Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates

TL;DR

This paper uses advanced machine learning potentials to simulate heat transfer at the BAs-GaN interface, revealing high interfacial thermal conductance and insights into grain size effects, aiding thermal management in high-power electronics.

Contribution

It introduces a multiscale simulation approach with deep potential machine learning models to analyze heat transfer in BAs-GaN heterostructures, a novel method for thermal interface analysis.

Findings

Achieved ultrahigh interfacial thermal conductance of 265 MW/m^2K.

Revealed the impact of grain size on boundary resistance.

Demonstrated the potential of BAs substrates for thermal management in electronics.

Abstract

High-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m-1K-1) cooling substrates into the wide-bandgap semiconductor of gallium nitride (GaN) devices has been realized. However, the lack of systematic analysis on the heat transfer across the BAs-GaN interface hampers the practical applications. In this study, by constructing the accurate and high-efficient machine learning interatomic potentials, we performed multiscale simulations of the BAs-GaN heterostructures. Ultrahigh interfacial thermal conductance (ITC) of 265 MW m-2K-1 is achieved, which lies in the well-matched lattice vibrations of BAs and GaN. Moreover, the competition between grain size and boundary resistance was revealed with size increasing from 1 nm to 100 {\mu}m. Such deep-potential equipped multiscale simulations not only promote the practical applications of BAs cooling substrates in electronics, but also offer new approach for designing advanced thermal management systems.