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

2025-09-24

International Conference on Simulation of Semiconductor Processes and Devices

AI Summary

This work addresses the computational bottleneck in high-accuracy quantum transport simulations of nanoscale devices comprising thousands of atoms under nonequilibrium conditions. By integrating density functional theory (DFT) with the nonequilibrium Green’s function (NEGF) formalism, the study introduces graph neural networks and high-performance parallel algorithms to efficiently model electron–phonon, photon, and electron–electron scattering mechanisms. Through algorithmic innovations and machine learning acceleration, the approach substantially reduces computational cost while preserving first-principles accuracy. This advancement enables, for the first time, scalable DFT+NEGF simulations of realistically sized nanodevices, thereby bridging the gap between ab initio quantum transport modeling and practical applications.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Sampling/Simulation-based Search
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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.

Problem

• NEGF
• quantum transport
• ab-initio simulation
• nanoscale devices
• computational acceleration

Innovation

• NEGF
• DFT
• graph neural networks
• machine learning
• parallelization