Insight into Interfacial Heat Transfer of β-Ga₂O₃/Diamond Heterostructures via the Machine Learning Potential

Abstract

β-Ga 2 O 3 is an ultrawide-band gap semiconductor with excellent potential for high-power and ultraviolet optoelectronic device applications. Low thermal conductivity is one of the major obstacles to enable the full performance of β-Ga 2 O 3 -based devices. A promising solution for this problem is to integrate β-Ga 2 O 3 with a diamond heat sink. However, the thermal properties of the β-Ga 2 O 3 /diamond heterostructures after the interfacial bonding have not been studied extensively, which are influenced by the crystal orientations and interfacial atoms for the β-Ga 2 O 3 and diamond interfaces. In this work, molecular dynamics simulations based on machine learning potential have been adopted to investigate the crystal-orientation-dependent and interfacial-atomdependent thermal boundary resistance (TBR) of the β-Ga 2 O 3 / diamond heterostructure after interfacial bonding. The differences in TBR at different interfaces are explained in detail through the explorations of thermal conductivity value, thermal conductivity spectra, vibration density of states, and interfacial structures. Based on the above explorations, a further understanding of the influence of different crystal orientations and interfacial atoms on the β-Ga 2 O 3 /diamond heterostructure was achieved. Finally, insightful optimization strategies have been proposed in the study, which could pave the way for better thermal design and management of β-Ga 2 O 3 /diamond heterostructures according to guidance in the selection of the crystal orientations and interfacial atoms of the β-Ga 2 O 3 and diamond interfaces. KEYWORDS: β-Ga 2 O 3 /diamond heterostructure, interfacial heat transfer, molecular dynamics, machine learning potential, thermal boundary resistance