Hybrid Deep Learning Crystallographic Mapping of Polymorphic Phases in Polycrystalline Hf0.5Zr0.5O2 Thin Films

By controlling the configuration of polymorphic phases in high‐k Hf0.5Zr0.5O2 thin films, new functionalities such as persistent ferroelectricity at an extremely small scale can be exploited. To bolster the technological progress and fundamental understanding of phase stabilization (or transition) and switching behavior in the research area, efficient and reliable mapping of the crystal symmetry encompassing the whole scale of thin films is an urgent requisite. Atomic‐scale observation with electron microscopy can provide decisive information for discriminating structures with similar symmetries. However, it often demands multiple/multiscale analysis for cross‐validation with other techniques, such as X‐ray diffraction, due to the limited range of observation. Herein, an efficient and automated methodology for large‐scale mapping of the crystal symmetries in polycrystalline Hf0.5Zr0.5O2 thin films is developed using scanning probe‐based diffraction and a hybrid deep convolutional neural network at a 2 nm2 resolution. The results for the doped hafnia films are fully proven to be compatible with atomic structures revealed by microscopy imaging, not requiring intensive human input for interpretation. Deep learning crystallographic analysis unequivocally addresses structure problems for sub‐10 nm polycrystalline hafnium zirconium oxide thin films. A hybrid deep learning‐based methodology for mapping crystal phases is developed in combination with 4D‐scanning transmission electron microscopy (position‐averaged) convergent beam electron diffraction (4D‐STEM (PA)CBED). With this, it can efficiently handle large 4D‐STEM CBED datasets composed of a mixture of polymorphic nanophases and perform rapid mapping of crystallographic parameters over the films.