Machine learning: next promising trend for microplastics study
Microplastics (MPs), as an emerging pollutant, pose a significant threat to humans and
ecosystems. However, traditional MPs characterization methods are limited by sample …
ecosystems. However, traditional MPs characterization methods are limited by sample …
Prediction of FinFET current-voltage and capacitance-voltage curves using machine learning with autoencoder
In this letter, we demonstrated the possibility of predicting full transistor current-voltage (IV)
and capacitance-voltage (CV) curves using machines trained by Technology Computer …
and capacitance-voltage (CV) curves using machines trained by Technology Computer …
[HTML][HTML] Overview of emerging semiconductor device model methodologies: From device physics to machine learning engines
Advancements in the semiconductor industry introduce novel channel materials, device
structures, and integration methods, leading to intricate physics challenges when …
structures, and integration methods, leading to intricate physics challenges when …
TCAD-augmented machine learning with and without domain expertise
H Dhillon, K Mehta, M ** profile of stacked nanosheet transistors
Complex nonlinear dependence of ultra-scaled transistor performance on its channel
geometry and source/drain (S/D) do** profile bring obstacles in the advanced technology …
geometry and source/drain (S/D) do** profile bring obstacles in the advanced technology …
Acceleration of semiconductor device simulation with approximate solutions predicted by trained neural networks
In order to accelerate the semiconductor device simulation, we propose to use a neural
network to learn an approximate solution for desired bias conditions. With an initial solution …
network to learn an approximate solution for desired bias conditions. With an initial solution …
A machine learning approach for optimizing and accurate prediction of performance parameters for stacked nanosheet transistor
In this article, the possibilities of accurate prediction of wide range of parameters and
optimizing the same through machine learning (ML) approach have been demonstrated for …
optimizing the same through machine learning (ML) approach have been demonstrated for …
TCAD simulation models, parameters, and methodologies for β-Ga2O3 power devices
HY Wong - ECS Journal of Solid State Science and Technology, 2023 - iopscience.iop.org
Abstract β-Ga 2 O 3 is an emerging material and has the potential to revolutionize power
electronics due to its ultra-wide-bandgap (UWBG) and lower native substrate cost compared …
electronics due to its ultra-wide-bandgap (UWBG) and lower native substrate cost compared …
Rapid MOSFET contact resistance extraction from circuit using SPICE-augmented machine learning without feature extraction
T Lu, V Kanchi, K Mehta, S Oza, T Ho… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
It is desirable to monitor the degradation of integrated circuits (ICs) or perform their failure
analysis through their electrical characteristics [such as the voltage-transfer characteristic …
analysis through their electrical characteristics [such as the voltage-transfer characteristic …
Out-of-training-range Synthetic FinFET and Inverter Data Generation using a Modified Generative Adversarial Network
In this letter, a novel variation of Generative Adversarial Network (GAN) is proposed and
used to predict device and circuit characteristics based on design parameters. Unlike …
used to predict device and circuit characteristics based on design parameters. Unlike …