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石墨烯在Ge(001)/Si(001)上的电子耦合与电荷转移景观:机器学习辅助的多尺度分析

Electronic Coupling and Charge-Transfer Landscape of Graphene on Ge(001)/Si(001): Multiscale Analysis Assisted by Machine Learning

Pawel Dabrowski, Przemysław Przybysz, Maciej Rogala, Iaroslav Lutsyk, Paweł Krukowski, Witold Kozłowski, Michał Piskorski, Piotr Milczarski, Iwona Pasternak, Jakub Sitek, Marek Kopciuszyński, Ryszard Zdyb, Jagoda Sławińska, Pawel J. Kowalczyk

arXiv 2609.37827首次发表:更新:

发表机构

University of Lodz; Zernike Institute for Advanced Materials, University of Groningen; Lodz University of Technology; Warsaw University of Technology; Maria Curie-Sklodowska University(罗兹大学; 格罗宁根大学泽尼克先进材料研究所; 罗兹理工大学; 华沙理工大学; 玛丽亚·居里-斯克隆多夫斯卡大学)

机构由 AI 辅助整理,请以论文原文为准。

AI 中文总结

本研究结合多种实验表征与DFT计算,并借助机器学习分类STS数据,揭示了石墨烯在Ge(001)/Si(001)上的电荷转移机制,为调控掺杂极性和优化石墨烯制备提供了指导。

AI 中文摘要

理解并控制石墨烯-半导体界面的电荷转移对于将二维材料集成到硅兼容技术中至关重要。本研究结合紫外光电子能谱(UPS)、开尔文探针力显微镜(KPFM)、角分辨光电子能谱(ARPES)、扫描隧道谱(STS)和密度泛函理论(DFT),解析了在Ge(001)/Si(001)上生长的石墨烯的功函数调制和电子耦合。UPS和KPFM揭示了与基底纳米刻面形貌相关的空间非均匀功函数景观。ARPES和DFT对原始界面的计算一致表明存在n型掺杂和电子从Ge向石墨烯的转移。相反,对氧化界面的建模预测掺杂极性反转为p型,为文献中报道的不同掺杂极性提供了合理解释。机器学习辅助的STS数据分类解析出不同的局域电子区域,范围从纳米刻面顶部的近自由站立石墨烯到刻面间更强耦合的区域以及具有独特局域电子响应的类纳米带区域。通过识别控制局域石墨烯-基底相互作用和电荷转移的机制,本研究为定制合成过程和最小化剥离过程中的缺陷形成提供了指导。这些见解支持生产用于电子器件的高质量石墨烯层,以及作为空气敏感材料保护涂层的转移。

英文摘要

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.

论文原文

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