Skip to main content
SpringerLink
Log in

Towards an intelligent photonic system

  • Review
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

The emerging intelligence technologies represented by deep learning have broadened their applications to various fields. Beyond the conventional electronics-based processing systems, the convergence of photonics and artificial intelligence (AI) technology enhances the performance and learning ability of AI. In this review, we propose the concept of an intelligent photonic system (IPS), illustrating it as a developing architecture with three different versions. For each version of IPS, we review several representative studies. Moreover we discuss the challenges towards an IPS and provide some prospects for the future development.

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

Similar content being viewed by others

References

  1. Kikuchi K. Fundamentals of coherent optical fiber communications. J Lightw Technol, 2016, 34: 157–179

    Article  Google Scholar 

  2. Yao J P. Microwave photonics. J Lightw Technol, 2009, 27: 314–335

    Article  Google Scholar 

  3. Liang J Y, Wang L V. Single-shot ultrafast optical imaging. Optica, 2018, 5: 1113–1127

    Article  Google Scholar 

  4. Chen J H, Li D R, Xu F. Optical microfiber sensors: sensing mechanisms, and recent advances. J Lightw Technol, 2019, 37: 2577–2589

    Article  Google Scholar 

  5. Capmany J, Novak D. Microwave photonics combines two worlds. Nat Photon, 2007, 1: 319–330

    Article  Google Scholar 

  6. Sun C, Wade M T, Lee Y, et al. Single-chip microprocessor that communicates directly using light. Nature, 2015, 528: 534–538

    Article  Google Scholar 

  7. Khan M H, Shen H, Xuan Y, et al. Ultrabroad-bandwidth arbitrary radiofrequency waveform generation with a silicon photonic chip-based spectral shaper. Nat Photon, 2010, 4: 117–122

    Article  Google Scholar 

  8. Zhuang L M, Roeloffzen C G H, Hoekman M, et al. Programmable photonic signal processor chip for radiofrequency applications. Optica, 2015, 2: 854–859

    Article  Google Scholar 

  9. Miller D A B. Self-configuring universal linear optical component. Photon Res, 2013, 1: 1–15

    Article  Google Scholar 

  10. Pérez D, Gasulla I, Crudgington L, et al. Multipurpose silicon photonics signal processor core. Nat Commun, 2017, 8: 636

    Article  Google Scholar 

  11. Perez D, Gasulla I, Capmany J. Toward programmable microwave photonics processors. J Lightw Technol, 2018, 36: 519–532

    Article  Google Scholar 

  12. Zhang J J, Yao J P. A microwave photonic signal processor for arbitrary microwave waveform generation and pulse compression. J Lightw Technol, 2016, 34: 5610–5615

    Article  Google Scholar 

  13. García-Meca C, Lechago S, Brimont A, et al. On-chip wireless silicon photonics: from reconfigurable interconnects to lab-on-chip devices. Light Sci Appl, 2017, 6: e17053

    Article  Google Scholar 

  14. Silver D, Huang A, Maddison C J, et al. Mastering the game of Go with deep neural networks and tree search. Nature, 2016, 529: 484–489

    Article  Google Scholar 

  15. Silver D, Schrittwieser J, Simonyan K, et al. Mastering the game of Go without human knowledge. Nature, 2017, 550: 354–359

    Article  Google Scholar 

  16. Topol E J. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 2019, 25: 44–56

    Article  Google Scholar 

  17. Abdel-Hamid O, Mohamed A, Jiang H, et al. Convolutional neural networks for speech recognition. IEEE/ACM Trans Audio Speech Lang Process, 2014, 22: 1533–1545

    Article  Google Scholar 

  18. Brown N, Sandholm T. Superhuman AI for multiplayer poker. Science, 2019, 365: 885–890

    Article  MathSciNet  MATH  Google Scholar 

  19. Winfield A. Ethical standards in robotics and AI. Nat Electron, 2019, 2: 46–48

    Article  Google Scholar 

  20. Wang J G, Zhou L B. Traffic light recognition with high dynamic range imaging and deep learning. IEEE Trans Intell Transp Syst, 2019, 20: 1341–1352

