[论文解读] Origin of Reduced Coercive Field in ScAlN: Synergy of Structural Softening and Dynamic Atomic Correlations

Ryotaro Sahashi, Po-Yen Chen+1 more|arXiv (Cornell University)|Mar 19, 2026

Ferroelectric and Negative Capacitance Devices被引用 0

一句话总结

论文解释了 Sc 掺杂的 AlN（ScAlN）中强制场 Ec 随 Sc 含量减少的原因，归因于结构软化与通过带有机器学习势的高级分子动力学模拟观察到的动态原子相关性的共同作用。

ABSTRACT

Among wurtzite-type ferroelectrics, scandium-doped aluminum nitride (ScAlN) has emerged as a leading candidate for CMOS-compatible low-voltage memory, combining strong spontaneous polarization with process compatibility. A remarkable feature of this system is the pronounced reduction of the coercive field (Ec) with increasing Sc concentration; however, its microscopic origin remains poorly understood at the atomic scale, particularly under finite temperature and applied electric fields. Here, we integrate a density-functional-theory-accurate machine-learning force field with an equivariant neural-network-based Born effective charge model to perform large-scale electric-field-driven molecular dynamics simulations at near-first-principles accuracy. The framework correctly reproduces the experimentally observed qualitative trends in key experimental trends, including the decrease in the c/a ratio and the monotonic reduction of Ec with increasing Sc content. Beyond static structural softening, we uncover a dynamic mechanism underlying Ec reduction. Sc atoms exhibit larger thermal vibrations and undergo preceding displacements during switching, acting as dynamic triggers for polarization reversal. Moreover, the displacement correlation between Sc and Al atoms evolves systematically with composition, enhancing cooperative atomic rearrangements and lowering the effective switching barrier. These results demonstrate that Ec reduction in ScAlN arises from the synergy of structural softening and dynamic correlation evolution, providing a new perspective for designing hexagonal ferroelectrics.

研究动机与目标

• 理解 ScAlN 中 Ec 降低的微观起源对 CMOS 兼容铁电材料的意义。
• 研究 Sc 浓度如何影响结构参数和切换行为。
• 开发一种能够在电场作用下实现近第一性原理精度的大尺度仿真计算框架。

提出的方法

• 使用具有密度泛函理论准确性的机器学习力场进行大尺度仿真。
• 应用等变神经网络基的 Born 有效电荷模型来捕捉极化响应。
• 在有限温度下进行电场驱动的分子动力学仿真。
• 通过将框架的定性趋势（如 c/a 比、Ec 对 Sc 含量的关系）与实验观察进行比较来验证。
• 在切换过程中通过跟踪 Sc 原子振动及位移前驱量来分析动态机制。

实验结果

研究问题

• RQ1 在 ScAlN 中提升 Sc 含量时，哪些微观机制驱动 Ec 的降低？
• RQ2 结构软化和动态原子相关性如何随组成和温度演化以影响极化切换？
• RQ3 一个计算框架是否能够在大尺度下以第一性原理精度再现晶格参数和 Ec 的实验趋势？

主要发现

• Ec 随 Sc 含量增加呈单调下降，符合实验观察。
• 伴随 Ec 降低的是静态结构软化，体现在晶格参数（c/a 比）的变化上。
• Sc 原子表现出更大的热振动，并在切换过程中作为极化反转的动态触发因素。
• Sc 与 Al 之间的位移相关性随组成演变，促进协同原子重排并降低切换势垒。
• ML 力场结合等变 Born 电荷模型能够在大尺度下的电场驱动 MD 中再现 qualitatively（定性）实验趋势。