Recent advances in NiO/Ga_(2)O_(3) heterojunctions for power electronics

Beta gallium oxide(β-Ga_(2)O_(3)) has attracted significant attention for applications in power electronics due to its ultrawide bandgap of ~ 4.8 eV and the large critical electric field of 8 MV/cm. These properties yield a high Baliga's figures of merit(BFOM) of more than 3000. Though β-Ga_(2)O_(3) possesses superior material properties, the lack of p-type doping is the main obstacle that hinders the development of β-Ga_(2)O_(3)-based power devices for commercial use. Constructing heterojunctions by employing other p-type materials has been proven to be a feasible solution to this issue. Nickel oxide(NiO) is the most promising candidate due to its wide band gap of 3.6–4.0 eV. So far, remarkable progress has been made in NiO/β-Ga_(2)O_(3) heterojunction power devices. This review aims to summarize recent advances in the construction, characterization, and device performance of the NiO/β-Ga_(2)O_(3) heterojunction power devices. The crystallinity, band structure, and carrier transport property of the sputtered NiO/β-Ga_(2)O_(3) heterojunctions are discussed. Various device architectures, including the NiO/β-Ga_(2)O_(3) heterojunction pn diodes(HJDs), junction barrier Schottky(JBS) diodes, and junction field effect transistors(JFET), as well as the edge terminations and super-junctions based on the NiO/β-Ga_(2)O_(3) heterojunction, are described.

SuperFusion: A Versatile Image Registration and Fusion Network with Semantic Awareness

Image fusion aims to integrate complementary information in source images to synthesize a fused image comprehensively characterizing the imaging scene. However, existing image fusion algorithms are only applicable to strictly aligned source images and cause severe artifacts in the fusion results when input images have slight shifts or deformations. In addition,the fusion results typically only have good visual effect, but neglect the semantic requirements of high-level vision tasks.This study incorporates image registration, image fusion, and semantic requirements of high-level vision tasks into a single framework and proposes a novel image registration and fusion method, named Super Fusion. Specifically, we design a registration network to estimate bidirectional deformation fields to rectify geometric distortions of input images under the supervision of both photometric and end-point constraints. The registration and fusion are combined in a symmetric scheme, in which while mutual promotion can be achieved by optimizing the naive fusion loss, it is further enhanced by the mono-modal consistent constraint on symmetric fusion outputs. In addition, the image fusion network is equipped with the global spatial attention mechanism to achieve adaptive feature integration. Moreover, the semantic constraint based on the pre-trained segmentation model and Lovasz-Softmax loss is deployed to guide the fusion network to focus more on the semantic requirements of high-level vision tasks. Extensive experiments on image registration, image fusion,and semantic segmentation tasks demonstrate the superiority of our Super Fusion compared to the state-of-the-art alternatives.The source code and pre-trained model are publicly available at https://github.com/Linfeng-Tang/Super Fusion.