Physics Guided Machine Learning Design of Phonon Bridged Transition Layers for Room Temperature Heterogeneous Integration

Interfacial thermal resistance severely bottlenecks wide bandgap semiconductor heterogeneous integration. Traditional high temperature processes induce severe residual stresses, making the customization of room temperature bonded thermal transition layers a critical challenge. To transcend these limits, we propose a synergistic end to end workflow bridging physics inspired machine learning with precision experimental fabrication. A physics guided artificial neural network maps the amorphous interlayer stoichiometry to its phonon transport mechanisms. This data driven screening reveals a thickness driven transition: maximizing phonon spectral overlap is paramount at ultrathin scales, whereas an elevated Debye temperature dominates thicker regimes. Experimentally, an in situ target assisted surface activated bonding (iTA‐SAB) technique achieves highly controlled room temperature deposition of these optimal designs for Si/SiC and GaN/SiC heterostructures. Rigorous computational evaluations confirm these customized interfaces effectively mitigate the amorphous thermal penalty, with predicted thermal resistances approaching the theoretical thermal resistance of a direct interface without any interlayer. Furthermore, this room temperature process fundamentally circumvents macroscopic residual stresses associated with high temperature epitaxy. Ultimately, this workflow shifts heterogeneous integration from empirical trial and error to rational data driven engineering.