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Title: Bayesian Estimation of Multi-Trap RTN Parameters Using Markov Chain Monte Carlo Method
Authors: AWANO, Hiromitsu  kyouindb  KAKEN_id
TSUTSUI, Hiroshi
OCHI, Hiroyuki
SATO, Takashi  kyouindb  KAKEN_id  orcid https://orcid.org/0000-0002-1577-8259 (unconfirmed)
Author's alias: 佐藤, 高史
Keywords: random telegraph noise
Bayesian estimation
Markov chain Monte Carlo
device characterization
source separation
statistical machine learning
Issue Date: Dec-2012
Publisher: The Institute of Electronics, Information and Communication Engineers
Journal title: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Volume: E95.A
Issue: 12
Start page: 2272
End page: 2283
Abstract: Random telegraph noise (RTN) is a phenomenon that is considered to limit the reliability and performance of circuits using advanced devices. The time constants of carrier capture and emission and the associated change in the threshold voltage are important parameters commonly included in various models, but their extraction from time-domain observations has been a difficult task. In this study, we propose a statistical method for simultaneously estimating interrelated parameters: the time constants and magnitude of the threshold voltage shift. Our method is based on a graphical network representation, and the parameters are estimated using the Markov chain Monte Carlo method. Experimental application of the proposed method to synthetic and measured time-domain RTN signals was successful. The proposed method can handle interrelated parameters of multiple traps and thereby contributes to the construction of more accurate RTN models.
Rights: © 2012 The Institute of Electronics, Information and Communication Engineers
URI: http://hdl.handle.net/2433/178695
DOI(Published Version): 10.1587/transfun.E95.A.2272
Related Link: http://www.ieice.org/jpn/index.html
Appears in Collections:Journal Articles

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