In Situ Contact Angle Measurement for Autonomous Spin Coating in Self‐Driving Labs

Current experimental workflows in solution‐processed thin‐film photovoltaics research are restricted to known solution combinations where solvent properties are predictable. To experimentally investigate the vast parameter space of new compounds, we must explore unknown material combinations using automated systems. However, this increases the risk of coating failures that previously required human judgment to detect. Integrating real‐time process monitoring into existing deposition equipment is essential to close that gap. We present a vision‐based add‐on to commercial spin coaters that provides the process feedback missing from standard equipment—wetting assessment and substrate orientation for robotic retrieval—enabling fully automated coating workflows. Our hybrid software pipeline combines semantic segmentation using a width‐scaled U‐Net with classical geometric analysis, enabling simultaneous contact angle measurement and substrate pose estimation from a single RGB camera. We validated the module through an 11 h fully automated experiment, tracking hydrophobic recovery on plasma‐treated substrates. By replacing human process supervision with machine vision, this system is a prerequisite for materials discovery in previously unexplored chemical regimes and transforms standard commercial spin coaters into components of Self‐Driving Labs.