深度学习加速的高k HfO2电介质掺杂剂选择：Y、Si和Al的无序解析研究
Deep-Learning-Accelerated Dopant Selection for High-k HfO2 Dielectrics: A Disorder-Resolved Study of Y, Si and Al
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中文总结 AI 辅助
该研究整合SQS、SevenNet和ALIGNN的高通量计算框架，解析Y、Si、Al掺杂HfO2的性能，揭示形成能等参数解耦规律，为高k电介质掺杂剂选择提供理性指导。
中文摘要 AI 辅助
氧化铪（HfO2）是现代硅技术中的基石性高k电介质材料。由于硅器件制造的约束排除了替换该材料本身的可能，掺杂剂掺入是在现有工艺流程中调控其带隙和介电常数的主要手段。然而，由于热力学稳定性、电子绝缘性与介电响应之间的耦合相互作用，掺杂剂选择在很大程度上仍依赖经验。本文提出一种整合特殊准随机结构（SQS）、机器学习势（SevenNet）和图神经网络（ALIGNN）的高通量计算框架，系统评估三种掺杂剂（Al、Si、Y）及两种技术相关多晶型（单斜相和正交相）下的掺杂HfO2组分。分析揭示了一项基础设计原则：形成能、带隙和介电常数是解耦参数，需根据应用需求优先选择而非同时优化。钇（Y）实现最低形成能（-3.763 eV/原子），并在工艺兼容的热预算下有利于正交相稳定；硅（Si）保留接近原始的带隙（约5.72 eV），对抑制栅极电介质应用中的漏电流至关重要；铝（Al）可实现浓度可调的带隙拓宽（5.6-5.9 eV），适合电压缩放。与实验文献及密度泛函理论（DFT）的验证确认了定量准确性（Si掺杂的带隙误差为0.02 eV，形成能平均绝对误差小于0.001 eV/原子）。该框架为HfO2基电介质及相关高k氧化物系统的掺杂剂工程提供了基于理性的、属性解耦的指导。
英文摘要
Hafnium oxide (HfO2) is the cornerstone high-k dielectric in modern silicon technology. Since the constraints of silicon device fabrication rule out replacing the material itself, dopant incorporation is the principal means available to engineer its band gap and dielectric constant within existing process flows. However, dopant selection is still largely empirical due to the coupled interplay among thermodynamic stability, electronic insulation, and dielectric response. Here, we present a high-throughput computational framework integrating special quasi-random structures (SQS), machine-learning potentials (SevenNet), and graph neural networks (ALIGNN) to systematically evaluate doped-HfO2 compositions across three dopants (Al, Si, Y) and two technologically relevant polymorphs (monoclinic and orthorhombic). Our analysis uncovers a fundamental design principle: formation energy, band gap, and dielectric constant are decoupled parameters requiring application-specific prioritization rather than simultaneous optimization. Yttrium achieves the lowest formation energy (-3.763 eV/atom) and favors orthorhombic phase stabilization at process-compatible thermal budgets; silicon preserves near-pristine band gaps (around 5.72 eV) critical for suppressing leakage in gate dielectric applications; and aluminum enables concentration-tunable band gap widening (5.6-5.9 eV) suited for voltage scaling. Validation against experimental literature and density functional theory (DFT) confirms quantitative accuracy (0.02 eV band gap error for Si-doping, mean absolute error less than 0.001 eV/atom formation energy). This framework provides rational, property-decoupled guidance for dopant engineering in HfO2-based dielectrics and related high-k oxide systems.
