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.

A Microfiber Mach-Zehnder interferometer (MMZI) based on tapered seven-core fiber (SCF) for curvature measurement has been reported and demonstrated. The tapered SCF promoted the light coupling efficiency from the single-mode fiber (SMF) to the SCF, which results in the transmission spectrum with a low loss and high extinction ratio. In the process of curvature measurement, the sensitivity can reach up to 174.02957 nm/m ⁻¹ , which is the highest curvature sensitivity in the MZI with SCF as best as we have known. Furthermore, the maximum temperature sensitivity is 0.00956 nm/°C when the temperature changes from 30 °C to 140 °C. It shows that this sensor is naturally insensitive to temperature, which has a good performance in avoiding the cross-sensitivity effect between the temperature and curvature.

We propose a novel wearable knee joint angle monitoring sensor based on the Mach-Zehnder interferometer (MZI) using a triple-clad fiber (TCF), which is fabricated by directly fusing a triple-clad fiber between two single-mode fibers (SMFs). The varying motion angle of knee joint will cause various optical path differences between the fiber core mode and the cladding modes, which will lead to the wavelength shifting of the MZI. Experimental results show that the sensor can measure the bending angles of the knee joint in the range from 40 ° to 90 °. When the knee joint angle changes from 65 ° to 90 °, the angle sensitivity can reach 569 pm/degree, and the correlation coefficient can achieve 0.98. In addition, the temperature sensitivity of the proposed sensor is 69 pm/°C in the temperature range from 40 °C to 90 °C, which shows that this sensor is hardly sensitive to temperature. Thus, the cross-sensitivity effect between the temperature and curvature can be avoided. These advantages provide a potential application prospect in the areas of sports medicine rehabilitation and robot monitoring.