Fully Convolutional Generative Machine Learning Method for Accelerating
  Non-Equilibrium Greens Function Simulations
Preslav Aleksandrov Ali Rezaei Nikolas Xeni Tapas Dutta Asen Asenov Vihar Georgiev
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.