Bayesian Estimation of Multi-Trap RTN Parameters Using Markov Chain Monte Carlo Method

Hiromitsu AWANO  Hiroshi TSUTSUI  Hiroyuki OCHI  Takashi SATO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E95-A   No.12   pp.2272-2283
Publication Date: 2012/12/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E95.A.2272
Print ISSN: 0916-8508
Type of Manuscript: Special Section PAPER (Special Section on VLSI Design and CAD Algorithms)
Category: Device and Circuit Modeling and Analysis
random telegraph noise,  Bayesian estimation,  Markov chain Monte Carlo,  device characterization,  source separation,  statistical machine learning,  

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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.