We propose an approach to generate neuron-like spikes of vertical-cavity surface-emitting laser (VCSEL) by multi-frequency switching. A stable temporal spiking sequence has been realized both by numerical simulations and experiments with a pulse width of sub-nanosecond, which is 8 orders of magnitude faster than ones from biological neurons. Moreover, a controllable spiking coding scheme using multi-frequency switching is designed and a sequence with 20 symbols is generated at the speed of up to 1 Gbps by experiment. Furthermore, we investigate the factors related to time delay of spiking generation, including injection strength and frequency detuning. With proper manipulation of detuning frequency, the spiking generation delay can be controlled upto 60 ns, which is 6 times longer than the delay controlled by intensity. The multi-frequency switching provides another manipulation dimension for spiking generation and will be helpful to exploit the abundant spatial-temporal features of spiking neural network. We believe the proposed VCSEL-neuron, as a single physical device for generating spiking signals with variable time delay, will pave the way for future photonic spiking neural networks.
In this Letter, we propose an optical delay-weight spiking neural network (SNN) architecture constructed by cascaded frequency and intensity-switched vertical-cavity surface emitting lasers (VCSELs). The synaptic delay plasticity of frequency-switched VCSELs is deeply studied by numerical analysis and simulations. The principal factors related to the delay manipulation are investigated with the tunable spiking delay up to 60 ns. Moreover, a two-layer spiking neural network based on the delay-weight supervised learning algorithm is applied to a spiking sequence pattern training task and then a classification task of the Iris dataset. The proposed optical SNN provides a compact and cost-efficient solution for delay weighted computing architecture without considerations of extra programmable optical delay lines.
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