A fast and test-proven methodology of assessing RTN/fluctuation on deeply scaled nano pMOSFETs

Gao, Rui; Mehedi, Mehzabeen; Chen, H.; Wang, X.; Zhang, J. F.; Lin, X. L.; He, Zhiyuan; Chen, Yiqiang; Lei, Dengyun; Huang, Yiming; En, Yunfei; Ji, Zhigang; Wang, R.

doi:10.1109/irps45951.2020.9129230

Cited by 2 publications

(2 citation statements)

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“…Some early works [13], [20] measured RTN with a time window of tens of seconds, whilst it is well known that RTN-induced fluctuation increases for longer time window. A number of compact models [16]- [18] were proposed by assuming that RTN magnitude and time constants follow certain statistical distributions. It is not verified, however, that these models can be used to predict long term RTN.…”

Section: Introductionmentioning

confidence: 99%

An Integral Methodology for Predicting Long-Term RTN

Tok

Mehedi

Zhang

et al. 2022

IEEE Trans. Electron Devices

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Random Telegraph Noise (RTN) adversely impacts circuit performance and this impact increases for smaller devices and lower operation voltage. To optimize circuit design, many efforts have been made to model RTN. RTN is highly stochastic, with significant device-to-device variations. Early works often characterize individual traps first and then group them together to extract their statistical distributions. This bottom-up approach suffers from limitations in the number of traps it is possible to measure, especially for the capture and emission time constants, calling the reliability of extracted distributions into question. Several compact models have been proposed, but their ability to predict long term RTN is not verified. Many early works measured RTN only for tens of seconds, although a longer time window increases RTN by capturing slower traps. The aim of this work is to propose an integral methodology for modelling RTN and, for the first time, to verify its capability of predicting the long term RTN. Instead of characterizing properties of individual traps/devices, the RTN of multiple devices were integrated to form one dataset for extracting their statistical properties. This allows using the concept of effective charged traps (ECT) and transforms the need for time constant distribution to obtaining the kinetics of ECT, making long term RTN prediction similar to predicting ageing. The proposed methodology opens the way for assessing RTN impact within a window of 10 years by efficiently evaluating the probability of a device parameter at a given level.

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Section: Introductionmentioning

confidence: 99%

An Integral Methodology for Predicting Long-Term RTN

Tok

Mehedi

Zhang

et al. 2022

IEEE Trans. Electron Devices

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show abstract

“…The review of this paper was arranged by Editor Edmundo Gutiérrez-D (Corresponding author: J. F. Zhang, e-mail: j.f.zhang@ljmu.ac.uk) both time [2]- [11], [16]- [19] and frequency [22] domain. In the time domain, Monte Carlo simulation has been carried out for both DC and AC RTN by assuming that RTN transitions are memoryless random Markov process [16]- [21]. The required inputs are the statistical distributions of (i) trap capture (τC)/emission (τE) time constants, (ii) RTN amplitude per trap, and (iii) number of traps per device.…”

Section: Introductionmentioning

confidence: 99%