The time zero dielectric breakdown characteristics of MOSCAP with ultra-thin EOT high-k metal gate stacks are studied. The TZDB results show an abnormal area dependence due to the series resistance effect. The series ...The time zero dielectric breakdown characteristics of MOSCAP with ultra-thin EOT high-k metal gate stacks are studied. The TZDB results show an abnormal area dependence due to the series resistance effect. The series resistance components extracted from the Fowler-Nordheim tunneling relation are attributed to the spreading resistance due to the asymmetry electrodes. Based on a series model to eliminate the series resistance effect, an area acceleration dependence is obtained by correcting the TZDB results. The area dependence follows Poisson area scaling rules, which indicates that the mechanism of TZDB is the same as TDDB and could be considered as a trap generation process.展开更多
Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications.Internet of Things(IoT),fog computing,edge computing,cloud computing,and the edge ...Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications.Internet of Things(IoT),fog computing,edge computing,cloud computing,and the edge of things are the spine of all real-time and scalable applications.Conspicuously,this study proposed a novel framework for a real-time and scalable application that changes dynamically with time.In this study,IoT deployment is recommended for data acquisition.The Pre-Processing of data with local edge and fog nodes is implemented in this study.The thresholdoriented data classification method is deployed to improve the intrusion detection mechanism’s performance.The employment of machine learningempowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework.The placement of respondent nodes near the framework’s IoT layer minimizes the network’s latency.For economic evaluation of the proposed framework with minimal efforts,EdgeCloudSim and FogNetSim++simulation environments are deployed in this study.The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach,improved response rate,and prediction mechanism.Moreover,the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time.展开更多
基金Project supported by the National High Technology Research and Development Program(863 Program)of China(No.SS2015AA010601)the National Natural Science Foundation of China(Nos.61176091+1 种基金61306129)the Opening Project of the Key Laboratory of Microelectronics Devices&Integrated Technology,Institute of Microelectronics,Chinese Academy of Sciences
文摘The time zero dielectric breakdown characteristics of MOSCAP with ultra-thin EOT high-k metal gate stacks are studied. The TZDB results show an abnormal area dependence due to the series resistance effect. The series resistance components extracted from the Fowler-Nordheim tunneling relation are attributed to the spreading resistance due to the asymmetry electrodes. Based on a series model to eliminate the series resistance effect, an area acceleration dependence is obtained by correcting the TZDB results. The area dependence follows Poisson area scaling rules, which indicates that the mechanism of TZDB is the same as TDDB and could be considered as a trap generation process.
文摘Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications.Internet of Things(IoT),fog computing,edge computing,cloud computing,and the edge of things are the spine of all real-time and scalable applications.Conspicuously,this study proposed a novel framework for a real-time and scalable application that changes dynamically with time.In this study,IoT deployment is recommended for data acquisition.The Pre-Processing of data with local edge and fog nodes is implemented in this study.The thresholdoriented data classification method is deployed to improve the intrusion detection mechanism’s performance.The employment of machine learningempowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework.The placement of respondent nodes near the framework’s IoT layer minimizes the network’s latency.For economic evaluation of the proposed framework with minimal efforts,EdgeCloudSim and FogNetSim++simulation environments are deployed in this study.The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach,improved response rate,and prediction mechanism.Moreover,the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time.