    Article  Google Scholar 

  21. Minasian R A. Photonic signal processing of microwave signals. IEEE Trans Microw Theor Techn, 2006, 54: 832–846

    Article  Google Scholar 

  22. Miller D A B. Perfect optics with imperfect components. Optica, 2015, 2: 747–750

    Article  Google Scholar 

  23. Yang G, Zou W W, Yu L, et al. Compensation of multi-channel mismatches in high-speed high-resolution photonic analog-to-digital converter. Opt Express, 2016, 24: 24061–24074

    Article  Google Scholar 

  24. Minzioni P, Alberti F, Schiffini A. Techniques for nonlinearity cancellation into embedded links by optical phase conjugation. J Lightw Technol, 2005, 23: 2364–2370

    Article  Google Scholar 

  25. Park S W, Park J Y, Bong K, et al. An energy-efficient and scalable deep learning/inference processor with tetra-parallel MIMD architecture for big data applications. IEEE Trans Biomed Circ Syst, 2015, 9: 838–848

    Google Scholar 

  26. Waldrop M M. The chips are down for Moore's law. Nature, 2016, 530: 144–147

    Article  Google Scholar 

  27. Tait A N, de Lima T F, Nahmias M A, et al. Silicon photonic modulator neuron. Phys Rev Appl, 2019, 11: 064043

    Article  Google Scholar 

  28. Denéve S, Alemi A, Bourdoukan R. The brain as an efficient and robust adaptive learner. Neuron, 2017, 94: 969–977

    Article  Google Scholar 

  29. Roy K, Jaiswal A, Panda P. Towards spike-based machine intelligence with neuromorphic computing. Nature, 2019, 575: 607–617

    Article  Google Scholar 

  30. Maass W, Natschläger T, Markram H. Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput, 2002, 14: 2531–2560

    Article  MATH  Google Scholar 

  31. Ma W, Zidan M A, Lu W D. Neuromorphic computing with memristive devices. Sci China Inf Sci, 2018, 61: 060422

    Article  Google Scholar 

  32. Wu N J. Neuromorphic vision chips. Sci China Inf Sci, 2018, 61: 060421

    Article  Google Scholar 

  33. Yan B N, Chen Y R, Li H. Challenges of memristor based neuromorphic computing system. Sci China Inf Sci, 2018, 61: 060425

    Article  Google Scholar 

  34. Cully A, Clune J, Tarapore D, et al. Robots that can adapt like animals. Nature, 2015, 521: 503–507

    Article  Google Scholar 

  35. Barbastathis G, Ozcan A, Situ G. On the use of deep learning for computational imaging. Optica, 2019, 6: 921–943

    Article  Google Scholar 

  36. Rivenson Y, Göröcs Z, Günaydin H, et al. Deep learning microscopy. Optica, 2017, 4: 1437–1443

    Article  Google Scholar 

  37. Sinha A, Lee J, Li S, et al. Lensless computational imaging through deep learning. Optica, 2017, 4: 1117–1125

    Article  Google Scholar 

  38. Wu Y C, Rivenson Y, Zhang Y B, et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica, 2018, 5: 704–710

    Article  Google Scholar 

  39. Zhang X Y, Chen Y F, Ning K F, et al. Deep learning optical-sectioning method. Opt Express, 2018, 26: 30762–30772

    Article  Google Scholar 

  40. Manifold B, Thomas E, Francis A T, et al. Denoising of stimulated Raman scattering microscopy images via deep learning. Biomed Opt Express, 2019, 10: 3860–3874

    Article  Google Scholar 

  41. Esman D J, Ataie V, Kuo B P, et al. Comb-assisted cyclostationary analysis of wideband RF signals. J Lightw Technol, 2017, 35: 3705–3712

    Article  Google Scholar 

  42. Ma M, Adams R, Chen L R. Integrated photonic chip enabled simultaneous multichannel wideband radio frequency spectrum analyzer. J Lightw Technol, 2017, 35: 2622–2628

    Article  Google Scholar 

  43. Fortier T, Baumann E. 20 years of developments in optical frequency comb technology and applications. Commun Phys, 2019, 2: 153

    Article  Google Scholar 

  44. Hammond A M, Camacho R M. Designing integrated photonic devices using artificial neural networks. Opt Express, 2019, 27: 29620–29638

    Article  Google Scholar 

  45. Malkiel I, Mrejen M, Nagler A, et al. Plasmonic nanostructure design and characterization via Deep Learning. Light Sci Appl, 2018, 7: 60

