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Title: Automation of Model Parameter Estimation for Random Telegraph Noise
Authors: SHIMIZU, Hirofumi
AWANO, Hiromitsu  kyouindb  KAKEN_id
HIROMOTO, Masayuki  kyouindb  KAKEN_id
SATO, Takashi  kyouindb  KAKEN_id  orcid (unconfirmed)
Author's alias: 佐藤, 高史
Keywords: random telegraph noise (RTN)
Gaussian mixture model (GMM)
expectation-maximization (EM)
information criteria
model estimation
Issue Date: 1-Dec-2014
Publisher: Institute of Electronics, Information and Communication Engineers(IEICE)
Journal title: IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Volume: E97.A
Issue: 12
Start page: 2383
End page: 2392
Abstract: The modeling of random telegraph noise (RTN) of MOS transistors is becoming increasingly important. In this paper, a novel method is proposed for realizing automated estimation of two important RTN-model parameters: the number of interface-states and corresponding threshold voltage shift. The proposed method utilizes a Gaussian mixture model (GMM) to represent the voltage distributions, and estimates their parameters using the expectation-maximization (EM) algorithm. Using information criteria, the optimal estimation is automatically obtained while avoiding overfitting. In addition, we use a shared variance for all the Gaussian components in the GMM to deal with the noise in RTN signals. The proposed method improved estimation accuracy when the large measurement noise is observed.
Rights: © 2014 The Institute of Electronics, Information and Communication Engineers
DOI(Published Version): 10.1587/transfun.E97.A.2383
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