Tok, KH, Zhang, JF, Brown, J, Zhigang, J and Zhang, WD (2023) Extracting statistical distributions of RTN originating from both acceptor-like and donor-like traps. In: Proceedings of IEEE 15th International Conference on ASIC (ASICON) . pp. 1-4. (2023 IEEE 15th International Conference on ASIC (ASICON 2023), 24-27 October 2023, Nanjing, China).

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Abstract

The impact of Random Telegraph Noise (RTN) on devices increases, as the device sizes are downscaled. Against a reference level, it is commonly observed that RTN can fluctuate both below and above this level. The modelling of RTN, however, was typically carried out only in the direction where drain current reduces. In reality, this current reduction can be compensated by simultaneous current increases. This calls the accuracy of the onedirectional RTN modelling into questions. Separating the fluctuation in one direction from the other is difficult experimentally. In this paper, we review the recently proposed integral methodology for achieving this separation. In contrast with early works, the integral methodology does not require selecting devices with fluctuation only in one direction. The RTN in all devices are measured and grouped together to form one dataset. It is then statically analyzed by assuming the presence of fluctuation in both directions. In this way, the separation is carried out numerically, rather than experimentally. Based on the maximum likelihood estimation, the popular statistical distributions are tested against experimental data. It is found that the General Extreme Value (GEV) distribution agrees best with the experimental threshold voltage shift, when compared with the Exponential and Lognormal distributions.

Item Type:	Conference or Workshop Item (Paper)
Additional Information:	© 2023 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.
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics. Nuclear engineering
Divisions:	Computer Science & Mathematics Engineering
Publisher:	IEEE
SWORD Depositor:	A Symplectic
Date Deposited:	21 Nov 2023 10:09
Last Modified:	05 Feb 2024 12:28
DOI or ID number:	10.1109/ASICON58565.2023.10396643
URI:	https://researchonline.ljmu.ac.uk/id/eprint/21828

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