    Article  Google Scholar 

  46. Laporte F, Dambre J, Bienstman P. Highly parallel simulation and optimization of photonic circuits in time and frequency domain based on the deep-learning framework PyTorch. Sci Rep, 2019, 9: 5918

    Article  Google Scholar 

  47. Zahavy T, Dikopoltsev A, Moss D, et al. Deep learning reconstruction of ultrashort pulses. Optica, 2018, 5: 666–673

    Article  Google Scholar 

  48. Xu S F, Zou X T, Ma B W, et al. Deep-learning-powered photonic analog-to-digital conversion. Light Sci Appl, 2019, 8: 66

    Article  Google Scholar 

  49. Zou X T, Xu S F, Li S J, et al. Optimization of the Brillouin instantaneous frequency measurement using convolutional neural networks. Opt Lett, 2019, 44: 5723–5726

    Article  Google Scholar 

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

    Article  Google Scholar 

  51. Lin X, Rivenson Y, Yardimci N T, et al. All-optical machine learning using diffractive deep neural networks. Science, 2018, 361: 1004–1008

    Article  MathSciNet  MATH  Google Scholar 

  52. Hamerly R, Bernstein L, Sludds A, et al. Large-scale optical neural networks based on photoelectric multiplication. Phys Rev X, 2019, 9: 021032

    Google Scholar 

  53. Bangari V, Marquez B A, Miller H, et al. Digital electronics and analog photonics for convolutional neural networks (DEAP-CNNs). IEEE J Sel Top Quantum Electron, 2020, 26: 1–13

    Article  Google Scholar 

  54. Williamson I A D, Hughes T W, Minkov M, et al. Reprogrammable electro-optic nonlinear activation functions for optical neural networks. IEEE J Sel Top Quantum Electron, 2020, 26: 1–12

    Article  Google Scholar 

  55. George J K, Mehrabian A, Amin R, et al. Neuromorphic photonics with electro-absorption modulators. Opt Express, 2019, 27: 5181–5191

    Article  Google Scholar 

  56. Zuo Y, Li B H, Zhao Y J, et al. All-optical neural network with nonlinear activation functions. Optica, 2019, 6: 1132–1137

    Article  Google Scholar 

  57. Mourgias-Alexandris G, Tsakyridis A, Passalis N, et al. An all-optical neuron with sigmoid activation function. Opt Express, 2019, 27: 9620–9630

    Article  Google Scholar 

  58. Miscuglio M, Mehrabian A, Hu Z B, et al. All-optical nonlinear activation function for photonic neural networks. Opt Mater Express, 2018, 8: 3851–3863

    Article  Google Scholar 

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

    Article  Google Scholar 

  60. Xu S F, Wang J, Wang R, et al. High-accuracy optical convolution unit architecture for convolutional neural networks by cascaded acousto-optical modulator arrays. Opt Express, 2019, 27: 19778

    Article  Google Scholar 

  61. Xu S F, Wang J, Zou W W. High-energy-efficiency integrated photonic convolutional neural networks. ArXiv:1910.12635

  62. Prucnal P R, Shastri B J. Neuromorphic Photonics. Boca Raton: CRC Press, 2017

    Book  Google Scholar 

  63. Nahmias M A, Shastri B J, Tait A N, et al. A leaky integrate-and-fire laser neuron for ultrafast cognitive computing. IEEE J Sel Top Quantum Electron, 2013, 19: 1–12

    Article  Google Scholar 

  64. Robertson J, Wade E, Kopp Y, et al. Toward neuromorphic photonic networks of ultrafast spiking laser neurons. IEEE J Sel Top Quantum Electron, 2020, 26: 1–15

    Article  Google Scholar 

  65. Xiang S Y, Zhang H, Guo X X, et al. Cascadable neuron-like spiking dynamics in coupled VCSELs subject to orthogonally polarized optical pulse injection. IEEE J Sel Top Quantum Electron, 2017, 23: 1–7

    Article  Google Scholar 

  66. Prucnal P R, Shastri B J, de Lima T F, et al. Recent progress in semiconductor excitable lasers for photonic spike processing. Adv Opt Photon, 2016, 8: 228–299

    Article  Google Scholar 

  67. Chakraborty I, Saha G, Roy K. Photonic in-memory computing primitive for spiking neural networks using phase-change materials. Phys Rev Appl, 2019, 11: 014063

