Low-Loss Photonic Reservoir Computing with Multimode Photonic Integrated Circuits.

Andrew Katumba, Jelle Heyvaert, Bendix Schneider, Sarah Uvin, Joni Dambre, Peter Bienstman
Author Information
  1. Andrew Katumba: Photonics Research Group, Department of Information Technology, Ghent University - imec, Ghent, Belgium. andrew.katumba@ugent.be. ORCID
  2. Jelle Heyvaert: Photonics Research Group, Department of Information Technology, Ghent University - imec, Ghent, Belgium.
  3. Bendix Schneider: Photonics Research Group, Department of Information Technology, Ghent University - imec, Ghent, Belgium.
  4. Sarah Uvin: Photonics Research Group, Department of Information Technology, Ghent University - imec, Ghent, Belgium.
  5. Joni Dambre: IDLab, Department of Electronics and Information Systems, Ghent University - imec, Ghent, Belgium.
  6. Peter Bienstman: Photonics Research Group, Department of Information Technology, Ghent University - imec, Ghent, Belgium.

Abstract

We present a numerical study of a passive integrated photonics reservoir computing platform based on multimodal Y-junctions. We propose a novel design of this junction where the level of adiabaticity is carefully tailored to capture the radiation loss in higher-order modes, while at the same time providing additional mode mixing that increases the richness of the reservoir dynamics. With this design, we report an overall average combination efficiency of 61% compared to the standard 50% for the single-mode case. We demonstrate that with this design, much more power is able to reach the distant nodes of the reservoir, leading to increased scaling prospects. We use the example of a header recognition task to confirm that such a reservoir can be used for bit-level processing tasks. The design itself is CMOS-compatible and can be fabricated through the known standard fabrication procedures.

References

  1. Neural Netw. 2007 Apr;20(3):391-403 [PMID: 17517492]
  2. Nat Commun. 2013;4:1364 [PMID: 23322052]
  3. Opt Express. 2013 Jan 14;21(1):12-20 [PMID: 23388891]
  4. Biol Cybern. 2011 Dec;105(5-6):355-70 [PMID: 22290137]
  5. Opt Lett. 2013 May 1;38(9):1422-4 [PMID: 23632505]
  6. Nat Commun. 2011 Sep 13;2:468 [PMID: 21915110]
  7. IEEE Trans Neural Netw. 2011 Sep;22(9):1469-81 [PMID: 21803686]
  8. Opt Express. 2012 Sep 24;20(20):22783-95 [PMID: 23037429]
  9. Science. 2004 Apr 2;304(5667):78-80 [PMID: 15064413]
  10. Opt Express. 2014 May 5;22(9):10868-81 [PMID: 24921786]
  11. Opt Express. 2014 Dec 15;22(25):31356-70 [PMID: 25607084]
  12. Opt Express. 2014 Apr 7;22(7):8672-86 [PMID: 24718237]
  13. Opt Express. 2012 Jan 30;20(3):3241-9 [PMID: 22330562]
  14. Sci Rep. 2012;2:287 [PMID: 22371825]
  15. Nat Commun. 2014 Mar 24;5:3541 [PMID: 24662967]
  16. Nanotechnology. 2013 Sep 27;24(38):384004 [PMID: 23999129]
  17. Neural Comput. 2002 Nov;14(11):2531-60 [PMID: 12433288]
  18. IEEE Trans Neural Netw Learn Syst. 2014 Feb;25(2):344-55 [PMID: 24807033]