García Fernández, JuliánSeoane Iglesias, NataliaComesaña Figueroa, EnriquePichel Campos, Juan CarlosGarcía Loureiro, Antonio Jesús2023-11-142023-11-142023-07-22Solid-State Electronics 207 (2023) 1087100038-1101http://hdl.handle.net/10347/31273In 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 deviceseng© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly citedAtribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/Machine learningTCADNanosheet FETMetal grain granularityVariabilityjournal article10.1016/j.sse.2023.108710open access