RESUMEN
Modified near-ballistic uni-traveling-carrier photodiodes with improved overall performances were studied theoretically and experimentally. A bandwidth up to 0.2 THz with a 3 dB bandwidth of 136 GHz and large output power of 8.22 dBm (99 GHz) under the -2V bias voltage were obtained. The device exhibits good linearity in the photocurrent-optical power curve even at large input optical power, with a responsivity of 0.206 A/W. Physical explanations for the improved performances have been made in detail. The absorption layer and the collector layer were optimized to retain a high built-in electric field around the interface, which not only ensures the smoothness of the band structure but also facilitates the near-ballistic transmission of uni-traveling carriers. The obtained results may find potential applications in future high-speed optical communication chips and high-performance terahertz sources.
RESUMEN
Graph-based neural networks have promising perspectives but are limited by electronic bottlenecks. Our work explores the advantages of optical neural networks in the graph domain. We propose an optical graph neural network (OGNN) based on inverse-designed optical processing units (OPUs) to classify graphs with optics. The OPUs, combined with two types of optical components, can perform multiply-accumulate, matrix-vector multiplication, and matrix-matrix multiplication operations. The proposed OGNN can classify typical non-Euclidean MiniGCDataset graphs and successfully predict 1000 test graphs with 100% accuracy. The OPU-formed optical-electrical graph attention network is also scalable to handle more complex graph data, such as the Cora dataset, with 89.0% accuracy.
