发表机构
Institute for Theoretical Physics, University of Amsterdam; Advanced Research Center for Nanolithography（阿姆斯特丹大学理论物理研究所; 先进纳米光刻研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对非晶材料电荷陷阱分类难题，提出状态对齐图小波指纹方法，统一电子身份、多尺度网络重组与能量稳定化，并揭示Si-N键断裂主导深陷阱形成。
AI 中文摘要
对非晶材料中电荷陷阱进行分类需要同时解析电子态和网络响应。我们开发了状态对齐的图小波指纹，并将其应用于a-Si$_3$N$_4$中的电子俘获，在该材料中，本征极化子俘获、配位驱动俘获和电荷驱动转换共存于同一系综中。在415对弛豫后的中性和带负电的晶胞中，局域化前后陷阱态的对齐将电子身份与多尺度结构指纹联系起来。电荷驱动的转换在多个图尺度上产生显著的重组，并增加指纹振幅。指纹变化识别出每个位点所经历的转变，而占据引起的能级移动追踪局域化增益，图变形追踪稳定化。电荷诱导的缺陷形成主导了最深光谱陷阱十分位数：69%形成于俘获时Si-N键断裂的位点，尽管该路径占已分类陷阱的44.2%。这些指纹在一个定量框架中统一了电子身份、多尺度网络重组和能量稳定化，用于解析非晶材料中的电荷俘获。
英文摘要
Classifying charge traps in amorphous materials requires the electronic state and the network response to be resolved together. We develop state-aligned graph-wavelet fingerprints and apply them to electron trapping in a-Si$_3$N$_4$, where intrinsic-polaronic trapping, coordination-driven trapping, and charge-driven conversion coexist within one ensemble. Across 415 pairs of relaxed neutral and negatively charged cells, alignment of the trap state before and after localisation connects electronic identity to multiscale structural fingerprints. Charge-driven conversion produces substantial reorganisation across graph scales and increases the fingerprint amplitude. The fingerprint change identifies the transformation each site undergoes, while the occupation-induced level shift tracks localisation gain and graph deformation tracks stabilisation. Charge-induced defect formation dominates the deepest spectral-trap decile: 69\% form at sites whose Si--N bond breaks on capture, although this route accounts for 44.2\% of the classified traps. The fingerprints unify electronic identity, multiscale network reorganisation and energetic stabilisation in a quantitative framework for resolving charge trapping in amorphous materials.
