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Optics Express

Optics Express

  • Editor: Andrew M. Weiner
  • Vol. 22, Iss. 7 — Apr. 7, 2014
  • pp: 8672–8686

Fast photonic information processing using semiconductor lasers with delayed optical feedback: Role of phase dynamics

Romain Modeste Nguimdo, Guy Verschaffelt, Jan Danckaert, and Guy Van der Sande  »View Author Affiliations

Optics Express, Vol. 22, Issue 7, pp. 8672-8686 (2014)

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Semiconductor lasers subject to delayed optical feedback have recently shown great potential in solving computationally hard tasks. By optically implementing a neuro-inspired computational scheme, called reservoir computing, based on the transient response to optical data injection, high processing speeds have been demonstrated. While previous efforts have focused on signal bandwidths limited by the semiconductor laser’s relaxation oscillation frequency, we demonstrate numerically that the much faster phase response makes significantly higher processing speeds attainable. Moreover, this also leads to shorter external cavity lengths facilitating future on-chip implementations. We numerically benchmark our system on a chaotic time-series prediction task considering two different feedback configurations. The results show that a prediction error below 4% can be obtained when the data is processed at 0.25 GSamples/s. In addition, our insight into the phase dynamics of optical injection in a semiconductor laser also provides a clear understanding of the system performance at different pump current levels, even below solitary laser threshold. Considering spontaneous emission noise and noise in the readout layer, we obtain good prediction performance at fast processing speeds for realistic values of the noise strength.

© 2014 Optical Society of America

OCIS Codes
(140.5960) Lasers and laser optics : Semiconductor lasers
(190.3100) Nonlinear optics : Instabilities and chaos
(200.3050) Optics in computing : Information processing
(250.4745) Optoelectronics : Optical processing devices

ToC Category:
Optics in Computing

Original Manuscript: February 7, 2014
Revised Manuscript: March 24, 2014
Manuscript Accepted: March 24, 2014
Published: April 3, 2014

Romain Modeste Nguimdo, Guy Verschaffelt, Jan Danckaert, and Guy Van der Sande, "Fast photonic information processing using semiconductor lasers with delayed optical feedback: Role of phase dynamics," Opt. Express 22, 8672-8686 (2014)

