An Integral Methodology for Predicting Long-Term RTN-Reference-Cited by-同舟云学术

An Integral Methodology for Predicting Long-Term RTN

Published:2022-07 Issue:7 Volume:69 Page:3869-3875
ISSN:0018-9383
Container-title:IEEE Transactions on Electron Devices
language:
Short-container-title:IEEE Trans. Electron Devices

Author:

Tok Kean H.¹,Mehedi Mehzabeen¹,Zhang Jian F.¹^ORCID,Brown James¹,Ye Zengliang¹^ORCID,Ji Zhigang¹^ORCID,Zhang Weidong¹^ORCID,Marsland John S.¹,Asenov Asen²,Georgiev Vihar²^ORCID

Affiliation:

1. School of Engineering, Liverpool John Moores University, Liverpool, U.K.

2. Department of Electronics and Electrical Engineering, University of Glasgow, Glasgow, U.K.

Funder

Engineering and Physical Science Research Council of U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials

Link

http://xplorestaging.ieee.org/ielx7/16/9802454/09785368.pdf?arnumber=9785368

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3. Worst-Case Performance Analysis Under Random Telegraph Noise Induced Threshold Voltage Variability

4. Understanding short-term BTI behavior through comprehensive observation of gate-voltage dependence of RTN in highly scaled high-k/metal-gate pFETs;miki;Proc Very Large Scale Integration Tech Symp,2011

5. As-grown-generation (AG) model of NBTI: A shift from fitting test data to prediction

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1. Extracting statistical distributions of RTN originating from both acceptor-like and donor-like traps;2023 IEEE 15th International Conference on ASIC (ASICON);2023-10-24

2. Characterizing and Modeling RTN Under Real Circuit Bias Conditions;IEEE Transactions on Electron Devices;2023-05

3. AC RTN: Testing, Modeling, and Prediction;IEEE Transactions on Electron Devices;2022-10