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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/37241

Title: A Dual-Point technique for the entire ID-VG characterization into subthreshold region under Random Telegraph Noise condition
Authors: Zhan, Xuepeng
Shen, Chengda
Ji, Zhigang
Chen, Jiezhi
Fang, Hui
Guo, Fangbin
Zhang, Jianfu
Keywords: Random Telegraph Noise(RTN)
Bias Temperature Instability
Time-Dependent Variability
Issue Date: 2019
Publisher: © IEEE
Citation: ZHAN, X. ... et al, 2019. A Dual-Point technique for the entire ID-VG characterization into subthreshold region under Random Telegraph Noise condition. IEEE Electron Device Letters, [in press].
Abstract: A simple Dual-Point technique to measure the entire transfer characteristics (ID-VG) down to sub-threshold region in the nano-scaled MOSFET under Random Telegraph Noise (RTN) condition with either capturing or emitting one elementary charge by a trap in the gate dielectric is proposed. Its compatibility with the commercial semiconductor analyzer makes it a readily-usable tool for future RTN study. In this work, we use this technique to explore the VG dependence of RTN induced by a single trapped carrier in both n- and p- FETs.
Description: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: This work is financially supported by the Engineering and Physical Science Research Council of UK under EP/L010607/1, China Key Research and Development Program (2016YFA0201802), National Natural Science Foundation of China (61874068) and China Postdoctoral Science Foundation (2018M630777).
Version: Accepted for publication
DOI: 10.1109/led.2019.2903516
URI: https://dspace.lboro.ac.uk/2134/37241
Publisher Link: https://doi.org/10.1109/led.2019.2903516
ISSN: 0741-3106
Appears in Collections:Published Articles (Computer Science)

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