2021
DOI: 10.3390/electronics10182219
|View full text |Cite
|
Sign up to set email alerts
|

Physics-Informed Neural Network for High Frequency Noise Performance in Quasi-Ballistic MOSFETs

Abstract: A physics-informed neural network (PINN) model is presented to predict the nonlinear characteristics of high frequency (HF) noise performance in quasi-ballistic MOSFETs. The PINN model is formulated by combining the radial basis function-artificial neural networks (RBF-ANNs) with an improved noise equivalent circuit model, including all the noise sources. The RBF-ANNs are utilized to model the thermal channel noise, induced gate noise, correlation noise, as well as the shot noise, due to the gate and source-dr… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2021
2021
2024
2024

Publication Types

Select...
4
1

Relationship

0
5

Authors

Journals

citations
Cited by 5 publications
(2 citation statements)
references
References 45 publications
0
2
0
Order By: Relevance
“…Upon the proposed PINN framework, various PINNs designed for disparate engineering applications have emerged. Perhaps their most renowned use is in predicting fluid fields [25,26], but other notable uses include electronics applications [27,28]. Notably, there have been a few attempts to use the PINN-based method to learn and predict nonlinear dynamical systems and chaos [29][30][31][32], with a notable good attempt by Antonelo et al [33] to modify PINN to adjust systematic controls based on the predictions of PINN.…”
Section: Introductionmentioning
confidence: 99%
“…Upon the proposed PINN framework, various PINNs designed for disparate engineering applications have emerged. Perhaps their most renowned use is in predicting fluid fields [25,26], but other notable uses include electronics applications [27,28]. Notably, there have been a few attempts to use the PINN-based method to learn and predict nonlinear dynamical systems and chaos [29][30][31][32], with a notable good attempt by Antonelo et al [33] to modify PINN to adjust systematic controls based on the predictions of PINN.…”
Section: Introductionmentioning
confidence: 99%
“…One of the celebrated characteristics of PINNs is they can learn from sparse data [24] as physics doesn't generate humongous data as easily in other commercial fields. Upon the proposed PINN framework, various types of PINNs designed for different engineering applications emerge in fields, with their most renowned works in predicting fluid fields [25,26], but also include electronics [28,29]. Henceforth a question arose: can PINN be applied for dynamical systems?…”
Section: Inductormentioning
confidence: 99%