The development of high efficiency perovskite solar cells relies heavily on identifying the precise combination of processing conditions and material compositions. Poor reproducibility and sensitivity to processing parameters have made it difficult to systematically optimize perovskite thin films across labs, and the high dimensionality of the processing space leaves traditional trial and error methods poorly equipped to navigate the search space. Self-driving laboratories offer a route to make this optimization more efficient and effective by coupling robotic synthesis with machine learning-driven decision making. Here we present a fully automated, closed loop spin coating platform driven by high dimensional Bayesian optimization to autonomously synthesize and optimize perovskite thin films.

We employ the SpinBot platform with custom modifications for closed loop integration. The system autonomously performs solution mixing, spin coating, thermal annealing, and characterization. It is capable of fabricating nearly the entire device stack on platform, with only electrode evaporation performed externally. Because completing and testing full devices is comparatively slow and would bottleneck the optimization loop, we instead characterize each film in-line with a suite of optical techniques; PL and absorption mapping, time-resolved PL, and intensity-modulated PL spectroscopy that probe the carrier dynamics and ionic transport governing device efficiency and stability. A custom software orchestration framework bridges the robotic hardware with the optimization loop, enabling continuous experimental iteration. As an initial demonstration, we used the platform to run an optimization of 20 processing parameters over 300 samples on a single perovskite composition, confirming that the integrated synthesis-characterization-optimization loop operates reliably and converges to improved film quality.

Building on this validated loop, we now target a more device relevant objective. From the combined characterization data, we construct a proxy metric for device efficiency and stability, and optimize processing conditions against it in a closed loop using Bayesian optimization. The optimization spans 10-20 continuous and categorical processing parameters, including antisolvent type, timing, and flow rate, alongside spin-coating and annealing parameters, and extends to precursor ink composition through in-situ solution mixing. Constructing this proxy from fast optical measurements provides the throughput needed to navigate such a large parameter space.