Mapping phonon dynamics to thermal transport
            <i>via</i>
            deep-learning NEMD: AlN/diamond interface engineering for GaN heat dissipation

Kongping Wu; Meiyong Liao

doi:10.1080/26941112.2025.2561987

Article Mapping phonon dynamics to thermal transport via deep-learning NEMD: AlN/diamond interface engineering for GaN heat dissipation

Kongping Wu ; Meiyong Liao

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Kongping Wu, Meiyong Liao. Mapping phonon dynamics to thermal transport via deep-learning NEMD: AlN/diamond interface engineering for GaN heat dissipation. Functional Diamond. 2025, 5 (1), 2561987. https://doi.org/10.1080/26941112.2025.2561987

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Diamond offers excellent heat sink for high power high-electron-mobility transistors based on III-nitrides. However, the GaN/diamond interfaces suffer from low thermal conductance due to phonon mismatch. Although AlN interlayers can mitigate this issue, processing-induced carbon vacancies and subsurface disorder near the AlN/diamond interface recreate a new thermal bottleneck. In this study, we employ deep learning-enhanced non-equilibrium molecular dynamics (NEMD) simulations to investigate atomic-scale thermal transport across AlN/diamond interfaces, with a particular focus on quantifying the impact of carbon vacancies. Results show interfacial thermal conductance (ITC) for AlN-Al/C(1 1 1) depends non-monotonically on the carbon vacancy concentration. The ITC peaks at 151.7 MW·m−2K−1 at a carbon vacancy concentration of 2.4% due to the formation of resonant vibrational states that bridge the phonon gap and promote phonon delocalization, enabling efficient tunneling across the interface. However, beyond this optimal concentration, vacancy clustering induces destructive phonon interference, strong scattering, and severe localization effects, leading to a sharp decline in ITC. This work provides a pathway for optimizing AlN thermal bridges to achieve low thermal resistance in GaN-on-diamond devices through precise control of vacancy concentration and crystallinity.

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