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Neuromorphic photonic computing has emerged as a competitive computing paradigm to overcome the bottlenecks of the von-Neumann architecture. Linear weighting and nonlinear spiking activation are two fundamental functions of a photonic spiking neural network (PSNN). However, they are separately implemented with different photonic materials and devices, hindering the large-scale integration of PSNN. Here, we propose, fabricate and experimentally demonstrate a photonic neuro-synaptic chip enabling the simultaneous implementation of linear weighting and nonlinear spiking activation based on a distributed feedback (DFB) laser with a saturable absorber (DFB-SA). A prototypical system is experimentally constructed to demonstrate the parallel weighted function and nonlinear spike activation. Furthermore, a four-channel DFB-SA array is fabricated for realizing matrix convolution of a spiking convolutional neural network, achieving a recognition accuracy of 87% for the MNIST dataset. The fabricated neuro-synaptic chip offers a fundamental building block to construct the large-scale integrated PSNN chip.

Fast scanning-speed and low-power-consumption are becoming progressively more and more important in realizing high-performance chiplet optical phased arrays (OPAs). Here, we establish an integrated OPA based on thin-film lithium niobate-on-insulator (LNOI) platform to access these outstanding performances. Significantly, a lithium niobate (LN) OPA chip is implemented by 32/48 channels LN waveguides enabled by electro-optic modulations, which showcases the low power consumption (1.11nJ/{\pi}}) and fast operation speed (14.4 ns) promising the advantage of the LNOI platform for integrated OPAs. As results, we experimentally achieved a beam steering with a 62.2{\deg}*8.8{\deg} field of view (FOV) and a beam divergence of 2.4{\deg}*1.2{\deg}. Moreover, by employing sparse aperiodic arrays in waveguides design we obtained a significant reduction of lateral divergence to 0.33{\deg} for the radiation beam. This work demonstrate that remarkable advantage of LNOI platform for power-saving and scalable OPA chips for various applications., Comment: 22 pages,5 figures,conferences added

Photonic neuromorphic computing has emerged as a promising avenue toward building a low-latency and energy-efficient non-von-Neuman computing system. Photonic spiking neural network (PSNN) exploits brain-like spatiotemporal processing to realize high-performance neuromorphic computing. However, the nonlinear computation of PSNN remains a significant challenging. Here, we proposed and fabricated a photonic spiking neuron chip based on an integrated Fabry-P\'erot laser with a saturable absorber (FP-SA) for the first time. The nonlinear neuron-like dynamics including temporal integration, threshold and spike generation, refractory period, and cascadability were experimentally demonstrated, which offers an indispensable fundamental building block to construct the PSNN hardware. Furthermore, we proposed time-multiplexed spike encoding to realize functional PSNN far beyond the hardware integration scale limit. PSNNs with single/cascaded photonic spiking neurons were experimentally demonstrated to realize hardware-algorithm collaborative computing, showing capability in performing classification tasks with supervised learning algorithm, which paves the way for multi-layer PSNN for solving complex tasks., Comment: 10 pages, 8 figures

A comprehensive investigation of the signal-to-noise ratio (SNR) of the microwave photonic filter (MPF) with an interferometric structure based on an incoherent broadband optical source (IBOS) is presented from the time and frequency domains, respectively. The interferometric structure and the IBOS in the MPF result in a beneficial single-bandpass radiofrequency (RF) response. However, the IBOS adds a relatively large noise to the filtered RF signal. Therefore, the analysis of the SNR is of great importance for the design of this kind of MPFs. Theoretical analysis shows that the SNR is a function of the center frequency of the passband, the modulation index, the chromatic dispersion, and the shape of the IBOS. An experiment is performed to verify the theory, and experimental results agree well with the theoretical calculation.

Hybrid free space optical (FSO)/radio frequency (RF) systems have been proved to be reliable links for high-data-rate wireless backhauls. In this paper, we present a unified performance analysis of the hybrid FSO/RF transmission system which transmits the identical data in both links and implements two popular diversity combining schemes, namely, selection combining (SC) and maximal ratio combining (MRC), in the receiver. Specially, for the FSO link, the Gamma-Gamma turbulence with pointing errors under heterodyne detection (HD) and intensity modulation/direction detection (IM/DD) is considered in our analysis while the general \k{appa}-{\mu} shadowed fading which unifies popular RF fading models is employed for the analysis of the RF link. As a result, unified closed-form expressions of outage probabilities and average bit error rates for different modulation schemes are derived. Analytical and Monte Carlo simulation results are provided to characterize the performance of the hybrid FSO/RF link which is compared to the single FSO link and the single RF link. The agreement between the analytical and simulation results confirms the unification of various FSO channels and RF fading scenarios into a single closed-form expression.

We report a 48-channel 100-GHz tunable laser near 1550 nm by integrating 16 DFB lasers. High wavelength-spacing uniformity is guaranteed by the reconstruction-equivalent-chirp technique, which enables a temperature tuning range below 20 Celsius degree.