Hybrid Deep Learning Crystallographic Mapping of Polymorphic Phases in Polycrystalline Hf_0.5Zr_0.5O₂ Thin Films

Abstract By controlling the configuration of polymorphic phases in high‐k Hf_0.5Zr_0.5O₂ 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 Hf_0.5Zr_0.5O₂ 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.