发表机构
Luxembourg Institute of Science and Technology (LIST); University of Luxembourg（卢森堡科学与技术研究所; 卢森堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过机器学习分子动力学揭示氧化铪铁电体中本征开关引发雪崩式氧扩散并导致击穿，并提出通过设计场脉冲避免该结果的方案。
AI 中文摘要
传统铁电体展现出通过电开关相互连接的明确定义的极化态。然而，在萤石结构的铁电体（如氧化铪）中，开关与氧扩散似乎共存，丰富了铁电性的本质。本文利用机器学习分子动力学研究了氧化铪铁电体在室温下的本征开关与扩散动力学。我们识别出两种不同的开关机制，它们在现实时间尺度上均活跃。关键的是，我们的模拟揭示，由于晶格未能足够快地耗散来自局部开关事件的热量，这两个过程以雪崩般的方式串联，导致氧传导。因此，我们的结果表明本征开关导致氧化铪铁电体中的击穿。它们还提示了如何通过适当设计的场脉冲来避免这一结果。
英文摘要
Conventional ferroelectrics exhibit well-defined polarization states linked through electric switching. In fluorite-structured ferroelectrics like hafnia, though, switching and oxygen diffusion seem to coexist, enriching the nature of ferroelectricity. Here we address the intrinsic, room-temperature switching and diffusion kinetics of hafnia ferroelectrics using machine-learning molecular dynamics. We identify two distinct switching mechanisms that are both active at realistic time scales. Critically, our simulations reveal that, because the lattice does not dissipate fast enough the heat originating from localized switching events, these two processes concatenate in an avalanche-like manner leading to oxygen conduction. Our results thus show that intrinsic switching leads to breakdown in hafnia ferroelectrics. They also suggest how this outcome might be avoided through suitably designed field pulses.
Comments13+16 pages, 5+10 figures
