SpectraFormer：一种基于注意力机制的拉曼解混工具，用于获取SiC上石墨烯缓冲层特征
SpectraFormer: an Attention-Based Raman Unmixing Tool for Accessing the Graphene Buffer-Layer Signature on SiC
AI总结：
SpectraFormer通过基于注意力机制的深度学习模型，实现了SiC上石墨烯缓冲层特征的无参考拉曼解混，提升了对弱振动特征的识别能力。
摘要：
拉曼光谱学是表征石墨烯的关键工具，但其在生长于硅碳化物（SiC）上的石墨烯应用受到SiC衬底强烈且变化的二次拉曼响应的严重限制。这种限制对缓冲层石墨烯至关重要，它是一种半导体界面相，其振动特征与SiC背景重叠，难以通过传统参考减法可靠获取，因为衬底信号具有强烈的空间和实验变化性。本文提出了SpectraFormer，一种基于变压器的深度学习模型，能够直接从生长后部分遮蔽的光谱数据中重建SiC拉曼衬底贡献，而无需依赖显式的参考测量。通过学习整个拉曼位移范围内的全局相关性，该模型捕捉了SiC背景的统计结构，并在混合光谱中实现了准确的重建。减去重建的衬底信号揭示了与ZLG相关的弱振动特征，这些特征无法通过传统分析方法获取。提取的光谱通过从头计算验证，允许将解析的特征分配给特定模式并确认其物理一致性。通过利用最先进的基于注意力的深度学习架构，这种方法建立了一种稳健、无参考的框架，用于SiC上石墨烯的拉曼分析，并提供了一个与实时数据采集兼容的基础，用于其整合到自动化的闭环AI辅助生长优化中。
英文摘要：
Raman spectroscopy is a key tool for graphene characterization, yet its application to graphene grown on silicon carbide (SiC) is strongly limited by the intense and variable second-order Raman response of the substrate. This limitation is critical for buffer layer graphene, a semiconducting interfacial phase, whose vibrational signatures are overlapped with the SiC background and challenging to be reliably accessed using conventional reference-based subtraction, due to strong spatial and experimental variability of the substrate signal. Here we present SpectraFormer, a transformer-based deep learning model that reconstructs the SiC Raman substrate contribution directly from post-growth partially masked spectroscopic data without relying on explicit reference measurements. By learning global correlations across the entire Raman shift range, the model captures the statistical structure of the SiC background and enables accurate reconstruction of its contribution in mixed spectra. Subtraction of the reconstructed substrate signal reveals weak vibrational features associated with ZLG that are inaccessible through conventional analysis methods. The extracted spectra are validated by ab initio vibrational calculations, allowing assignment of the resolved features to specific modes and confirming their physical consistency. By leveraging a state-of-the-art attention-based deep learning architecture, this approach establishes a robust, reference-free framework for Raman analysis of graphene on SiC and provides a foundation, compatible with real-time data acquisition, to its integration into automated, closed-loop AI-assisted growth optimization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
