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CMOS scaling for the 5 nm node and beyond: Device, process and technology
HH Radamson, Y Miao, Z Zhou, Z Wu, Z Kong, J Gao… - Nanomaterials, 2024 - mdpi.com
After more than five decades, Moore's Law for transistors is approaching the end of the
international technology roadmap of semiconductors (ITRS). The fate of complementary …
international technology roadmap of semiconductors (ITRS). The fate of complementary …
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 …
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 …
[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 …
Machine Learning Based Compact Model Design for Reconfigurable FETs
M Reuter, J Wilm, A Kramer… - IEEE Journal of the …, 2024 - ieeexplore.ieee.org
In integrated circuit design compact models are the abstraction layer which connects
semiconductor physics and circuit simulation. Established compact models like BSIM …
semiconductor physics and circuit simulation. Established compact models like BSIM …
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 …
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 …