A Physics-Based Statistical RTN Model for the Low Frequency Noise in MOSFETs

Silva, Mauricio Banaszeski da; Tuinhout, H.P.; Duijnhoven, Adrie Zegers-van; Wirth, Gilson; Scholten, A.J.

doi:10.1109/ted.2016.2593916

Cited by 33 publications

(16 citation statements)

References 25 publications

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“…Step 4. For each bootstrap sample {y * ij }, fit a regression model as in 3.2, and also obtain the estimation of the slope at point x, that is m ′ h (x) (1) ; Step 5. Repeat steps 1-4, a total of R times.…”

Section: Algorithmmentioning

confidence: 99%

“…Repeat steps 1-4, a total of R times. As a result, obtain a sample m ′ h (x) (1) , m ′ h (x) (2) , . .…”

Section: Algorithmmentioning

confidence: 99%

“…Noise is a stochastic signal and unpredictable, therefore it is necessary to use statistical methods to analyze its behavior. Any tentative to understand the random behavior of the underlying phenomenon can allow to undertake further improvements, see [20], [1] and [3], among others. One interesting investigation line is to analyze the problem from a statistical perspective and in this sense the study of potential correlations between the switching threshold voltage and noise characteristics is of great importance, as considered in [7], and yet it has received little attention in the current literature.…”

Section: Introductionmentioning

confidence: 99%

“…Although the literature on stochastic models for explaining the random behavior of low frequency noise is not short (see [16], [23], [1], [3]), however there are not many studies specifically focused on the relationship noise-voltage from a statistical learning point of view, that is, based on data. This is where the statistician can get into the game with the aim of adapting powerful machine learning techniques that have been far demonstrated their efficacy both theoretic and practically and bringing them to this particular application field of Physics.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Statistical Supervised Learning With Engineering Data: A Case Study of Low Frequency Noise Measured On Semiconductor Devices

Gamiz

Kalen

Nozal-Cañadas

et al. 2021

Preprint

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Our practical motivation is the analysis of potential correlations between spectral noise current and threshold voltage from common on-wafer MOSFETs. The usual strategy leads to the use of standard techniques based on Normal linear regression easily accessible in all statistical software (both free or commercial). However, these statistical methods are not appropriate because the assumptions they lie on are not met. More sophisticated methods are required. A new strategy based on the most novel nonparametric techniques which are data-driven and thus free from questionable parametric assumptions is proposed. A backfitting algorithm accounting for random effects and nonparametric regression is designed and implemented. The nature of the correlation between threshold voltage and noise is examined by conducting a statistical test, which is based on a novel technique that summarizes in a color map all the relevant information of the data. The way the results are presented in the plot makes it easy for a non-expert in data analysis to understand what is underlying. The good performance of the method is proven through simulations and it is applied to a data case in a field where these modern statistical techniques are novel and result very efficient.

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Section: Algorithmmentioning

confidence: 99%

“…Repeat steps 1-4, a total of R times. As a result, obtain a sample m ′ h (x) (1) , m ′ h (x) (2) , . .…”

Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Statistical Supervised Learning With Engineering Data: A Case Study of Low Frequency Noise Measured On Semiconductor Devices

Gamiz

Kalen

Nozal-Cañadas

et al. 2021

Preprint

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“…Therefore, to accurately model the memory cell's endurance and retention characteristics, the filling state of all the bulk traps must be obtained, which requires a comprehensive electron trapping-detrapping (T-D) model. In addition, the electron T-D phenomenon has also great impact on the device's bias temperature instability (BTI) [2,3] and random telegraph noise (RTN) [4,5] as the size scales.…”

Section: Introductionmentioning

confidence: 99%

A physical model of electron trapping/detrapping in electrically stressed oxide

Wang

Liu

Zhang

et al. 2017

IEICE Electron. Express

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A physical model of electron trapping/detrapping in electrically stressed oxide has been proposed in this paper. The new model is based on both inelastic multi-phonon trap-assisted tunneling and thermal emission, and also considers the capture effect of oxide bulk traps. It handles every trap separately, and establishes the dynamic procedure of traps capture and emission of electrons. Finally, through the proposed model we may accurately and effectively obtain the filling state of all the oxide traps at any stress and any time, which is very useful for the modeling of the endurance and data retention characteristics of floating gate nonvolatile memories.

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