Silicon carbide (SiC) polytypes are emerging for integrated nonlinear and quantum photonics due to their wide-bandgap energies, second-order optic nonlinearity and process compatibility with complementary metal-oxide-semiconductor technologies. Among polytypes, 3C-SiC is the only one epitaxially grown on wafer-scale silicon substrates. However, on-chip nonlinear and quantum light sources leveraging the second-order nonlinearity of 3C-SiC have not been reported to our knowledge. Here, we design and fabricate an elliptical microring on 3C-SiC. We demonstrate a nonlinear light source with a second-harmonic generation efficiency of $$17.4\pm 0.2 \% {W}^{-1}$$17.4 ± 0.2 % W − 1 and difference-frequency generation with a signal-idler bandwidth of 97 nm. We demonstrate a spontaneous parametric down-conversion source with a photon-pair generation rate of 4.8 MHz and a coincidence-to-accidental ratio of $$3361\pm 84$$3361 ± 84 . We measure a low heralded single-photon second-order coherence $${g}_{H}^{\left(2\right)}=0.0007$$g H 2 = 0.0007 . We observe time-bin entanglement with a visibility of $$86.0\pm 2.4 \%$$86.0 ± 2.4 % using this source. Our work paves a way toward SiC-based on-chip nonlinear and quantum photonic circuits.

In recent years, cognitive radar (CR) with waveform diversity has exhibited significant performance improvements over the traditional fixed waveform radar and become an area of vigorous research and development. This study presents a dynamic waveform selection algorithm to strive for tracking error minimisation for CR manoeuvering target tracking in clutter. Based on the concepts of resolution cell and measurement extraction cell, the statistical characteristics of radar measurements are discussed without dependence upon the Cramér‐Rao lower bound of the measurement errors and the high signal‐to‐noise ratio assumption. A particle filter combined with probabilistic data association is used as a tracker. To quantify the utility of available waveforms, the predicted tracking mean‐square error, because of its dependence on actual future measurements, is approximated efficiently via Gaussian fitting of the prior density of the target state and statistical linearisation of the measurement equation. Monte Carlo simulation results show that the proposed dynamic waveform selection algorithm can improve tracking performance considerably, especially in terms of track loss probability.

Silicon carbide (SiC) electronics has seen a rapid development in industry over the last two decades due to its capabilities in handling high powers and high temperatures while offering a high saturated carrier mobility for power electronics applications. With the increased capacity in producing large-size, single-crystalline SiC wafers, it has recently been attracting attention from academia and industry to exploit SiC for integrated photonics owing to its large bandgap energy, wide transparent window, and moderate second-order optical nonlinearity, which is absent in other centrosymmetric silicon-based material platforms. SiC with various polytypes exhibiting second- and third-order optical nonlinearities are promising for implementing nonlinear and quantum light sources in photonic integrated circuits. By optimizing the fabrication processes of the silicon carbide-on-insulator platforms, researchers have exploited the resulting high-quality-factor microring resonators for various nonlinear frequency conversions and spontaneous parametric down-conversion in photonic integrated circuits. In this paper, we review the fundamentals and applications of SiC-based microring resonators, including the material and optical properties, the device design for nonlinear and quantum light sources, the device fabrication processes, and nascent applications in integrated nonlinear and quantum photonics.

With the explosive growth of data, tensor processing has emerged as a pivotal component of the next generation of artificial intelligence (AI) algorithms. Current photonic convolutional processors transform tensor convolutions into multi-channel general matrix multiplication (GeMM), which follows the path of electronic counterparts, leading to data replication and hardware complexity. In this study, we experimentally and theoretically demonstrate a photonic tensor processing unit (PTPU) with a single modulator, which offers a more concise approach for multi-channel standard tensor convolution processing, different from the channel-wise convolution method. By executing multi-tensor parallel computing instead of multi-channel parallel computing, PTPU can directly produce feature tensors without clock synchronization and delay compensation between multiple channels and allows more release of physical hardware resources. Furthermore, an integrated array of semiconductor optical amplifiers (SOAs) are used to be photonic synapses for programmable weight bank, demonstrating record-high precision of 9.2 bits for weights. In the proof-of-concept experiment, we extracted features from a 3-channel (RGB) image in horizontal and vertical directions, using integrated multi-wavelength photonic tensor kernels. We then built a 3D convolutional neural network to predict the presence of COVID-19 based on computer tomography (CT) scan data consisting of 64-channel tensors.

Knowledge graph embedding methods, which transform high-dimensional and complex graph contents into low-dimensional vectors (or matrices, tensors) representations. They are useful for a wide range of graph analysis tasks including link prediction, node classification, recommendation and visualization. Most existing approaches represent graph nodes as vectors in a low-dimensional embedding space, ignoring the semantic hierarchy and the uncertainty present in the real-world graphs. To address this challenge, in this paper, we propose GHAKE, Gaussian Hierarchy Knowledge Graph Embedding, a novel graph embedding method that preserves both semantic hierarchy and node uncertain effectively and efficiently in an end-to-end manner. GHAKE effectively models uncertainty through Gaussian embeddings, and models semantic hierarchy via mapping entities into the polar coordinate system. In our comprehensive experiments, we evaluate GHAKE on real-world graphs, and the results demonstrate that can effectively model the semantic hierarchies and the uncertain of entities in knowledge graphs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the link prediction task.

Current photonic convolutional processors transform tensor convolutions into multi-channel general matrix multiplication (GeMM), leading to data replication and hardware complexity. In this study, we propose and experimentally demonstrate a photonic tensor processing unit (PTPU) with single dataflow, which offers a more concise approach to multi-channel standard tensor convolution processing. In experiment, we extracted features from a 3-channel (RGB) image in horizontal and vertical directions using an integrated multi-wavelength source. We then built a $3\mathrm{D}$ -convolutional neural network to predict the presence of COVID-19 based on computer tomography (CT) scan data consisting of 64-channel tensors.