    Article  Google Scholar 

  68. Xiang S Y, Ren Z X, Zhang Y H, et al. All-optical neuromorphic XOR operation with inhibitory dynamics of a single photonic spiking neuron based on a VCSEL-SA. Opt Lett, 2020, 45: 1104–1107

    Article  Google Scholar 

  69. Cheng Z G, Ríos C, Pernice W H P, et al. On-chip photonic synapse. Sci Adv, 2017, 3: e1700160

    Article  Google Scholar 

  70. Tait A N, Nahmias M A, Shastri B J, et al. Broadcast and weight: an integrated network for scalable photonic spike processing. J Lightw Technol, 2014, 32: 4029–4041

    Article  Google Scholar 

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

    Article  Google Scholar 

  72. Xiang S Y, Zhang Y L, Gong J K, et al. STDP-based unsupervised spike pattern learning in a photonic spiking neural network with VCSELs and VCSOAs. IEEE J Sel Top Quantum Electron, 2019, 25: 1–9

    Article  Google Scholar 

  73. Ren Q S, Zhang Y L, Wang R, et al. Optical spike-timing-dependent plasticity with weight-dependent learning window and reward modulation. Opt Express, 2015, 23: 25247–25258

    Article  Google Scholar 

  74. Toole R, Tait A N, de Lima T F, et al. Photonic implementation of spike-timing-dependent plasticity and learning algorithms of biological neural systems. J Lightw Technol, 2016, 34: 470–476

    Article  Google Scholar 

  75. Fok M P, Tian Y, Rosenbluth D, et al. Pulse lead/lag timing detection for adaptive feedback and control based on optical spike-timing-dependent plasticity. Opt Lett, 2013, 38: 419–421

    Article  Google Scholar 

  76. Ma B W, Chen J P, Zou W W. A DFB-LD-based photonic neuromorphic network for spatiotemporal pattern recognition. In: Proceedings of Optical Fiber Communication Conference, San Diego, 2020. M2K.2

    Google Scholar 

  77. Smit M, Leijtens X. Integration of passive and active components in InP-Based PICs In: Proceedings of Advances in Optical Sciences Congress, Honolulu, 2009. ITuB2

    Google Scholar 

  78. van Emmerik C I, Dijkstra M, de Goede M, et al. Single-layer active-passive Al2O3 photonic integration platform. Opt Mater Express, 2018, 8: 3049–3054

    Article  Google Scholar 

  79. de Valicourt G, Chang C M, Eggleston M S, et al. Photonic integrated circuit based on hybrid III-V/silicon integration. J Lightw Technol, 2018, 36: 265–273

    Article  Google Scholar 

  80. Yoo S J B, Guan B B, Scott R P. Heterogeneous 2D/3D photonic integrated microsystems. Microsyst Nanoeng, 2016, 2: 16030

    Article  Google Scholar 

  81. Hill M, Smit M, Crombez P, et al. Digital vs. Analog photonic integration In: Proceedings of Integrated Photonics and Nanophotonics Research and Applications, Boston, 2008. IWC1

    Google Scholar 

  82. Atabaki A H, Moazeni S, Pavanello F, et al. Integrating photonics with silicon nanoelectronics for the next generation of systems on a chip. Nature, 2018, 556: 349–354

    Article  Google Scholar 

  83. Sengupta K, Nagatsuma T, Mittleman D M. Terahertz integrated electronic and hybrid electronic-photonic systems. Nat Electron, 2018, 1: 622–635

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Key R&D Program of China (Grant No. 2019YFB2203700) and National Natural Science Foundation of China (Grant No. 61822508).

Author information

Authors and Affiliations

  1. State Key Laboratory of Advanced Optical Communication Systems and Networks, Intelligent Microwave Lightwave Integration Innovation Center (iMLic), Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Weiwen Zou, Bowen Ma, Shaofu Xu & Xiuting Zou

  2. State Key Laboratory of Advanced Optical Communication Systems and Networks, School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China

    Xingjun Wang

Authors
  1. Weiwen Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bowen Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shaofu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiuting Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xingjun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Weiwen Zou.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zou, W., Ma, B., Xu, S. et al. Towards an intelligent photonic system. Sci. China Inf. Sci. 63, 160401 (2020). https://doi.org/10.1007/s11432-020-2863-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-020-2863-y

Keywords

Search

Navigation