Nagy, DanielIndalecio Fernández, GuillermoGarcía Loureiro, Antonio JesúsEspiñeira Deus, GabrielElmessary, Muhammad A.Kalna, KarolSeoane Iglesias, Natalia2020-05-012020-05-012019http://hdl.handle.net/10347/21967Variability of semiconductor devices is seriously limiting their performance at nanoscale. The impact of variability can be accurately and effectively predicted by computer-aided simulations in order to aid future device designs. Quantum corrected (QC) drift-diffusion (DD) simulations are usually employed to estimate the variability of state-of-the-art non-planar devices but require meticulous calibration. More accurate simulation methods, such as QC Monte Carlo (MC), are considered time consuming and elaborate. Therefore, we predict TiN metal gate work-function granularity (MGG) and line edge roughness (LER) induced variability on a 10-nm gate length gate-all-around Si nanowire FET and perform a rigorous comparison of the QC DD and MC results. In case of the MGG, we have found that the QC DD predicted variability can have a difference of up to 20% in comparison with the QC MC predicted one. In case of the LER, we demonstrate that the QC DD can overestimate the QC MC simulation produced variability by a significant error of up to 56%. This error between the simulation methods will vary with the root mean square (RMS) height and maximum source/drain $n$ -type doping. Our results indicate that the aforementioned QC DD simulation technique yields inaccurate results for the ON-current variabilityeng© The Author(s) 2019. Open Access. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/http://creativecommons.org/licenses/by/3.0/Drift-diffusionLine edge roughnessMetal gate granularityMonte CarloQuantum correctionsNanowire FETjournal article10.1109/ACCESS.2019.28925922169-3536open access