Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter

The rising power density of advanced electronics demands improved thermal management, while traditional single-scale methods are unable to fully reveal the complex heat transfer mechanisms in heterostructures. This work establishes a multiscale simulation framework by constructing a machine learning potential, enabling accurate cross-scale parameter transfer from atomic to mesoscopic and then to macroscopic levels. Results show that the thermal boundary resistance (TBR) at the β-Ga2O3/diamond interface is higher than that at the β-Ga2O3/Si and β-Ga2O3/SiC interfaces, and that the TBR decreases with increasing temperature, which contradicts conventional understanding. Vibrational density of states and interface conductance modal analysis elucidate the underlying mechanisms. These mesoscale insights are incorporated into macroscopic simulations, showing the β-Ga2O3/diamond heterostructure’s peak power capability reaches 226% of that of β-Ga2O3/Si. Further analysis reveals that although the thermal conductivity of the heat-spreading substrate remains the dominant factor in overall thermal performance, the thermal bottleneck gradually shifts toward the interface as both substrate conductivity and operating temperature rise. Moreover, crystal orientation significantly influences thermal performance and thermal stress distribution, necessitating careful trade-offs. This study not only provides effective strategies for optimizing β-Ga2O3-based devices but also establishes a generalizable paradigm for cross-scale thermal management research in heterogeneous material systems.