High-throughput experimentation (HTE) is increasingly being employed to accelerate metal halide perovskite (MHP) semiconductor thin-film development.[1] As of now, most approaches focus on solution-based deposition methods.[2–4] To address the need for scalable and industrially-compatible fabrication approaches, vapor-based deposition methods are gaining popularity.[5] However, durability concerns remain a major obstacle for large-scale deployment.[6] This motivates high-throughput stability studies of vapor-deposited MHP thin films.
Combinatorial materials science is perfectly suited to address this challenge, specifically for time-consuming degradation studies where parallelization of experiments is key.[7] Using vapor deposition techniques, large parameter spaces can be covered on single substrates, whereas automated characterization and data analysis facilitate rapid properties screening.[8]
In this work, we present a comprehensive workflow for the aging behavior of thin-film MHPs which includes structural, optical and chemical characterization.[9] To mitigate ambient degradation of pristine thin films during characterization or transfers, we employ a complete inert-gas workflow. Furthermore, we perform a rapid in situ screening of the transmission and reflectance in a broad wavelength range under accelerated aging conditions. Specifically, the samples are exposed to 85 °C and 1 kW m−2 white light bias, probing intrinsic material degradation in an accelerated fashion. With a temperature variation of ±1 °C and light intensity variation of <2% across combinatorial libraries, meaningful combinatorial stability screening is enabled. Automated characterizations of the structural properties yield deep insights into the aging process, extending and validating insights from changes in the optical transmission.  We further demonstrate how these data sets can be used to better understand changes in the optical properties for highly scattering thin-films especially using machine learning assisted analysis. Furthermore, the workflow can be combined with high-throughput surface characterization techniques that our group previously demonstrated as a novel tool for accelerated materials discovery and optimization.[10–12]
As a case study, we investigate the effect of residual precursors on the stability of two-step deposited MHP thin films grown on vapor-deposited templates.[9] This workflow further allows to screen compositional spaces of libraries grown from completely vapor-based deposition methods.