Institute for Functional Imaging of Materials and Center for Nanophase Materials Sciences, Oak Ridge National Laboratory , Oak Ridge, Tennessee 37831, United States.
Department of Materials Science and Engineering, National Chiao Tung University , Hsinchu 30010, Taiwan.
Nano Lett. 2015 Oct 14;15(10):6650-7. doi: 10.1021/acs.nanolett.5b02472. Epub 2015 Aug 31.
Development of new generation electronic devices necessitates understanding and controlling the electronic transport in ferroic, magnetic, and optical materials, which is hampered by two factors. First, the complications of working at the nanoscale, where interfaces, grain boundaries, defects, and so forth, dictate the macroscopic characteristics. Second, the convolution of the response signals stemming from the fact that several physical processes may be activated simultaneously. Here, we present a method of solving these challenges via a combination of atomic force microscopy and data mining analysis techniques. Rational selection of the latter allows application of physical constraints and enables direct interpretation of the statistically significant behaviors in the framework of the chosen physical model, thus distilling physical meaning out of raw data. We demonstrate our approach with an example of deconvolution of complex transport behavior in a bismuth ferrite-cobalt ferrite nanocomposite in ambient and ultrahigh vacuum environments. Measured signal is apportioned into four electronic transport patterns, showing different dependence on partial oxygen and water vapor pressure. These patterns are described in terms of Ohmic conductance and Schottky emission models in the light of surface electrochemistry. Furthermore, deep data analysis allows extraction of local dopant concentrations and barrier heights empowering our understanding of the underlying dynamic mechanisms of resistive switching.
新一代电子设备的发展需要理解和控制铁电、磁性和光学材料中的电子输运，这受到两个因素的阻碍。首先，在纳米尺度下工作的复杂性，其中界面、晶界、缺陷等决定了宏观特性。其次，由于可能同时激活多个物理过程，响应信号的卷积。在这里，我们通过原子力显微镜和数据挖掘分析技术的组合来提出一种解决这些挑战的方法。合理选择后者允许应用物理约束，并能够在所选物理模型的框架内直接解释具有统计学意义的行为，从而从原始数据中提取物理意义。我们通过在环境和超高真空环境中对铋铁氧体-钴铁氧体纳米复合材料中复杂输运行为的解卷积示例来说明我们的方法。测量信号被分配到四种电子输运模式中，这些模式显示出对部分氧气和水蒸气压力的不同依赖性。根据表面电化学原理，这些模式用欧姆电导和肖特基发射模型来描述。此外，深度数据分析还可以提取局部掺杂浓度和势垒高度，从而增强我们对电阻开关背后动态机制的理解。
