Compact Gate Capacitance Model with Polysilicon Depletion Effect for MOS Device

Abebe, Henok; Morris, Hedley C.; Cumberbatch, E.; Tyree, V.C.

doi:10.5573/jsts.2007.7.3.209

Cited by 11 publications

(2 citation statements)

References 9 publications

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“…At this point, the derivation of the proposed models will be given by starting from finding the analytical expression of 𝐶 𝑔 and 𝑓 𝑇 . Similar to [18], 𝐶 𝑔 can be defined as [22]…”

Section: The Proposed Modelsmentioning

confidence: 99%

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Novel Complete Probabilistic Models of Random Variation in High Frequency Performance of Nanoscale MOSFET

Banchuin

2013

Journal of Electrical and Computer Engineering

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The novel probabilistic models of the random variations in nanoscale MOSFET's high frequency performance defined in terms of gate capacitance and transition frequency have been proposed. As the transition frequency variation has also been considered, the proposed models are considered as complete unlike the previous one which take only the gate capacitance variation into account. The proposed models have been found to be both analytic and physical level oriented as they are the precise mathematical expressions in terms of physical parameters. Since the up-to-date model of variation in MOSFET's characteristic induced by physical level fluctuation has been used, part of the proposed models for gate capacitance is more accurate and physical level oriented than its predecessor. The proposed models have been verified based on the 65 nm CMOS technology by using the Monte-Carlo SPICE simulations of benchmark circuits and Kolmogorov-Smirnov tests as highly accurate since they fit the Monte-Carlo-based analysis results with 99% confidence. Hence, these novel models have been found to be versatile for the statistical/variability aware analysis/design of nanoscale MOSFET-based analog/mixed signal circuits and systems.

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Section: The Proposed Modelsmentioning

confidence: 99%

“…where 𝑄 𝑔 denotes the gate charge [22] which can be determined as a function of 𝐼 𝑑 by using the approach adopted from [23] as…”

Section: The Proposed Modelsmentioning

confidence: 99%

Novel Complete Probabilistic Models of Random Variation in High Frequency Performance of Nanoscale MOSFET

Banchuin

2013

Journal of Electrical and Computer Engineering

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Machine learning for analyzing and characterizing InAsSb-based nBn photodetectors

Glasmann

Kyrtsos

Bellotti

2020

Mach. Learn.: Sci. Technol.

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This paper discusses two cases of applying artificial neural networks to the capacitance–voltage characteristics of InAsSb-based barrier infrared detectors. In the first case, we discuss a methodology for training a fully-connected feedforward network to predict the capacitance of the device as a function of the absorber, barrier, and contact doping densities, the barrier thickness, and the applied voltage. We verify the model’s performance with physics-based justification of trends observed in single parameter sweeps, partial dependence plots, and two examples of gradient-based sensitivity analysis. The second case focuses on the development of a convolutional neural network that addresses the inverse problem, where a capacitance–voltage profile is used to predict the architectural properties of the device. The advantage of this approach is a more comprehensive characterization of a device by capacitance–voltage profiling than may be possible with other techniques. Finally, both approaches are material and device agnostic, and can be applied to other semiconductor device characteristics.

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Analytical analysis and modelling of variation in gate capacitance of subthreshold MOSFET

Banchuin

Chaisricharoen

2014

The 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICT

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Compact Gate Capacitance Model with Polysilicon Depletion Effect for MOS Device

Cited by 11 publications

References 9 publications

Novel Complete Probabilistic Models of Random Variation in High Frequency Performance of Nanoscale MOSFET

Novel Complete Probabilistic Models of Random Variation in High Frequency Performance of Nanoscale MOSFET

Machine learning for analyzing and characterizing InAsSb-based nBn photodetectors

Analytical analysis and modelling of variation in gate capacitance of subthreshold MOSFET

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