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 …
Fast and expandable ANN-based compact model and parameter extraction for emerging transistors
In this paper, we present a fast and expandable artificial neural network (ANN)-based
compact model and parameter extraction flow to replace the existing complicated compact …
compact model and parameter extraction flow to replace the existing complicated compact …
Machine learning-based device modeling and performance optimization for FinFETs
This brief introduces a machine learning based framework to model FinFET's IV and CV
curves with artificial neural networks and to further optimize FinFET's performance on DC …
curves with artificial neural networks and to further optimize FinFET's performance on DC …
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 …
Artificial neural network-based modeling for estimating the effects of various random fluctuations on dc/analog/rf characteristics of gaa si nanosheet fets
Advanced field-effect transistors (FETs), such as gate-all-around (GAA) nanowire (NW) and
nanosheet (NS) devices, have been highly scaled; therefore, they are critically affected even …
nanosheet (NS) devices, have been highly scaled; therefore, they are critically affected even …
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 …
Transfer learning approach to analyzing the work function fluctuation of gate-all-around silicon nanofin field-effect transistors
With the shrinking of technological nodes, analysis of nanosized-metal-grain pattern-
dependent devices is becoming critical; various machine learning (ML) approaches have …
dependent devices is becoming critical; various machine learning (ML) approaches have …
Application of long short-term memory modeling technique to predict process variation effects of stacked gate-all-around Si nanosheet complementary-field effect …
Emerging machine-learning (ML) methodology has been overcoming the challenging task of
analyzing the process variation effect of nanoscale devices using 3-D stochastic device …
analyzing the process variation effect of nanoscale devices using 3-D stochastic device …
Bayesian optimization of MOSFET devices using effective stop** condition
B Kim, M Shin - IEEE Access, 2021 - ieeexplore.ieee.org
Current nanometer-scale metal-oxide-semiconductor field-effect transistor (MOSFET)
devices exhibit short-channel, quantum, and self-heating effects, making modeling and …
devices exhibit short-channel, quantum, and self-heating effects, making modeling and …
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 …