G., Danckaert, J., and der Sande, G.V., Simultaneous computation of two independent tasks using reservoir computing based on a single photonic nonlinear node with optical feedback, IEEE Trans. Neurol. Network Learn. Syst., 2015, vol. 26, no. 12, pp. 3301–3307. https://doi.org/10.1109/TNNLS.2015.2404346

    Article  MathSciNet  Google Scholar 

  47. Cheng, T.-Y., Chou, D.-Y., Liu, C.-C., Chang, Y.-J., and Chen, C.-C., Optical neural networks based on optical fiber-communication, Neurocomputing, 2019, vol. 364, pp. 239–244. https://doi.org/10.1016/j.neucom.2019.07.051

    Article  Google Scholar 

  48. Zang, Y., Chen, M., Yang, S., and Chen, H., Electro-optical neural networks based on time-stretch method, IEEE J. Sel. Top. Quantum Electron., 2020, vol. 26, no. 1, pp. 1–10. https://doi.org/10.1109/JSTQE.2019.2957446

    Article  Google Scholar 

  49. Froemke, R.C. and Dan, Y., Spike-timing-dependent synaptic induced by natural spike trains, Nature, 2002, vol. 416, pp. 433–438.

    Article  Google Scholar 

  50. Cheng, Z.G. et al., On-chip photonic synapse, Sci. Adv., 2017, vol. 3, e1700160.

    Article  Google Scholar 

  51. Feldmann, J., Youngblood, N., Wright, C.D., et al., All-optical spiking neurosynaptic networks with self-learning capabilities, Nature, 2019, vol. 569, pp. 208–214.

    Article  Google Scholar 

  52. Clements, W.R., Humphreys, P.C., Metcalf, B.J., et al., Optimal design for universal multiport interferometers, Optica, 2016, vol. 3, pp. 1460–1465.

    Article  Google Scholar 

  53. Ribeiro, A., Ruocco, A., Vanacker, L., et al., Demonstration of a 4×4-port universal linear circuit, Optica, 2016, vol. 3, pp. 1348–1357.

    Article  Google Scholar 

  54. Shen, Y.C., Harris, N.C., Skirlo, S., et al., Deep learning with coherent nanophotonic circuits, Nat. Photon, 2017, vol. 11, pp. 441–446.

    Article  Google Scholar 

  55. Hughes, T.W., Minkov, M., Shi, Y., et al., Training of photonic neural networks through in situ backpropagation and gradient measurement, Optica, 2018, vol. 5, pp. 864–871.

    Article  Google Scholar 

  56. Chiles, J., Buckley, S.M., Nam, S.W., et al., Design, fabrication, and metrology of 10× 100 multi-planar integrated photonic routing manifolds for neural networks, APL Photonics, 2018, vol. 3, p. 106101.

    Article  Google Scholar 

  57. Tait, A.N., Wu, A.X., de Lima, T.F., et al., Microring weight banks, IEEE J. Sel. Top. Quantum Electron, 2016, vol. 22, pp. 312–325.

    Article  Google Scholar 

  58. Nahmias, M.A., Shastri, B.J., Tait, A.N., and Prucnal, P.R., A leaky integrate-and-fire laser neuron for ultrafast cognitive computing, IEEE J. Sel. Topics Quantum Electron, 2013, vol. 19, no. 5, pp. 1–12.

    Article  Google Scholar 

  59. Shen, Y.C., Harris, N.C., Skirlo, S., et al., Deep learning with coherent nanophotonic circuits, Nat. Photonics, 2017, vol. 11, pp. 441–446.

    Article  Google Scholar 

  60. Reck, M., Zeilinger, A., Bernstein, H.J., et al., Experimental realization of any discrete unitary operator, Phys. Rev. Lett., 1994, vol. 73, pp. 58–61.

    Article  Google Scholar 

  61. Ying Zuo, Bohan Li, Yujun Zhao, Yue Jiang, You-Chiuan Chen, Peng Chen, Gyu-Boong Jo, Junwei Liu, and Shengwang Du, All-optical neural network with nonlinear activation functions, Optica, 2019, vol. 6, pp. 1132–1137.

    Article  Google Scholar 

  62. Miller, D.A.B., Attojoule optoelectronics for low-energy information processing and communications, J. Lightwave Technol., 2017, vol. 35, pp. 346–396.

    Article  Google Scholar 

  63. Zhu, Y.X., Zhang, F., Yang, F., et al., Toward single lane 200G optical interconnects with silicon photonic modulator, J. Lightwave Technol., 2019, vol. 38, pp. 67–74.

    Article  Google Scholar 

  64. Chang, L., Xie, W.Q., Shu, H.W., et al., Ultra-efficient frequency comb generation in algaas-on-insulator microresonators, 2019. ArXiv: 1909.09778.

  65. Ankur Saharia, Ravi Kumar Maddila, Jalil Ali, Preecha Yupapin, and Ghanshyam Singh, An elementary optical logic circuit for quantum computing: A review, Opt. Quantum Electron., 2019, issue 7.

  66. Fang, M.Y.-S., Sasikanth Manipatruni, Wierzynski, C., Khosrowshahi, A., and DeWeese, M.R., Design of optical neural networks with component imprecisions, Opt. Express, 2019, vol. 27, pp. 14009–14029.

    Article  Google Scholar 

  67. Miller, D.A.B., Attojoule optoelectronics for low-energy information processing and communications, J. Lightwave Technol., 2017, vol. 35, pp. 346–396.

    Article  Google Scholar 

  68. Zhu, Y.X., Zhang, F., Yang, F., et al., Toward single lane 200G optical interconnects with silicon photonic modulator, J. Lightwave Technol., 2019, vol. 38, pp. 67–74.

    Article  Google Scholar 

  69. Chang, L., Xie, W.Q., Shu, H.W., et al., Ultra-efficient frequency comb generation in algaas-on-insulator microresonators, 2019. ArXiv: 1909.09778.

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Manipal University Jaipur, 303007, Rajasthan, India

    Ankur Saharia & Manish Tiwari

  2. Department of Electronics and Communication Engineering, 302017, Jaipur, MNITJ, India

    Kamalkishor Choure, Ravi Kumar Maddila & Ghanshyam Singh

  3. Department of Electronics and Communication Engineering, Poornima College of Engineering, 302022, Jaipur, India

    Nitesh Mudgal

Authors
  1. Ankur Saharia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamalkishor Choure
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nitesh Mudgal
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ravi Kumar Maddila
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Manish Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ghanshyam Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ankur Saharia.

Ethics declarations

The authors declare that they have no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ankur Saharia, Choure, K., Mudgal, N. et al. Introductory Review on All-Optical Machine Learning Leap in Photonic Integrated Circuits. Opt. Mem. Neural Networks 31, 393–402 (2022). https://doi.org/10.3103/S1060992X22040075

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S1060992X22040075

Keywords:

Search

Navigation