Abstract
Understanding the ideal limit of interfacial thermal conductance (ITC) across semiconductor heterointerfaces is crucial for optimizing heat dissipation in practical applications. By employing a highly accurate and efficient machine-learned potential trained herein, we perform extensive non-equilibrium molecular dynamics simulations to investigate the ITC of diamond/cubic boron nitride (cBN) interfaces. The diamond/cBN interface attains an ultrahigh ITC on the order of ∼10 GW/(m2 K), placing it among the highest values reported for heterostructure interfaces. This exceptional conductance originates from extended phonon modes due to acoustic matching and localized C-atom modes that propagate through B-C bonds. However, atomic diffusion across the ideal interface creates mixing layers that disrupt these characteristic phonon modes, substantially suppressing the thermal transport from its ideal limit. Our findings reveal how interface phonon modes govern thermal transport across diamond/cBN interfaces, providing insights for thermal management in semiconductor devices.
| Original language | English |
|---|---|
| Article number | 129245 |
| Journal | International Journal of Heat and Mass Transfer |
| Volume | 271 |
| DOIs | |
| State | Published - 15 Dec 2026 |
| Externally published | Yes |
Keywords
- Diamond/cubic boron nitride interface
- Interfacial thermal conductance
- Machine Learning
- molecular dynamics simulations
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