Ab initio Modeling of MoS2/Oxide Device Interfaces with Machine Learned Electronic Structures

2026-08-27

AI Summary

该研究通过结合机器学习电子结构模型和量子输运求解器，加速了对MoS2/氧化物界面设备的模拟，揭示了金属原子在界面附近对电流的影响。

Abstract

We introduce a new ab initio approach to simulate semiconductor devices that integrates scalable machine-learned (ML) electronic structure models with an advanced quantum transport (QT) solver. The developed framework enables 10,000X speedups over density functional theory to produce the Hamiltonian matrix of devices made of >20,000 atoms, while offering high prediction accuracy. We use its unique features to investigate MoS2/oxide samples and single-layer MoS2 field-effect transistors, where the surrounding oxide layers, here, HfO2 or Al2O3, are explicitly included into the QT domain. In particular, we reveal that the presence of undercoordinated metal atoms (Hf or Al) close to the semiconductor-oxide interface significantly affects the magnitude of the electronic current and its propagation through MoS2.

Problem

• ab initio modeling
• semiconductor devices
• machine learning
• quantum transport
• MoS2/oxide interfaces

Innovation

• machine-learned electronic structure models
• quantum transport solver
• speedups over density functional theory
• undercoordinated metal atoms
• MoS2/oxide interfaces