Fully Convolutional Generative Machine Learning Method for Accelerating Non-Equilibrium Greens Function Simulations

TL;DR

This paper introduces ML-NEGF, a machine learning-enhanced simulation method that accelerates non-equilibrium Greens function calculations in nano-electronics by 60% without sacrificing accuracy.

Contribution

The paper presents a novel convolutional generative network extension integrated with NEGF simulations, significantly improving convergence speed in device modeling.

Findings

Achieves 60% faster convergence compared to standard NEGF.

Effectively learns device physics, maintaining accuracy.

Reduces computational time in nano-electronics simulations.

Abstract

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.