Machine-Learning-Enabled Discovery of Coexisting Phases through Nanospectroscopy of a Wide-Bandgap Semiconductor.

Abstract

Wide bandgap semiconductors with high room temperature mobilities are promising materials for high-power electronics. Stannate films provide wide bandgaps and optical transparency, although electron-phonon scattering can limit mobilities. In SrSnO₃, epitaxial strain engineering stabilizes a high-mobility tetragonal phase at room temperature, resulting in a 3-fold increase in electron mobility among doped films. However, strain relaxation in thicker films leads to nanotextured coexistence of tetragonal and orthorhombic phases with unclear implications for optoelectronic performance. The observed nanoscale phase coexistence demands nanospectroscopy to supply spatial resolution beyond conventional, diffraction-limited microscopy. With nanoinfrared spectroscopy, we provide a comprehensive analysis of phase coexistence in SrSnO₃ over a broad energy range, distinguishing inhomogeneous phonon and plasma responses arising from structural and electronic domains. We establish Nanoscale Imaging and Spectroscopy with Machine-learning Assistance (NISMA) to map nanotextured phases and quantify their distinct optical responses through a robust quantitative analysis, which can be applied to a broad array of complex oxide materials.