RT Journal Article
T1 An accurate machine learning model to study the impact of realistic metal grain granularity on Nanosheet FETs
A1 García Fernández, Julián
A1 Seoane Iglesias, Natalia
A1 Comesaña Figueroa, Enrique
A1 Pichel Campos, Juan Carlos
A1 García Loureiro, Antonio Jesús
K1 Machine learning
K1 TCAD
K1 Nanosheet FET
K1 Metal grain granularity
K1 Variability
AB In this work, we present a machine learning neural network model to predict the impact of realistic metal grain granularity (MGG) variability on the threshold voltage V Th and on the ID -VG characteristics of a silicon-based 12 nm gate length nanosheet FET. This model is based on the multi-layer perceptron (MLP) machine learning architecture. As realistic MGG maps consist of the distribution of grains on the gate with different work-function values, it is relevant to apply algorithms such as the principal component analysis to reduce these features to the most representative ones. Once the realistic MGG features are correctly reduced without losing information, we train two different neural networks with the neurons in the output layer as the only difference, to predict the VTh and the ID - VG characteristics, respectively. The comparison between TCAD results and the model, shows excellent agreement for the mean and standard deviation of VTh distributions for different average grain sizes values (from 3 nm to 10 nm) demonstrating the accuracy of the machine learning model. Also, we study the amount of data needed to accurately train the MLPs, leading to results that allow us to drastically reduce the computational time required to perform variability studies for state-of-art nano FET devices
PB Elsevier
SN 0038-1101
YR 2023
FD 2023-07-22
LK http://hdl.handle.net/10347/31273
UL http://hdl.handle.net/10347/31273
LA eng
NO Solid-State Electronics 207 (2023) 108710
DS Minerva
RD 28 abr 2026