An Efficient Analog Compact NBTI Model for Stress and Recovery Based on Activation Energy Maps

Puschkarsky, Katja; Reisinger, H.; Rott, Gunnar Andreas; Schlunder, C.; Gustin, Wolfgang

doi:10.1109/ted.2019.2941889

Cited by 11 publications

(11 citation statements)

References 31 publications

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“…The defect density map is represented in agreement with bivariate Gaussian distribution, describing the permanent and recoverable components. In the activation energy space, the density D(E c , E e ) is constructed by the joint probability density function [9,10,14,15]…”

Section: Theoretical Modelmentioning

confidence: 99%

“…A correlation coefficient is defined as ρ = covariance(E c , E e )/σ c σ e . For defects being charged up to the stress time t s and not yet being discharged at the recovery time t r , the threshold voltage shift is calculated by integrating over the activation energy map as follows [9,10,14,15] ∆V…”

Section: Theoretical Modelmentioning

confidence: 99%

“…In nanoscale CMOS devices, the stochastic behavior of a few defects can have a significant impact on the reliability of devices and their circuits [1][2][3][4][5][6][7][8]. The negative bias temperature instability (NBTI) is among the most critical reliability issues and can be described by capture-emission energy (CEE) maps [3][4][5][9][10][11][12][13][14][15]. Accurately modeling the NBTI relies on a combined methodology comprising thorough CEE maps and efficient models.…”

Section: Introductionmentioning

confidence: 99%

“…Accurately modeling the NBTI relies on a combined methodology comprising thorough CEE maps and efficient models. However, CEE maps are limited by the stress-recovery measurement window [9,10,[14][15][16][17]. An accurate estimation of the parameters of CEE distribution is crucial to enable full lifetime calculation of NBTI without measurement time extrapolation.…”

Section: Introductionmentioning

confidence: 99%

“…An accurate estimation of the parameters of CEE distribution is crucial to enable full lifetime calculation of NBTI without measurement time extrapolation. The kinetics of defects responsible for NBTI has been explained by the random capture-emission process of charge carriers caused by oxide traps [9][10][11][12][13][14][15]18,19]. Moreover, it has been found that the recoverable component of NBTI and random telegraph signal (RTS) noise are due to same defects [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Data-Driven Modeling of Low Frequency Noise Using Capture-Emission Energy Maps

Lee

2020

Applied Sciences

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A new approach for modeling low frequency noise is presented to enable the predictions of noise behavior from negative bias temperature instability (NBTI). The noise model is based on a capture-emission energy (CEE) map describing the probability density function of widely distributed defect capture-emission activation energies. To enlarge the capture-emission energy window and to perform the accurate estimation of the recoverable component of CEE, the Gaussian mixture model (GMM) is applied to the CEE map. This approach provides an efficient identification of noise sources and an in-depth noise analysis under both stationary and cyclo-stationary conditions.

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Section: Theoretical Modelmentioning

confidence: 99%

Section: Theoretical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data-Driven Modeling of Low Frequency Noise Using Capture-Emission Energy Maps

Lee

2020

Applied Sciences

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Perspective of 2D Integrated Electronic Circuits: Scientific Pipe Dream or Disruptive Technology?

et al. 2022

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Within the last decade, considerable efforts have been devoted to fabricating transistors utilizing 2D semiconductors. Also, small circuits consisting of a few transistors have been demonstrated, including inverters, ring oscillators, and static random access memory cells. However, for industrial applications, both time‐zero and time‐dependent variability in the performance of the transistors appear critical. While time‐zero variability is primarily related to immature processing, time‐dependent drifts are dominated by charge trapping at defects located at the channel/insulator interface and in the insulator itself, which can substantially degrade the stability of circuits. At the current state of the art, 2D transistors typically exhibit a few orders of magnitude higher trap densities than silicon devices, which considerably increases their time‐dependent variability, resulting in stability and yield issues. Here, the stability of currently available 2D electronics is carefully evaluated using circuit simulations to determine the impact of transistor‐related issues on the overall circuit performance. The results suggest that while the performance parameters of transistors based on certain material combinations are already getting close to being competitive with Si technologies, a reduction in variability and defect densities is required. Overall, the criteria for parameter variability serve as guidance for evaluating the future development of 2D technologies.

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Physics-informed machine learning model for bias temperature instability

Lee

2021

AIP Advances

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A new machine learning model is presented to predict the dynamic behavior of threshold voltage shifts induced by bias temperature instability (BTI) in CMOS devices. The model is constructed by combining physical theories with machine learning such as an artificial neural network and a Gaussian mixture model (GMM). To enlarge the capture–emission energy (CEE) window and to perform independent estimations of two distinct components of CEE distribution, the GMM with soft clustering is utilized, enabling full lifetime modeling of BTI. By training the CEE map with the consideration of the occupancy probability of traps and then executing the integration along the CEE, the threshold voltage shifts are obtained. This approach forms data-driven modeling that naturally encodes underlying physical theories as prior information. The resulting model exhibits a good performance for predicting the dynamic characteristics of BTI under various stress-recovery conditions.

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An Efficient Analog Compact NBTI Model for Stress and Recovery Based on Activation Energy Maps

Cited by 11 publications

References 31 publications

Data-Driven Modeling of Low Frequency Noise Using Capture-Emission Energy Maps

Data-Driven Modeling of Low Frequency Noise Using Capture-Emission Energy Maps

Perspective of 2D Integrated Electronic Circuits: Scientific Pipe Dream or Disruptive Technology?

Physics-informed machine learning model for bias temperature instability

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