HfAlO/InAlAs metal-oxide-semiconductor capacitor (MOS capacitor) is considered as the most popular candidate of the isolated gate of InAs/AlSb high electron mobility transistor (HEMT). In order to improve the performa...HfAlO/InAlAs metal-oxide-semiconductor capacitor (MOS capacitor) is considered as the most popular candidate of the isolated gate of InAs/AlSb high electron mobility transistor (HEMT). In order to improve the performance of the HfAlO/InAlAs MOS-capacitor, samples are annealed at different temperatures for investigating the HfAlO/InAlAs interfacial characyeristics and the device's electrical characteristics. We find that as annealing temperature increases from 280 ℃ to 480 ℃, the surface roughness on the oxide layer is improved. A maximum equivalent dielectric constant of 8.47, a minimum equivalent oxide thickness of 5.53 nm, and a small threshold voltage of -1.05 V are detected when being annealed at 380 ℃;furthermore, a low interfacial state density is yielded at 380 ℃, and this can effectively reduce the device leakage current density to a significantly low value of 1×10-7 A/cm2 at 3-V bias voltage. Therefore, we hold that 380 ℃ is the best compromised annealing temperature to ensure that the device performance is improved effectively. This study provides a reliable conceptual basis for preparing and applying HfAlO/InAlAs MOS-capacitor as the isolated gate on InAs/AlSb HEMT devices.展开更多
Domain adaptation(DA)for semantic segmentation aims to reduce the annotation burden for the dense pixellevel prediction task.It focuses on tackling the domain gap problem and manages to transfer knowledge learned from...Domain adaptation(DA)for semantic segmentation aims to reduce the annotation burden for the dense pixellevel prediction task.It focuses on tackling the domain gap problem and manages to transfer knowledge learned from abundant source data to new target scenes.Although recent works have achieved rapid progress in this field,they still underperform fully supervised models with a large margin due to the absence of any available hints in the target domain.Considering that few-shot labels are cheap to obtain in practical applications,wc attempt to leverage them to mitigate the performance gap between DA and fully supervised methods.The key to this problem is to leverage the few-shot labels to learn robust domain-invariant predictions effectively.To this end,we first design a data perturbation strategy to enhance the robustness of the representations.Furthermore,a transferable prototype module is proposed to bridge the domain gap based on the source data and few-shot targets.By means of these proposed methods,our approach can perform on par with the fully supervised models to some extent.We conduct extensive experiments to demonstrate the effectiveness of the proposed methods and report the state-of-the-art performance on two popular DA tasks,i.e.,from GTA5 to Cityscapes and SYNTHIA to Cityscapes.展开更多
文摘HfAlO/InAlAs metal-oxide-semiconductor capacitor (MOS capacitor) is considered as the most popular candidate of the isolated gate of InAs/AlSb high electron mobility transistor (HEMT). In order to improve the performance of the HfAlO/InAlAs MOS-capacitor, samples are annealed at different temperatures for investigating the HfAlO/InAlAs interfacial characyeristics and the device's electrical characteristics. We find that as annealing temperature increases from 280 ℃ to 480 ℃, the surface roughness on the oxide layer is improved. A maximum equivalent dielectric constant of 8.47, a minimum equivalent oxide thickness of 5.53 nm, and a small threshold voltage of -1.05 V are detected when being annealed at 380 ℃;furthermore, a low interfacial state density is yielded at 380 ℃, and this can effectively reduce the device leakage current density to a significantly low value of 1×10-7 A/cm2 at 3-V bias voltage. Therefore, we hold that 380 ℃ is the best compromised annealing temperature to ensure that the device performance is improved effectively. This study provides a reliable conceptual basis for preparing and applying HfAlO/InAlAs MOS-capacitor as the isolated gate on InAs/AlSb HEMT devices.
基金This work was supported in part by the National Key R&D Program of China(2019QY1604)the Major Project for New Generation of AI(2018AAA0100400)the National Youth Talent Support Program,and the National Natural Science Foundation of China(Grant Nos.U21B2042,62006231,and 62072457).
文摘Domain adaptation(DA)for semantic segmentation aims to reduce the annotation burden for the dense pixellevel prediction task.It focuses on tackling the domain gap problem and manages to transfer knowledge learned from abundant source data to new target scenes.Although recent works have achieved rapid progress in this field,they still underperform fully supervised models with a large margin due to the absence of any available hints in the target domain.Considering that few-shot labels are cheap to obtain in practical applications,wc attempt to leverage them to mitigate the performance gap between DA and fully supervised methods.The key to this problem is to leverage the few-shot labels to learn robust domain-invariant predictions effectively.To this end,we first design a data perturbation strategy to enhance the robustness of the representations.Furthermore,a transferable prototype module is proposed to bridge the domain gap based on the source data and few-shot targets.By means of these proposed methods,our approach can perform on par with the fully supervised models to some extent.We conduct extensive experiments to demonstrate the effectiveness of the proposed methods and report the state-of-the-art performance on two popular DA tasks,i.e.,from GTA5 to Cityscapes and SYNTHIA to Cityscapes.
