arXiv 2512.092492025-12-10cond-mat.mtrl-sci

Auto-3DPFM：在纳米尺度上自动化极化矢量映射

Auto-3DPFM: Automating Polarization-Vector Mapping at the Nanoscale

Oak Ridge National Laboratory（橡树岭国家实验室）；Technical University of Denmark（丹麦技术大学）；University of Geneva（日内瓦大学）；UNSW Sydney（新南威尔士大学悉尼分校）；University of Canterbury（坎特伯雷大学）

Ralph Bulanadi, Marti Checa, Michelle Wang, Franck Rothen, John Lasseter, Sumner B. Harris, Daniel Sando, Valanoor Nagarajan, Liam Collins, Stephen Jesse, Rama … 展开作者

Ralph Bulanadi, Marti Checa, Michelle Wang, Franck Rothen, John Lasseter, Sumner B. Harris, Daniel Sando, Valanoor Nagarajan, Liam Collins, Stephen Jesse, Rama Vasudevan, Yongtao Liu

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AI总结：

Auto-3DPFM通过自动化技术实现纳米尺度极化矢量的高精度表征，提升铁电材料研究的效率与准确性。

摘要：

铁电材料的功能特性强烈受到铁电极化取向的影响；因此，获得一致且精确的极化矢量表征对于铁电研究至关重要。在这里，我们开发了一种完全自动化的三维压电响应力显微镜（Auto-3DPFM）技术，该技术自动化了干涉式PFM中用于3D极化矢量表征的所有必要步骤，包括激光对准、探针校准和接近、图像获取、极化矢量重建和可视化。自动化减少了铁电极化矢量表征的实验负担，而来回校准确保了3D极化重建的一致性和可重复性。还开发了一种算法流程，通过空间矢量-角度差方法识别畴壁并计算其特征角度，通过Auto-3DPFM实现了一种传统PFM技术无法实现的独特能力。除了在3D极化映射方面取得重大进展外，Auto-3DPFM还通过高通量和自主表征加速铁电材料研究的发现。当与自动驾驶实验室中的机器学习和自适应采样策略结合时，Auto-3DPFM将成为推进铁电物理和微电子发展的重要工具。

英文摘要：

The functional properties of ferroelectric materials are strongly influenced by ferroelectric polarization orientation; as such, access to consistent and precise characterization of polarization vectors is of substantial importance to ferroelectrics research. Here, we develop a fully automated three-dimensional piezoresponse force microscopy (Auto-3DPFM) technique automating all essential steps in interferometric PFM for 3D polarization vector characterization, including laser alignment, tip calibration and approach, image acquisition, polarization vector reconstruction, and visualization. The automation reduces the experimental burden of ferroelectric polarization vector characterization, while the back-and-forth calibration ensures consistency and reproducibility of 3D polarization reconstruction. An algorithmic workflow is also developed to identify domain walls and calculate their characteristic angles via a spatial vector-angle-difference method, presenting one unique capability enabled by Auto-3DPFM that is not accessible with traditional PFM techniques. Beyond representing a significant step forward in 3D polarization mapping, Auto-3DPFM promises to accelerate discovery via high-throughput and autonomous characterization in ferroelectric materials research. When integrated with machine learning and adaptive sampling strategies in self-driving labs, Auto-3DPFM will serve as a valuable tool for advancing ferroelectric physics and microelectronics development.

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