Insight into the High Mobility and Stability of In2 O3 :H Film.

Wide bandgap semiconductors, particularly In2 O3 :Sn (ITO), are widely used as transparent conductive electrodes in optoelectronic devices. Nevertheless, due to the strohave beenng scattering probability of high-concentration oxygen vacancy (VO ) defects, the mobility of ITO is always lower than 40 cm2  V-1  s-1 . Recently, hydrogen-doped In2 O3 (In2 O3 :H) films have been proven to have high mobility (>100 cm2  V-1  s-1 ), but the origin of this high mobility is still unclear. Herein, a high-resolution electron microscope and theoretical calculations are employed to investigate the atomic-scale mechanisms behind the high carrier mobility in In2 O3 :H films. It is found that VO can cause strong lattice distortion and large carrier scattering probability, resulting in low carrier mobility. Furthermore, hydrogen doping can simultaneously reduce the concentration of VO , which accounts for high carrier mobility. The thermal stability and acid-base corrosion mechanism of the In2 O3 :H film are investigated and found that hydrogen overflows from the film at high temperatures (>250 °C), while acidic or alkaline environments can cause damage to the In2 O3 grains themselves. Overall, this work provides insights into the essential reasons for high carrier mobility in In2 O3 :H and presents a new research approach to the doping and stability mechanisms of transparent conductive oxides.

Deep High-Resolution Representation Learning for Visual Recognition.

High-resolution representations are essential for position-sensitive vision problems, such as human pose estimation, semantic segmentation, and object detection. Existing state-of-the-art frameworks first encode the input image as a low-resolution representation through a subnetwork that is formed by connecting high-to-low resolution convolutions in series (e.g., ResNet, VGGNet), and then recover the high-resolution representation from the encoded low-resolution representation. Instead, our proposed network, named as High-Resolution Network (HRNet), maintains high-resolution representations through the whole process. There are two key characteristics: (i) Connect the high-to-low resolution convolution streams in parallel and (ii) repeatedly exchange the information across resolutions. The benefit is that the resulting representation is semantically richer and spatially more precise. We show the superiority of the proposed HRNet in a wide range of applications, including human pose estimation, semantic segmentation, and object detection, suggesting that the HRNet is a stronger backbone for computer vision problems. All the codes are available at https://github.com/HRNet.