Significant impact of Al1-xGaxN interlayer on GaN/AlN thermal boundary conductance

TL;DR

This study uses first-principles-based deep learning potentials to evaluate how AlGaN interlayers affect thermal boundary conductance in GaN/AlN heterostructures, revealing that certain interlayer configurations can significantly enhance or degrade heat transfer.

Contribution

It introduces a first-principles-based deep learning approach to accurately evaluate TBC in AlGaN interlayer systems, revealing the impact of alloy composition transitions.

Findings

AlGaN interlayer degrades TBC between GaN and AlN.

Sigmoidally transitioning Al composition increases TBC.

Deep learning potentials enable accurate TBC predictions.

Abstract

AlN-GaN heterostructures are central to high-power and high-frequency electronics, including RF devices, power converters, and AI accelerators. An intermediate Al1-xGaxN (AlGaN) layer is often present, either unintentionally during growth or intentionally to induce a 2D electron gas, yet its impact on the interfacial thermal boundary conductance (TBC) remains unknown due to the lack of reliable measurement or modeling methods. Here, we report a first principles-based evaluation of the TBCs of AlN-AlGaN, AlGaN-GaN, and AlN-AlGaN-GaN interfaces over the full alloy range. This is realized by the development of accurate deep learning interatomic potentials based on first-principles simulations. Contrary to other material systems where mixed interlayers enhance thermal coupling, we find that an AlGaN interlayer markedly degrades TBC between GaN and AlN, explaining the observation in experiments. Finally, we show that if the Al composition is sigmoidally transitioned from 0 to 1 across the AlN-GaN interface, it can remarkably increase the TBC, compared to an abrupt or a linear transition. This work is expected to shed light on an accurate thermal analysis and electro-thermal co-design of future AlGaN-based devices.