Revealing Phonon Bridge Effect for Amorphous vs Crystalline Metal–Silicide Layers at Si/Ti Interfaces by a Machine Learning Potential

Metal–semiconductor interfaces play a central role in micro- and nanoelectronic devices, as heat dissipation or temperature drop across these interfaces can significantly affect device performance. Prediction of accurate thermal boundary resistance (TBR) across these interfaces, considering realistic structures and their correlation with underlying thermal transport, remains challenging. In this work, we develop a unified Neuroevolution Potential (NEP) for the Si–Ti system that accurately reproduces energies, forces, and phonon properties of bulk Si, Ti, and TiSi2 and extends naturally to interfacial environments to analyze interfacial transport. An important development over current machine-learned interatomic potentials is the capability to model complex structures at metal–semiconductor interfaces, as the NEP enables large-scale nonequilibrium molecular dynamics simulations of epitaxial Si/Ti interfaces to elucidate the effect of amorphous or crystalline silicide interfacial layers. Simulated TBRs show excellent agreement with our time-domain thermoreflectance (TDTR) measurements, validating the robustness of our predictions. Spectral analyses reveal that the amorphous TiSi2 interfacial layer helps in efficient interfacial transport when the thickness is less than 1.5 nm compared to the crystalline TiSi2 layer, due to the high spectral conductance in the 3–6 THz frequency range and also due to the opening of channels for anharmonic transport, but this trend reverses when the interfacial layer thickness increases beyond 1.5 nm. Comparison of TBRs at the Si/TiSi2 interface for different crystalline phases of TiSi2 establishes that the C54 phase has reduced TBR compared to the C49 phase, which is correlated with the difference in their phonon density of states (PDOS) overlap with Si. These results provide atomistic insight into the role of crystalline versus amorphous silicides in interfacial heat transport and demonstrate a transferable machine-learned potential for studying heat dissipation in advanced semiconductor devices.