用自动化原子力显微镜研究铁电畴壁钉扎,发现局域结构决定电场响应。
Exploring Domain Wall Pinning in Ferroelectrics via Automated High Throughput AFM
- 机器学习控制的压电力显微镜自动采集1500次开关数据
- 多畴区畴壁需30V以上才移动,单变体畴壁20V即激活
- 可生成微结构-脉冲参数的预测图谱,用于设计铁电存储器
铁电材料中畴壁动力学强烈依赖位置,因每个极性界面被锁定于独特局部微结构。这需要使用扫描探针显微技术进行空间分辨的壁钉扎研究。钉扎中心和预存畴壁通常稀疏分布,无法使用密集超光谱成像模式,且需耗时的人工实验。本文研究了在立方KTaO₃上外延生长的大面积PbTiO₃薄膜,通过机器学习控制的自动化压电力显微镜量化电场驱动下极化-应变畴结构的动力学。对1500次开关事件的分析显示,畴壁位移不仅取决于电场参数,还受局域铁电-铁弹性构型影响。例如,多畴区中如a₁⁻/c⁺ ∥ a₂⁻/c⁻的孪晶界在偏压达30V前保持钉扎,偏压从20V增至30V时变化甚微;而单变体边界如a₂⁺/c⁺ ∥ a₂⁻/c⁻在20V即被激活。这些关于可能铁电与铁弹性壁取向的统计结果,结合自动化高通量AFM流程,可提炼为关联畴构型与脉冲参数的预测图谱,为设计铁电存储器提供基础。
原文摘要 · Abstract (English)
Domain-wall dynamics in ferroelectric materials are strongly position-dependent since each polar interface is locked into a unique local microstructure. This necessitates spatially resolved studies of the wall-pinning using scanning-probe microscopy techniques. The pinning centers and preexisting domain walls are usually sparse within image plane, precluding the use of dense hyperspectral imaging modes and requiring time-consuming human experimentation. Here, a large area epitaxial PbTiO$_3$ film on cubic KTaO$_3$ were investigated to quantify the electric field driven dynamics of the polar-strain domain structures using ML-controlled automated Piezoresponse Force Microscopy. Analysis of 1500 switching events reveals that domain wall displacement depends not only on field parameters but also on the local ferroelectric-ferroelastic configuration. For example, twin boundaries in polydomains regions like a$_1^-$/$c^+$ $\parallel$ a$_2^-$/$c^-$ stay pinned up to a certain level of bias magnitude and change only marginally as the bias increases from 20V to 30V, whereas single variant boundaries like a$_2^+$/$c^+$ $\parallel$ a$_2^-$/$c^-$ stack are already activated at 20V. These statistics on the possible ferroelectric and ferroelastic wall orientations, together with the automated, high-throughput AFM workflow, can be distilled into a predictive map that links domain configurations to pulse parameters. This microstructure-specific rule set forms the foundation for designing ferroelectric memories.
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