The
key to elucidating various unexplained phenomena regarding
ferroelectric materials and discovering new ones as well as improving
the properties of these materials is to understand the domain dynamics
at the nanoscale. The recently developed local capacitance–voltage
(C–V) mapping method allows
nanoscale analysis of domain dynamics through dielectric measurements.
This method has advantages over conventional methods, such as the
ability to simultaneously achieve high speed and high sensitivity
in the observation, allowing for high-resolution map data to be acquired
quickly. Herein, we present a methodology to analyze hyperspectral
image data with the huge amount of information generated by this method
using a machine learning approach to extract and visualize the information
necessary for understanding domain dynamics. In addition, using this
methodology, we focus on the effect of grain boundaries on polarization
switching in ferroelectric films. We chose doped HfO2 thin
films as the specific target of our measurement. While these materials
are expected to be applied to ultralow power consumption nonvolatile
memory and neuromorphic devices, a major problem is that their properties
change during the application of electric field cycling, known as
wake-up and fatigue. In this study, we analyzed doped HfO2 films before wake-up using local C–V mapping. The acquired data sets were then clustered into
several regions based on the similarity of their polarization properties
using unsupervised learning methods such as k-means
and Gaussian mixture models. In addition to the typical butterfly
shaped C–V curves that represent
normal polarization switching, these clusters contained asymmetric
butterfly curves that may be caused by domain pinning or other built-in
field-derived effects. Subsequent statistical analysis suggested that,
in pristine HfO2 films before wake-up, although defects
at grain boundaries have some effect on the polarization switching
properties, they are not the main cause of the variation in properties.