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An assessment of the statistical distribution of Random Telegraph Noise Time Constants

Mehedi, M, Tok, KH, Zhang, JF, Ji, Z, Ye, Z, Zhang, WD and Marsland, JS (2020) An assessment of the statistical distribution of Random Telegraph Noise Time Constants. IEEE Access, 8. pp. 182273-182282. ISSN 2169-3536

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As transistor sizes are downscaled, a single trapped charge has a larger impact on smaller devices and the Random Telegraph Noise (RTN) becomes increasingly important. To optimize circuit design, one needs assessing the impact of RTN on the circuit and this can only be accomplished if there is an accurate statistical model of RTN. The dynamic Monte Carlo modelling requires the statistical distribution functions of both the amplitude and the capture/emission time (CET) of traps. Early works were focused on the amplitude distribution and the experimental data of CETs were typically too limited to establish their statistical distribution reliably. In particular, the time window used has been often small, e.g. 10 sec or less, so that there are few data on slow traps. It is not known whether the CET distribution extracted from such a limited time window can be used to predict the RTN beyond the test time window. The objectives of this work are three fold: to provide the long term RTN data and use them to test the CET distributions proposed by early works; to propose a methodology for characterizing the CET distribution for a fabrication process efficiently; and, for the first time, to verify the long term prediction capability of a CET distribution beyond the time window used for its extraction.

Item Type: Article
Uncontrolled Keywords: 08 Information and Computing Sciences, 09 Engineering, 10 Technology
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions: Engineering
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date Deposited: 05 Oct 2020 11:28
Last Modified: 22 Aug 2022 11:30
DOI or ID number: 10.1109/ACCESS.2020.3028747
URI: https://researchonline.ljmu.ac.uk/id/eprint/13797
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