Recent Trends in Bias Temperature Instability

Kaczer, B.; Franco, J.; Toledano-Luque, M.; Roussel, Jean-Marc; Cho, M.; Simoen, Eddy; Groeseneken, G.

doi:10.1007/978-1-4614-4078-9_2

Cited by 2 publications

(9 citation statements)

References 15 publications

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“…In other words, the 1/f noise PSD can be considered as an early indicator of the NBTI behavior of the pMOSFETs studied. In fact, it is by now well-established that there exists a strong correlation between 1/f noise and the NBTI relaxation transients, [18][19][20] since both are associated with pre-existing traps in the gate stack. The main difference is that NBTI probes a wider part (in depth and in energy) of the oxide trap density of states.…”

Section: Discussionmentioning

confidence: 99%

Low-Frequency Noise Assessment of the Oxide Trap Density in Thick-Oxide Input-Output Transistors for DRAM Applications

Simoen

Ritzenthaler

Cho

et al. 2016

ECS J. Solid State Sci. Technol.

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The impact of the implementation of a high-κ/metal-gate (HKMG) stack on the oxide integrity of input-output (I/O) pMOSFETs for DRAM periphery applications is investigated by means of low-frequency (LF) noise spectroscopy. It is shown that the predominant 1/f noise is governed by number fluctuations, irrespective of the details of the gate stack. However, the trap density in the 5 nm SiO 2 gate oxide, derived from the noise power spectral density is significantly increased by the application of the HKMG and/or Al 2 O 3 cap and the subsequent diffusion anneal at 900 • C.

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Section: Discussionmentioning

confidence: 99%

Low-Frequency Noise Assessment of the Oxide Trap Density in Thick-Oxide Input-Output Transistors for DRAM Applications

Simoen

Ritzenthaler

Cho

et al. 2016

ECS J. Solid State Sci. Technol.

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“…It has been reported that capturing one charge carrier by a trap can induce a shift of threshold voltage of tens of millivolts [3] and a fluctuation of current up to 10% [13]. This level of instability is comparable with that typically used to define the ageing-induced device lifetime [14].…”

Section: Introductionmentioning

confidence: 85%

“…OISE in MOSFETs adversely affects the performance of circuits [1][2][3][4][5][6][7][8][9][10][11][12]. IoT edge units are particularly vulnerable to it because the requirement of low power drives the operation voltage towards threshold level and reduces noise toleration [11], [12].…”

Section: Introductionmentioning

confidence: 99%

“…To optimize the circuit design, many efforts were made to model noise [1][2][3][4][5][6][7][8][9][10][11][12]. When devices are relatively large, there are many traps, and they collectively form 1/f noise.…”

Section: Introductionmentioning

confidence: 99%

“…As the device size shrinks to nano-meter scale, there are only a few traps in a device. The impact of a single trap increases and can be observed as Random Telegraph Noise (RTN) [1][2][3][4][5][6][7][8][9][10][11][12], where drain current exhibits step-like changes. The stochastic nature of trap distribution results in a large device-to-device variation Manuscript received MM DD, YYYY.…”

Section: Introductionmentioning

confidence: 99%

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An Integral Methodology for Predicting Long-Term RTN

Tok

Mehedi

Zhang

et al. 2022

IEEE Trans. Electron Devices

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Random Telegraph Noise (RTN) adversely impacts circuit performance and this impact increases for smaller devices and lower operation voltage. To optimize circuit design, many efforts have been made to model RTN. RTN is highly stochastic, with significant device-to-device variations. Early works often characterize individual traps first and then group them together to extract their statistical distributions. This bottom-up approach suffers from limitations in the number of traps it is possible to measure, especially for the capture and emission time constants, calling the reliability of extracted distributions into question. Several compact models have been proposed, but their ability to predict long term RTN is not verified. Many early works measured RTN only for tens of seconds, although a longer time window increases RTN by capturing slower traps. The aim of this work is to propose an integral methodology for modelling RTN and, for the first time, to verify its capability of predicting the long term RTN. Instead of characterizing properties of individual traps/devices, the RTN of multiple devices were integrated to form one dataset for extracting their statistical properties. This allows using the concept of effective charged traps (ECT) and transforms the need for time constant distribution to obtaining the kinetics of ECT, making long term RTN prediction similar to predicting ageing. The proposed methodology opens the way for assessing RTN impact within a window of 10 years by efficiently evaluating the probability of a device parameter at a given level.

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Recent Trends in Bias Temperature Instability

Cited by 2 publications

References 15 publications

Low-Frequency Noise Assessment of the Oxide Trap Density in Thick-Oxide Input-Output Transistors for DRAM Applications

Low-Frequency Noise Assessment of the Oxide Trap Density in Thick-Oxide Input-Output Transistors for DRAM Applications

An Integral Methodology for Predicting Long-Term RTN

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