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 …
A review on machine learning approaches for predicting the effect of device parameters on performance of nanoscale MOSFETs
R Ghoshhajra, K Biswas… - 2021 Devices for Integrated …, 2021 - ieeexplore.ieee.org
This review investigates the possibility of using Machine Learning as a replacement for
numerical TCAD device simulation. As the chip design is getting complex to incorporate …
numerical TCAD device simulation. As the chip design is getting complex to incorporate …
TCAD-augmented machine learning with and without domain expertise
In this article, using experimental data, we demonstrate that the technology computer-aided
design (TCAD) is a very cost-effective tool to generate the data to build machine learning …
design (TCAD) is a very cost-effective tool to generate the data to build machine learning …
Low thermal resistance (0.5 K/W) Ga₂O₃ Schottky rectifiers with double-side packaging
The low thermal conductivity of Ga 2 O 3 has arguably been the most serious concern for Ga
2 O 3 power and RF devices. Despite many simulation studies, there is no experimental …
2 O 3 power and RF devices. Despite many simulation studies, there is no experimental …
Application of noise to avoid overfitting in TCAD augmented machine learning
In this paper, we propose and study the use of noise to avoid the overfitting issue in
Technology Computer-Aided Design-augmented machine learning (TCAD-ML). TCAD-ML …
Technology Computer-Aided Design-augmented machine learning (TCAD-ML). TCAD-ML …
A machine learning approach to modeling intrinsic parameter fluctuation of gate-all-around Si nanosheet MOSFETs
The sensitivity of semiconductor devices to any microscopic perturbation is increasing with
the continuous shrinking of device technology. Even the small fluctuations have become …
the continuous shrinking of device technology. Even the small fluctuations have become …
Framework for TCAD augmented machine learning on multi-I–V characteristics using convolutional neural network and multiprocessing
In a world where data is increasingly important for making breakthroughs, microelectronics
is a field where data is sparse and hard to acquire. Only a few entities have the infrastructure …
is a field where data is sparse and hard to acquire. Only a few entities have the infrastructure …
Analytical Model and Structure of the Multilayer Enhancement-Mode β-Ga2O3 Planar MOSFETs
L Guo, S Luan, H Zhang, L Yuan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
In this article, the planar-Ga 2 O 3 junctionless transistors with/without unintentional doping
(UID) buffer layer are numerically investigated. It was found that the double-layer Ga 2 O 3 …
(UID) buffer layer are numerically investigated. It was found that the double-layer Ga 2 O 3 …
Device performance prediction of nanoscale junctionless FinFET using MISO artificial neural network
This paper investigates the way to use Multi-layer neural network as a possible replacement
of numerical TCAD device simulation to study device characteristics using limited …
of numerical TCAD device simulation to study device characteristics using limited …
[HTML][HTML] 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 …