This work describes a novel simulation approach that combines machine learning and device modelling simulations. The device simulations are based on the quantum mechanical non-equilibrium Greens function (NEGF) approach and the machine learning method is an extension to a convolutional generative network. We have named our new simulation approach ML-NEGF and we have implemented it in our in-house simulator called NESS (nano-electronics simulations software). The reported results demonstrate the improved convergence speed of the ML-NEGF method in comparison to the standard NEGF approach. The trained ML model effectively learns the underlying physics of nano-sheet transistor behaviour, resulting in faster convergence of the coupled Poisson-NEGF simulations. Quantitatively, our ML- NEGF approach achieves an average convergence acceleration of 60%, substantially reducing the computational time while maintaining the same accuracy.
@article{arxiv.2309.09374,
title = {Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Greens Function Simulations},
author = {Preslav Aleksandrov and Ali Rezaei and Nikolas Xeni and Tapas Dutta and Asen Asenov and Vihar Georgiev},
journal= {arXiv preprint arXiv:2309.09374},
year = {2023}
}