A unified model for the flicker noise in metal-oxide-semiconductor field-effect transistors

Hung, K.K.; Ko, P.K.; Hu, Chenming; Cheng, Yung‐Chen

doi:10.1109/16.47770

Cited by 806 publications

(455 citation statements)

References 38 publications

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“…The following discussion will build on our understanding of flicker noise in semiconductor devices that generally results from the fluctuation of charge carrier number and mobility. 41 For the ionic current in a nanopore, it is known to consist of two components: and current bias were applied so as to reach an invariable bulk current.…”

Section: Origin Of the Nanopore Flicker Noisementioning

confidence: 99%

Generalized Noise Study of Solid-State Nanopores at Low Frequencies

et al. 2017

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Nanopore technology has been extensively investigated for analysis of biomolecules, and a success story in this field concerns DNA sequencing using a nanopore chip featuring an array of hundreds of biological nanopores (BioNs). Solid-state nanopores (SSNs) have been explored to attain longer lifetime and higher integration density than what BioNs can offer, but SSNs are generally considered to generate higher noise whose origin remains to be confirmed. Here, we systematically study low-frequency (including thermal and flicker) noise characteristics of SSNs measuring 7 to 200 nm in diameter drilled through a 20-nm-thick SiN x membrane by focused ion milling. Both bulk and surface ionic currents in the nanopore are found to contribute to the flicker noise, with their respective contributions determined by salt concentration and pH in electrolytes as well as bias conditions. Increasing salt concentration at constant pH and voltage bias leads to increase in the bulk ionic current and noise therefrom. Changing pH at constant salt concentration and current bias results in variation of surface charge density, and hence alteration of surface ionic current and noise. In addition, the noise from Ag/AgCl electrodes can become predominant when the pore size is large and/or the salt concentration is high. Analysis of our comprehensive experimental results leads to the establishment of a generalized nanopore noise model. The model not only gives an excellent account of the experimental observations, but can also be used for evaluation of various noise components in much smaller nanopores currently not experimentally available.

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Section: Origin Of the Nanopore Flicker Noisementioning

confidence: 99%

Generalized Noise Study of Solid-State Nanopores at Low Frequencies

et al. 2017

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“…͑1͒ and the other is based on the number fluctuation of charge carriers correlated with mobility fluctuations ͑CNF+ CMF͒. 5,10 The mobility fluctuations are due to Coulombic scattering by trapped charges. Unlike the HMF model for bulk conduction, the CNF+ CMF model is well defined for surface conduction, and the noise can be expressed by,…”

Section: Low-frequency Noise In Junctionless Multigate Transistorsmentioning

confidence: 99%

Low-frequency noise in junctionless multigate transistors

Jang

Lee

et al. 2011

Applied Physics Letters

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Low-frequency noise in n-type junctionless multigate transistors was investigated. It can be well understood with the carrier number fluctuations whereas the conduction is mainly limited by the bulk expecting Hooge mobility fluctuations. The trapping/release of charge carriers is related not only to the oxide-semiconductor interface but also to the depleted channel. The volume trap density is in the range of 6-30 x 10(16) cm(-3) eV(-1), which is similar to Si-SiO2 bulk transistors and remarkably lower than in high-k transistors. These results show that the noise in nanowire devices might be affected by additional trapping centers. (C) 2011 American Institute of Physics. (doi:10.1063/1.3569724

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“…ISFET is subject to inaccuracies originating from both drift and low frequency (1/f) noise. The 1/f noise mechanism in metal-oxide-semiconductor FET (MOSFET) has been studied extensively and is rather well elucidated [1]- [4]. In contrast, very little effort has been made to develop physical models for drift.…”

Section: Introductionmentioning

confidence: 99%

Physically-Informed Correction of Instability (Drift)

Jamasb¹

2012

IJCEE

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Abstract-A Physical model for the long-term deterministic behavior associated with instability (or drift) in pH-sensitive ion-selective field effect transistors (ISFET's) has been employed to distinguish between drift and the inherent low-frequency (1/f) noise, both of which corrupt the sensor output signal. Based on this model, a physically-informed method for sensor signal enhancement in the time-domain has been proposed, which relies on proper windowing of the sensor output. The random-like evolution in time associated with a weak power-law relaxation towards equilibrium is the key aspect of the model for drift that is utilized in the proposed corrective scheme. Drift may be interpreted as a weakly chaotic behavior, exhibiting anomalous dynamics characterized by ageing, which reflects a weak (power-law) relaxation towards equilibrium.

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A unified model for the flicker noise in metal-oxide-semiconductor field-effect transistors

Cited by 806 publications

References 38 publications

Generalized Noise Study of Solid-State Nanopores at Low Frequencies

Generalized Noise Study of Solid-State Nanopores at Low Frequencies

Low-frequency noise in junctionless multigate transistors

Physically-Informed Correction of Instability (Drift)

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