Enhancing ferroelectric characterization at nanoscale: A comprehensive approach for data processing in spectroscopic piezoresponse force microscopy

H. Valloire, P. Quéméré, N. Vaxelaire, H. Kuentz, G. Le Rhun, Ł. Borowik
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Abstract

Switching Spectroscopy Piezoresponse Force Microscopy (SSPFM) stands out as a powerful method for probing ferroelectric properties within materials subjected to incremental polarization induced by an external electric field. However, the dense data processing linked to this technique is a critical factor influencing the quality of obtained results. Furthermore, meticulous exploration of various artifacts, such as electrostatics, which may considerably influence the signal, is a key factor in obtaining quantitative results. In this paper, we present a global methodology for SSPFM data processing, accessible in open-source with a user-friendly Python application called PySSPFM. A ferroelectric thin film sample of potassium sodium niobate has been probed to illustrate the different aspects of our methodology. Our approach enables the reconstruction of hysteresis nano-loops by determining the PR as a function of applied electric field. These hysteresis loops are then fitted to extract characteristic parameters that serve as measures of the ferroelectric properties of the sample. Various artifact decorrelation methods are employed to enhance measurement accuracy, and additional material properties can be assessed. Performing this procedure on a grid of points across the surface of the sample enables the creation of spatial maps. Furthermore, different techniques have been proposed to facilitate post-treatment analysis, incorporating algorithms for machine learning (K-means), phase separation, and mapping cross correlation, among others. Additionally, PySSPFM enables a more in-depth investigation of the material by studying the nanomechanical properties during poling, through the measurement of the resonance properties of the cantilever–tip–sample surface system.
加强纳米级铁电特性分析:光谱压电响应力显微镜数据处理的综合方法
开关光谱压电响应力显微镜(SSPFM)是一种强大的方法,可用于探测受外部电场诱导的增量极化作用材料的铁电特性。然而,与该技术相关的密集数据处理是影响所获结果质量的关键因素。此外,对可能严重影响信号的静电等各种假象进行细致的探索,也是获得定量结果的关键因素。在本文中,我们介绍了一种用于 SSPFM 数据处理的全局方法,该方法可通过名为 PySSPFM 的用户友好 Python 应用程序开源访问。我们对铌酸钠钾的铁电薄膜样品进行了探测,以说明我们方法的不同方面。我们的方法可以通过确定 PR 与外加电场的函数关系来重建磁滞纳米环。然后对这些磁滞环进行拟合,以提取作为样品铁电特性量度的特征参数。为提高测量精度,还采用了各种伪相关方法,并可评估其他材料特性。在样品表面的网格点上执行此程序,可创建空间地图。此外,还提出了不同的技术来促进后处理分析,其中包括机器学习算法(K-means)、相分离和映射交叉相关等。此外,PySSPFM 还能通过测量悬臂-尖端-样品表面系统的共振特性,研究极化过程中的纳米机械特性,从而对材料进行更深入的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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