IEICE TRANSACTIONS on Fundamentals
Automation of Model Parameter Estimation for Random Telegraph Noise
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E97-A No.12 pp.2383-2392
- Publication Date
- 2014/12/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E97.A.2383
- Type of Manuscript
- Special Section PAPER (Special Section on VLSI Design and CAD Algorithms)
- Category
- Device and Circuit Modeling and Analysis
Authors
Hirofumi SHIMIZU
Kyoto University
Hiromitsu AWANO
Kyoto University
Masayuki HIROMOTO
Kyoto University
Takashi SATO
Kyoto University
Keyword
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Hirofumi SHIMIZU, Hiromitsu AWANO, Masayuki HIROMOTO, Takashi SATO, "Automation of Model Parameter Estimation for Random Telegraph Noise" in IEICE TRANSACTIONS on Fundamentals,
vol. E97-A, no. 12, pp. 2383-2392, December 2014, doi: 10.1587/transfun.E97.A.2383.
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.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E97.A.2383/_p
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@ARTICLE{e97-a_12_2383,
author={Hirofumi SHIMIZU, Hiromitsu AWANO, Masayuki HIROMOTO, Takashi SATO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Automation of Model Parameter Estimation for Random Telegraph Noise},
year={2014},
volume={E97-A},
number={12},
pages={2383-2392},
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.},
keywords={},
doi={10.1587/transfun.E97.A.2383},
ISSN={1745-1337},
month={December},}
Copy
TY - JOUR
TI - Automation of Model Parameter Estimation for Random Telegraph Noise
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2383
EP - 2392
AU - Hirofumi SHIMIZU
AU - Hiromitsu AWANO
AU - Masayuki HIROMOTO
AU - Takashi SATO
PY - 2014
DO - 10.1587/transfun.E97.A.2383
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E97-A
IS - 12
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - December 2014
AB - 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.
ER -
English
