Artificial neural network-based compact modeling methodology for advanced transistors
The artificial neural network (ANN)-based compact modeling methodology is evaluated in
the context of advanced field-effect transistor (FET) modeling for Design-Technology …
the context of advanced field-effect transistor (FET) modeling for Design-Technology …
[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 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 …
A physical-based artificial neural networks compact modeling framework for emerging FETs
We report a compact modeling framework based on the Grove–Frohman (GF) model and
artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The …
artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The …
A Novel Prediction Technology of Output Characteristics for IGBT Based on Compact Model and Artificial Neural Networks
The output characteristics of the insulated gate bipolar transistor (IGBT) are the critical metric
for the measurement of power control and conversion of power electronic systems. Existing …
for the measurement of power control and conversion of power electronic systems. Existing …
Transistor compact model based on multigradient neural network and its application in SPICE circuit simulations for gate-all-around Si cold source FETs
Q Yang, G Qi, W Gan, Z Wu, H Yin… - … on Electron Devices, 2021 - ieeexplore.ieee.org
Transistor compact model (TCM) is the key bridge between process technology and circuit
design. Typically, TCM is desired to capture the nonlinear device electronic characteristics …
design. Typically, TCM is desired to capture the nonlinear device electronic characteristics …
Compact models for initial MOSFET sizing based on higher-order artificial neural networks
H Habal, D Tsonev, M Schweikardt - Proceedings of the 2020 ACM/IEEE …, 2020 - dl.acm.org
Simple MOSFET models intended for hand analysis are inaccurate in deep sub-micrometer
process technologies and in the moderate inversion region of device operation. Accurate …
process technologies and in the moderate inversion region of device operation. Accurate …
A Comprehensive Technique based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nonosheet Transistors
Machine learning (ML) is poised to play an important part in advancing the predicting
capability in semiconductor device compact modeling domain. One major advantage of ML …
capability in semiconductor device compact modeling domain. One major advantage of ML …
Optimization of deep learning-based bsim-cmg iv parameter extraction in seconds
A deep learning-based parameter extraction for industry standard BSIM-CMG compact
model is presented in this paper. A Monte-Carlo simulation varying key BSIM-CMG …
model is presented in this paper. A Monte-Carlo simulation varying key BSIM-CMG …
Machine learning-assisted device modeling with process variations for advanced technology
Y Lyu, W Chen, M Zheng, B Yin, J Li… - IEEE Journal of the …, 2023 - ieeexplore.ieee.org
Process variations (PV), including global variation (GV) and local variation (LV), have
become one of the major issues in advanced technologies, which is crucial for circuit …
become one of the major issues in advanced technologies, which is crucial for circuit …