[HTML][HTML] Machine learning for semiconductors
DY Liu, LM Xu, XM Lin, X Wei, WJ Yu, Y Wang, ZM Wei - Chip, 2022 - Elsevier
Thanks to the increasingly high standard of electronics, the semiconductor material science
and semiconductor manufacturing have been booming in the last few decades, with massive …
and semiconductor manufacturing have been booming in the last few decades, with massive …
[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 …
Superior printed parts using history and augmented machine learning
Abstract Machine learning algorithms are a natural fit for printing fully dense superior
metallic parts since 3D printing embodies digital technology like no other manufacturing …
metallic parts since 3D printing embodies digital technology like no other manufacturing …
A machine learning study on superlattice electron blocking layer design for AlGaN deep ultraviolet light-emitting diodes using the stacked XGBoost/LightGBM …
Aluminium gallium nitride (AlGaN)-based deep ultraviolet (DUV) light-emitting diodes
(LEDs) suffer from low internal quantum efficiency (IQE) and serious efficiency droop. One …
(LEDs) suffer from low internal quantum efficiency (IQE) and serious efficiency droop. One …
A machine learning approach for optimization of channel geometry and source/drain doping profile of stacked nanosheet transistors
Complex nonlinear dependence of ultra-scaled transistor performance on its channel
geometry and source/drain (S/D) doping profile bring obstacles in the advanced technology …
geometry and source/drain (S/D) doping profile bring obstacles in the advanced technology …
Machine learning-based device modeling and performance optimization for FinFETs
This brief introduces a machine learning based framework to model FinFET's IV and CV
curves with artificial neural networks and to further optimize FinFET's performance on DC …
curves with artificial neural networks and to further optimize FinFET's performance on DC …
Tcad device simulation with graph neural network
There is an increasing number of studies to accelerate the TCAD simulation with deep
learning models. Such studies rely on performing a procedure that interpolates an …
learning models. Such studies rely on performing a procedure that interpolates an …
Using machine learning with optical profilometry for GaN wafer screening
To improve the manufacturing process of GaN wafers, inexpensive wafer screening
techniques are required to both provide feedback to the manufacturing process and prevent …
techniques are required to both provide feedback to the manufacturing process and prevent …
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 …