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  1. J. P. Crutchfield, L. D. William, S. Sudeshna, “Introduction to focus issue:intrinsic and designed computation: information processing in dynamical systems-beyond the digital hegemony,” Chaos 20, 037101 (2010). [CrossRef]
  2. D. Woods, T. J. Naughton, “Optical computing: photonic neural networks,” Nat. Phys. 8257–259 (2012). [CrossRef]
  3. W. Maass, T. Natschläger, H. Markram, “Real-time computing without stable states: a new framework for neural computation based on perturbations,” Neural Comput. 14, 2531–2560 (2002). [CrossRef] [PubMed]
  4. H. Jaeger, H. Haas, “Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication,” Science 304, 78–80 (2004). [CrossRef] [PubMed]
  5. D. Verstraeten, B. Schrauwen, M. D’Haene, D. Stroobandt, “An experimental unification of reservoir computing methods,” Neural Networks 20, 391–403 (2007). [CrossRef] [PubMed]
  6. J. J. Steil, “Backpropagation-decorrelation: Online recurrent learning with O(N) complexity,” In Proceedings of IJCNN ’04’ 1, 843–848 (2004).
  7. H. J. Caulfield, S. Dolev, “Why future supercomputing requires optics,” Nat. Photon. 4, 261–263 (2010). [CrossRef]
  8. K. Vandoorne, W. Dierckx, B. Schrauwen, D. Verstraeten, R. Baets, P. Bienstman, J. Campenhout, “Towards optical signal processing using photonic reservoir computing,” Opt. Express 16, 11182–11192 (2008). [CrossRef] [PubMed]
  9. L. Appeltant, M. C. Soriano, G. Van der Sande, J. Danckaert, S. Massar, J. Dambre, B. Schrauwen, C. R. Mirasso, I. Fischer, “Information processing using a single dynamical node as complex system,” Nat. Commun. 2, 468–472 (2011). [CrossRef] [PubMed]
  10. L. Larger, M. C. Soriano, D. Brunner, L. Appeltant, J. M. Gutierrez, L. Pesquera, C. R. Mirasso, I. Fischer, “Photonic information processing beyond Turing: an optoelectronic implementation of reservoir computing,” Opt. Express 20, 3241–3249 (2012). [CrossRef] [PubMed]
  11. Y. Paquot, F. Duport, A. Smerieri, J. Dambre, B. Schrauwen, M. Haelterman, S. Massar, “Optoelectronic reservoir computing,” Sci. Rep. 2, 287 (2012). [CrossRef] [PubMed]
  12. R. Martinenghi, S. Rybalko, M. Jacquot, Y. K. Chembo, L. Larger, “Photonic nonlinear transient computing with multiple-delay wavelength dynamics,” Phys. Rev Lett. 108, 244101 (2012). [CrossRef] [PubMed]
  13. F. Duport, B. Schneider, A. Smerieri, M. Haelterman, Serge Massar, “All Optical Reservoir Computing” Optics Express 20, 22783–22795 (2012). [CrossRef]
  14. A. Smerieri, F. Duport, M. Haelterman, S. Massar, “Analog readout for optical reservoir computers,” in Advances in Neural Information Processing Systems, Vol. 25, P. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, K.Q. Weinberger, eds. (MIT Press, 2012), pp. 953–961.
  15. D. Brunner, M. C. Soriano, C. R. Mirasso, I. Fischer, “Parallel photonic information processing at gigabyte per second data rates using transient states,” Nature Commun. 4, 1364 (2013). [CrossRef]
  16. K. Hicke, M. A. Escalona-Moran, D. Brunner, M. C. Soriano, I. Fischer, C. R. Mirasso, “Information processing using transient dynamics of semiconductor lasers subject to delayed feedback,” IEEE J. Sel. Top. Quantum Electron. 19, 1501610 (2013). [CrossRef]
  17. R. Lang, K. Kobayashi, “External Optical Feedback Effects on Semiconductor Injection Laser Properties,” IEEE J. Quantum Electron. 16, 347–355 (1980) [CrossRef]
  18. T. Heil, A. Uchida, P. Davis, T. Aida, “TE-TM dynamics in a semiconductor laser subject to polarization-rotated feedback,” Phys. Rev. A 68, 033811 (2003). [CrossRef]
  19. M. C. Soriano, J. García-Ojalvo, C. R. Mirasso, I. Fischer, “Complex photonics: dynamics and applications of delay-coupled semiconductors lasers,” Rev. Mod. Phys. 85, 421–470 (2013). [CrossRef]
  20. R. M. Nguimdo, G. Verschaffelt, J. Danckaert, X. Leijtens, J. Bolk, G. Van der Sande, “Fast random bit generation based on a single chaotic semiconductor ring laser,” Opt. Express 20, 28603–28613 (2012) [CrossRef] [PubMed]
  21. S. Xiang, W. Pan, B. Luo, L. S. Yan, X. H. Zou, N. Jiang, L. Yang, H. N. Zhu, “Unpredictability-enhanced chaotic vertical-cavity surface-emitting lasers with variable-polarization optical feedback,” J. Lightw. Technol. 292173–2179 (2011). [CrossRef]
  22. R. M. Nguimdo, M. C. Soriano, P. Colet, “Role of the phase in the identification of delay time in semiconductor lasers with optical feedback,” Opt. Lett. 36, 4332–4334 (2011). [CrossRef] [PubMed]
  23. D. Rontani, A. Locquet, M. Sciamanna, D. S. Citrin, S. Ortin, “Time-Delay Identification in a Chaotic Semiconductor Laser With Optical Feedback: A Dynamical Point of View,” IEEE J. Quantum Electron. 45, 879–891 (2009). [CrossRef]
  24. R. M. Nguimdo, G. Verschaffelt, J. Danckaert, G. Van der Sande, “Loss of time-delay signature in chaotic semiconductor ring lasers,” Opt. Lett. 37, 2541–2544 (2012). [CrossRef] [PubMed]
  25. S. Wieczorek, B. Krauskopf, T. B. Simpson, D. Lenstra, “The dynamical complexity of optically injected semiconductor lasers,” Phys. Rep. 4161 (2005). [CrossRef]
  26. A. S. Weigend, N. A. Gershenfeld, “Time series prediction: Forecasting the future and understanding the past,” ftp://ftp.santafe.edu/pub/Time-Series/Competition (1993).
  27. M. C. Soriano, S. Ortín, D. Brunner, L. Larger, C. R. Mirasso, I. Fischer, L. Pesquera, “Optoelectronic reservoir computing: Tackling noise-induced performance degradation,” Opt. Express 21, 12–20 (2013). [CrossRef] [PubMed]
  28. L. Appeltant, G. Van der Sande, J. Danckaert, I. Fischer, ”Constructing optimized binary masks for reservoir computing with delay systems,” Sci. Rep. 4, 3629 (2014). [CrossRef] [PubMed]
  29. M. C. Soriano, S. Ortín, L. Keuninckx, L. Appeltant, J. Danckaert, L. Pesquera, G. Van der Sande, ”Delay-based Reservoir Computing: noise effects in a combined analog and digital implementation,” accepted to IEEE Trans. Neural Netw. Learn. Syst. (2014).

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