Electronic Coupling and Charge-Transfer Landscape of Graphene on Ge(001)/Si(001): Multiscale Analysis Assisted by Machine Learning
Understanding and controlling charge transfer at graphene-semiconductor interfaces is essential for the integration of two-dimensional materials into silicon-compatible technologies. Here, we combine ultraviolet photoelectron spectroscopy (UPS), Kelvin probe force microscopy (KPFM), angle-resolved photoemission spectroscopy (ARPES), scanning tunneling spectroscopy (STS) and density functional theory (DFT) to resolve work-function modulation and electronic coupling in graphene grown on Ge(001)/Si(001). UPS and KPFM reveal a spatially non-uniform work-function landscape correlated with the nanofaceted morphology of the substrate. ARPES and DFT calculations for the pristine interface consistently indicate n-type doping and electron transfer from Ge to graphene. In contrast, modeling of the oxidized interface predicts a reversal to p-type doping, providing a plausible explanation for the different doping polarities reported in the literature. Machine-learning-assisted classification of the STS data resolves distinct local electronic regimes, ranging from nearly free-standing graphene on nanofacet tops to more strongly coupled inter-facet regions and nanoribbon-like regions with distinct local electronic responses. By identifying the mechanisms governing local graphene-substrate interactions and charge transfer, our study provides guidelines for tailoring the synthesis process and minimizing defect formation during delamination. These insights support the production of high-quality graphene layers for electronic devices and for transfer as protective coatings for air-sensitive materials.