Results 1 to 10 of about 24,792 (217)
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
exaly +3 more sources
IEEE Journal of the Electron Devices Society, 2020 This work, for the first time, experimentally demonstrates a TCAD-Machine Learning (TCAD-ML) framework to assist the analysis of device-to-device variation and operating (ambient) temperature without the need of physical quantities extraction.
Hiu Yung Wong, Ming Xiao, Boyan Wang
exaly +3 more sources
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 +2 more
exaly +3 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]
SensorsThis 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 +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 +3 more
doaj +1 more source
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.
Myung, Sanghoon +5 more
openaire +2 more sources
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 +3 more
doaj +2 more sources
Anales (Asociación Física Argentina), 2023 Ionizing radiation detection devices have been widely used in recent years in various applications and experimental fields, such as high energy physics, nuclear physics, and medical imaging.
N.E. Martín, M. Sofo Haro, M. Valente
doaj +1 more source
Material Modeling in Semiconductor Process Applications
Journal of Microelectronic Manufacturing, 2020 During the past decade, significant progress has been achieved in the application of material modeling to aid technology development in semiconductor manufacturing companies such as Intel.
Boris A. Voinov +4 more
doaj +1 more source