Search

Diamond is renowned as the ultimate semiconductor thanks to its exceptional physical properties, including unmatched hardness, exceptional wear resistance, superior mechanical and tribological characteristics, and high fracture strength. Diamond solution-gate field-effect transistors (D-SGFETs) leverage these advantages, along with their outstanding high-power and high-frequency performance, excellent thermal conductivity, wide bandgap, high carrier mobility, and rapid saturation speed. These features make D-SGFETs highly promising for fast and precise biomedical detection applications. This paper provides a comprehensive review of the fabrication techniques for diamond SGFETs, encompassing diamond film synthesis, surface conduction layer formation, source/drain fabrication, and FET packaging. Furthermore, the study delves into the surface functionalization of diamond SGFETs and their diverse applications in biomedical detection. Finally, the paper discusses the future outlook of diamond SGFETs in advancing biomedical detection technologies. [ABSTRACT FROM AUTHOR]

Link prediction, which has important applications in many fields, predicts the possibility of the link between two nodes in a graph. Link prediction based on Graph Neural Network (GNN) obtains node representation and graph structure through GNN, which has attracted a growing amount of attention recently. However, the existing GNN-based link prediction approaches possess some shortcomings. On the one hand, because a graph contains different types of nodes, it leads to a great challenge for aggregating information and learning node representation from its neighbor nodes. On the other hand, the attention mechanism has been an effect instrument for enhancing the link prediction performance. However, the traditional attention mechanism is always monotonic for query nodes, which limits its influence on link prediction. To address these two problems, a Dual-Path Graph Neural Network (DPGNN) for link prediction is proposed in this study. First, we propose a novel Local Random Features Augmentation for Graph Convolution Network as a baseline of one path. Meanwhile, Graph Attention Network version 2 based on dynamic attention mechanism is adopted as a baseline of the other path. And then, we capture more meaningful node representation and more accurate link features by concatenating the information of these two paths. In addition, we propose an adaptive auxiliary module for better balancing the weight of auxiliary tasks, which brings more benefit to link prediction. Finally, extensive experiments verify the effectiveness and superiority of our proposed DPGNN for link prediction.