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

ß-Ga2O3 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 ß-Ga2O3-based devices. A promising solution for this problem is to integrate ß-Ga2O3 with a diamond heat sink. However, the thermal properties of the ß-Ga2O3/diamond heterostructures after the interfacial bonding have not been studied extensively, which are influenced by the crystal orientations and interfacial atoms for the ß-Ga2O3 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-atom-dependent thermal boundary resistance (TBR) of the ß-Ga2O3/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 ß-Ga2O3/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 ß-Ga2O3/diamond heterostructures according to guidance in the selection of the crystal orientations and interfacial atoms of the ß-Ga2O3 and diamond interfaces.

Interfacial Optimization for AlN/Diamond Heterostructures via Machine Learning Potential Molecular Dynamics Investigation of the Mechanical Properties.

AlN/diamond heterostructures hold tremendous promise for the development of next-generation high-power electronic devices due to their ultrawide band gaps and other exceptional properties. However, the poor adhesion at the AlN/diamond interface is a significant challenge that will lead to film delamination and device performance degradation. In this study, the uniaxial tensile failure of the AlN/diamond heterogeneous interfaces was investigated by molecular dynamics simulations based on a neuroevolutionary machine learning potential (NEP) model. The interatomic interactions can be successfully described by trained NEP, the reliability of which has been demonstrated by the prediction of the cleavage planes of AlN and diamond. It can be revealed that the annealing treatment can reduce the total potential energy by enhancing the binding of the C and N atoms at interfaces. The strain engineering of AlN also has an important impact on the mechanical properties of the interface. Furthermore, the influence of the surface roughness and interfacial nanostructures on the AlN/diamond heterostructures has been considered. It can be indicated that the combination of surface roughness reduction, AlN strain engineering, and annealing treatment can effectively result in superior and more stable interfacial mechanical properties, which can provide a promising solution to the optimization of mechanical properties, of ultrawide band gap semiconductor heterostructures.