Optical Identification of Materials Transformations in Oxide Thin Films

TL;DR

This study demonstrates that optical microscopy and reflectance spectroscopy can efficiently identify material phase boundaries in oxide thin films, significantly aiding the targeted structural analysis of complex material libraries.

Contribution

The paper introduces a rapid optical approach to pre-screen phase boundaries in oxide thin films, improving the efficiency of subsequent high-resolution structural characterization.

Findings

Optical methods detect over 95% of structural transformations.

Optical techniques effectively identify phase boundaries in composition-gradient libraries.

The approach accelerates and guides high-precision XRD analysis.

Abstract

Recent advances in high-throughput experimentation for combinatorial studies have accelerated the discovery and analysis of materials across a wide range of compositions and synthesis conditions. However, many of the more powerful characterization methods are limited by speed, cost, availability, and/or resolution. To make efficient use of these methods, there is value in developing approaches for identifying critical compositions and conditions to be used as a-priori knowledge for follow-up characterization with high-precision techniques, such as micron-scale synchrotron based X-ray diffraction (XRD). Here we demonstrate the use of optical microscopy and reflectance spectroscopy to identify likely phase-change boundaries in thin film libraries. These methods are used to delineate possible metastable phase boundaries following lateral-gradient Laser Spike Annealing (lg-LSA) of oxide materials. The set of boundaries are then compared with definitive determinations of structural transformations obtained using high-resolution XRD. We demonstrate that the optical methods detect more than 95% of the structural transformations in a composition-gradient La-Mn-O library and a Ga$_2$O$_3$ sample, both subject to an extensive set of lg-LSA anneals. Our results provide quantitative support for the value of optically-detected transformations as a priori data to guide subsequent structural characterization, ultimately accelerating and enhancing the efficient implementation of $\mu$m-resolution XRD experiments.