4.7 Article

Experimental study of neuromorphic node based on a multiwaveband emitting two-section quantum dot laser

Journal

PHOTONICS RESEARCH
Volume 9, Issue 4, Pages B87-B95

Publisher

CHINESE LASER PRESS
DOI: 10.1364/PRJ.413371

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Funding

  1. Hellenic Foundation for Research and Innovation
  2. General Secretariat for Research and Technology [2247 NEBULA]

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In this study, experimental results show that a quantum-dot laser can be tuned to operate as either a leaky integrate and fire or resonant and fire neuron by adjusting the bias. The multiband emission of quantum-dot devices enhances neurocomputational capabilities, leading to increased spike firing rate and suppressed neural spike duration. These new operation regimes, combined with thermal insensitivity and silicon cointegration, make these neuromorphic nodes a promising platform for large-scale photonic spiking neural networks.
In this work, we present experimental results concerning excitability in a multiband emitting quantum-dot-based photonic neuron. The experimental investigation revealed that the same two-section quantum dot laser can be tuned through a simple bias adjustment to operate either as a leaky integrate and fire or as a resonate and fire neuron. Furthermore, by exploiting the inherent multiband emission of quantum-dot devices revealed by the existence of multiple lasing thresholds, a significant enhancement in the neurocomputational capabilities, such as spiking duration and firing rate, is observed. Spike firing rate increased by an order of magnitude that leads to an enhancement in processing speed and, more importantly, neural spike duration was suppressed to the picosecond scale, which corresponds to a significant temporal resolution enhancement. These new regimes of operation, when combined with thermal insensitivity, silicon cointegration capability, and the fact that these multiband mechanisms are also present in miniaturized quantum-dot devices, render these neuromorphic nodes a proliferating platform for large-scale photonic spiking neural networks. (C) 2021 Chinese Laser Press

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