In this paper, a new dual-training method for a time-delay reservoir computing (RC) system based on a single vertical-cavity surface-emitting laser (VCSEL) is proposed and demonstrated experimentally for the first time. The prediction performance of the RC system by using the dual-training method has been experimentally and numerically investigated. Here, the dual-training method is defined as performing a further RC based on the difference between the target value and the predicted value of the traditional single training. It is found that enhanced prediction performance of the RC system can be obtained by employing the dual-training method, compared to the traditional single training method. More specifically, the NMSE values of the RC system with the dual-training method applied can be improved to 760% compared with the single training method in experiments. Besides, the effects of injection power, bias currents, feedback strength, and frequency detuning are also considered. The proposed dual-training method is of great significance to the performance enhancement of the RC and has an important promotion effect on the application of the RC in the future.

Photonic neuromorphic computing has emerged as a promising approach to building a low-latency and energy-efficient non-von Neuman computing system. A photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of a PSNN remains a significant challenge. Here, we propose and fabricate a photonic spiking neuron chip based on an integrated Fabry–Perot laser with a saturable absorber (FP-SA). The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, a refractory period, inhibitory behavior and cascadability are experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we propose time-multiplexed temporal spike encoding to realize a functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons are experimentally demonstrated to realize hardware-algorithm collaborative computing, showing the capability to perform classification tasks with a supervised learning algorithm, which paves the way for a multilayer PSNN that can handle complex tasks.

We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback (DFB) laser chip with an intracavity saturable absorber (SA). The DFB laser with an intracavity SA (DFB-SA) contains a gain region and an SA region. The gain region is designed and fabricated by the asymmetric equivalent π-phase shift based on the reconstruction-equivalent-chirp technique. Under properly injected current in the gain region and reversely biased voltage in the SA region, periodic self-pulsation was experimentally observed due to the Q-switching effect. The self-pulsation frequency increases with the increase of the bias current and is within the range of several gigahertz. When the bias current is below the self-pulsation threshold, neuronlike spiking responses appear when external optical stimulus pulses are injected. Experimental results show that the spike threshold, temporal integration, and refractory period can all be observed in the fabricated DFB-SA chip. To numerically verify the experimental findings, a time-dependent coupled-wave equation model was developed, which described the physics processes inside the gain and SA regions. The numerical results agree well with the experimental measurements. We further experimentally demonstrated that the weighted sum output can readily be encoded into the self-pulsation frequency of the DFB-SA neuron. We also benchmarked the handwritten digit classification task with a simple single-layer fully connected neural network. By using the experimentally measured dependence of the self-pulsation frequency on the bias current in the gain region as an activation function, we can achieve a recognition accuracy of 92.2%, which bridges the gap between the continuous valued artificial neural networks and spike-based neuromorphic networks. To the best of our knowledge, this is the first experimental demonstration of a photonic integrated spiking neuron based on a DFB-SA, which shows great potential to realizing large-scale multiwavelength photonic spiking neural network chips.

As Moore’s law has reached its limits, it is becoming increasingly difficult for traditional computing architectures to meet the demands of continued growth in computing power. Photonic neural computing has become a promising approach to overcome the von Neuman bottleneck. However, while photonic neural networks are good at linear computing, it is difficult to achieve nonlinear computing. Here, we propose and experimentally demonstrate a coherent photonic spiking neural network consisting of Mach–Zehnder modulators (MZMs) as the synapse and an integrated quantum-well Fabry–Perot laser with a saturable absorber (FP-SA) as the photonic spiking neuron. Both linear computation and nonlinear computation are realized in the experiment. In such a coherent architecture, two presynaptic signals are modulated and weighted with two intensity modulation MZMs through the same optical carrier. The nonlinear neuron-like dynamics including temporal integration, threshold, and refractory period are successfully demonstrated. Besides, the effects of frequency detuning on the nonlinear neuron-like dynamics are also explored, and the frequency detuning condition is revealed. The proposed hardware architecture plays a foundational role in constructing a large-scale coherent photonic spiking neural network.