An Integral Methodology for Predicting Long-Term RTN

Author:

Tok Kean H.1,Mehedi Mehzabeen1,Zhang Jian F.1ORCID,Brown James1,Ye Zengliang1ORCID,Ji Zhigang1ORCID,Zhang Weidong1ORCID,Marsland John S.1,Asenov Asen2,Georgiev Vihar2ORCID

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

Reference28 articles.

1. From mean values to distributions of BTI lifetime of deeply scaled FETs through atomistic understanding of the degradation;toledano-luque;Proc Symp VLSI Technol Dig Tech Papers,2011

2. An Assessment of the Statistical Distribution of Random Telegraph Noise Time Constants

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

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