Results 1 to 10 of about 9,307 (224)

Improvement of TCAD Augmented Machine Learning Using Autoencoder for Semiconductor Variation Identification and Inverse Design

IEEE Access, 2020
A machine learning (ML) model by combing two autoencoders and one linear regression model is proposed to avoid overfitting and to improve the accuracy of Technology Computer-Aided Design (TCAD)-augmented ML for semiconductor structural variation ...
Kashyap Mehta, Sophia Susan Raju, Ming Xiao   +2 more
exaly   +5 more sources

Restructuring TCAD System: Teaching Traditional TCAD New Tricks

2021 IEEE International Electron Devices Meeting (IEDM), 2021
Traditional TCAD simulation has succeeded in predicting and optimizing the device performance; however, it still faces a massive challenge - a high computational cost. There have been many attempts to replace TCAD with deep learning, but it has not yet been completely replaced.
Sanghoon Myung, Wonik Jang, Seonghoon Jin, Jae Myung Choe, Changwook Jeong, Daesin Kim   +5 more
openaire   +3 more sources

TCAD Simulation of Novel Semiconductor Devices [PDF]

2021 IEEE 14th International Conference on ASIC (ASICON), 2021
Simulation of conventional and emerging electronic devices using Technology Computer Aided Design (TCAD) tools has been an essential part of the semiconductor industry as well as academic research. Computational efficiency and accuracy of the numerical modeling are the key criteria on which quality and usefulness of a TCAD tool are ascertained. Further,
Dutta, Tapas, Medina Bailon, Cristina, Rezaei, Ali, Nagy, Daniel, Adamu-Lema, Fikru, Xeni, Nikolas, Abourrig, Yassine, Kumar, Naveen, Georgiev, Vihar, Asenov, Asen   +9 more
openaire   +2 more sources

Prediction of Device Characteristics of Feedback Field-Effect Transistors Using TCAD-Augmented Machine Learning

Micromachines, 2023
In this study, the device characteristics of silicon nanowire feedback field-effect transistors were predicted using technology computer-aided design (TCAD)-augmented machine learning (TCAD-ML).
Sola Woo, Juhee Jeon, Sangsig Kim
exaly   +3 more sources

EPreNet: A Condition-Guided Network Accelerates Etching Profile Prediction [PDF]

Micromachines
Plasma etching is a critical step in semiconductor manufacturing, yet existing approaches are either computationally expensive or limited to predicting scalar etching metrics rather than full profile evolution.
Mengjiao Lu, Zerui Jin, Wanjun Wang, Jianghai He, Qingqing Liu   +4 more
doaj   +2 more sources

Predictive modeling of drain current in advanced FET architectures using ML-based TCAD calibration [PDF]

Scientific Reports
The design and optimization of advanced semiconductor devices, such as multi gate MOSFETs, play a critical role in the miniaturization process of integrated circuits and the advancement of modern electronics.
Siddhabrata Mohapatra, Roshni Shekhar, Aditya Jadhav, Biswajit Jena   +3 more
doaj   +2 more sources

Comparative Characterization of NWFET and FinFET Transistor Structures Using TCAD Modeling

Micromachines, 2022
A complete comparison for 14 nm FinFET and NWFET with stacked nanowires was carried out. The electrical and thermal performances in two device structures were analyzed based on TCAD simulation results.
Konstantin O Petrosyants
exaly   +3 more sources

The Modeling of a Single-Electron Bipolar Avalanche Transistor in 150 nm CMOS [PDF]

Sensors
This paper addresses the complex behavior of Single-Electron Bipolar Avalanche Transistors (SEBATs) through a comprehensive modeling approach. TCAD simulations were used to analyze the behavior of the device during avalanche pulses triggered by electron ...
Abderrezak Boughedda, Lucio Pancheri, Luca Parmesan, Leonardo Gasparini, Gabriele Quarta, Daniele Perenzoni, Matteo Perenzoni   +6 more
doaj   +2 more sources

Quasi-Ballistic Drift-Diffusion Simulation of SiGe Nanowire MOSFETs Using the Kinetic Velocity Model

IEEE Journal of the Electron Devices Society, 2021
This paper presents the calibration of the novel kinetic velocity model (KVM) in the drift-diffusion (DD) transport approach, which can account for the ballistic effect in short-channel devices.
Ko-Hsin Lee, Axel Erlebach, Oleg Penzin, Lee Smith   +3 more
doaj   +1 more source

TCAD - A Progressive Tool for Engineers

Radioengineering, 1993
The semiconductor industry is continuously striving to improve the performance of electron devices and circuits. It implies the need for better understanding of their basic behaviour.
I. Adamcik, P. Hazdra, J. Voves, J. Vobecky   +3 more
doaj   +2 more sources