Deep-learning-assisted physics-driven MOSFET current-voltage modeling
In this work, we propose using deep learning to improve the accuracy of the partially-physics-
based conventional MOSFET current-voltage model. The benefits of having some physics …
based conventional MOSFET current-voltage model. The benefits of having some physics …
[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 …
A Perspective View of Silicon Based Classical to Non-Classical MOS Transistors and their Extension in Machine Learning
Unprecedented growth in CMOS technology and demand of high-density integrated circuits
(ICs) in semiconductor industry has motivated to research community towards the …
(ICs) in semiconductor industry has motivated to research community towards the …
Deep learning-based BSIM-CMG parameter extraction for 10-nm FinFET
A new deep learning (DL)-based parameter extraction method is presented in this brief; 50k
training cases are generated by Monte Carlo simulations of these preselected parameters in …
training cases are generated by Monte Carlo simulations of these preselected parameters in …
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 …
Machine learning augmented compact modeling for simultaneous improvement in computational speed and accuracy
In this article, we have presented the use of prior physics knowledge-based artificial neural
networks (KBANNs) to improve the simulation speed and accuracy of compact models for …
networks (KBANNs) to improve the simulation speed and accuracy of compact models for …
Machine-learning-based compact modeling for sub-3-nm-node emerging transistors
SM Woo, HJ Jeong, JY Choi, HM Cho, JT Kong… - Electronics, 2022 - mdpi.com
In this paper, we present an artificial neural network (ANN)-based compact model to
evaluate the characteristics of a nanosheet field-effect transistor (NSFET), which has been …
evaluate the characteristics of a nanosheet field-effect transistor (NSFET), which has been …
Deep learning approach to inverse grain pattern of nanosized metal gate for multichannel gate-all-around silicon nanosheet MOSFETs
For the first time, a deep learning (DL) algorithm is presented to study the effect of the source
of variability on the performance of semiconductor nanodevice. This paper reports the …
of variability on the performance of semiconductor nanodevice. This paper reports the …