Acceleration of Atomistic NEGF: Algorithms, Parallelization, and Machine Learning

TL;DR

This paper reviews advances in NEGF simulations for nanoscale devices, highlighting algorithmic improvements, parallel computing, and the potential of machine learning to accelerate ab-initio quantum transport calculations.

Contribution

It presents new algorithms and parallelization strategies for NEGF, and explores integrating machine learning to enhance simulation efficiency for realistic nanoscale systems.

Findings

Enhanced algorithms enable larger system simulations.

Parallelization significantly reduces computation time.

Machine learning approaches show promise for faster ab-initio calculations.

Abstract

The Non-equilibrium Green's function (NEGF) formalism is a particularly powerful method to simulate the quantum transport properties of nanoscale devices such as transistors, photo-diodes, or memory cells, in the ballistic limit of transport or in the presence of various scattering sources such as electronphonon, electron-photon, or even electron-electron interactions. The inclusion of all these mechanisms has been first demonstrated in small systems, composed of a few atoms, before being scaled up to larger structures made of thousands of atoms. Also, the accuracy of the models has kept improving, from empirical to fully ab-initio ones, e.g., density functional theory (DFT). This paper summarizes key (algorithmic) achievements that have allowed us to bring DFT+NEGF simulations closer to the dimensions and functionality of realistic systems. The possibility of leveraging graph neural networks and machine learning to speed up ab-initio device simulations is discussed as well.