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Commercialization of printed photovoltaics requires knowledge of the optimalcomposition and microstructure of the single layers, and the ability to controlthese properties over large areas under industrial conditions. Whilemicrostructure optimization can be readily achieved by lab scale methods, thetransfer from laboratory scale to a pilot production line ('lab to fab') is aslow and cumbersome process. Here, we show how we can optimize the performanceof organic solar cells and at the same time assess process performance in a 2Dcombinatorial approach directly on an industrially relevant slot die coatingline. This is enabled by a multi-nozzle slot die coating head allowingparameter variations along and across the web. This modification allows us togenerate and analyze 3750 devices in a single coating run, varying the activelayer donor:acceptor ratio and the thickness of the electron transport layer(ETL). We use Gaussian Process Regression (GPR) to exploit the whole datasetfor precise determination of the optimal parameter combination.Performance-relevant features of the active layer morphology are inferred fromUV-Vis absorption spectra. By mapping morphology in this way, small undesiredgradients of process conditions (extrusion rates, annealing temperatures) aredetected and their effect on device performance is quantified. The correlationbetween process parameters, morphology and performance obtained by GPR provideshints to the underlying physics, which are finally quantified by automatedhigh-throughput drift-diffusion simulations. This leads to the conclusion thatvoltage losses which are observed for very thin ETL coatings are due toincomplete coverage of the electrode by the ETL, which cause enhanced surfacerecombination.

Cutting 'lab-to fab' short: High Throughput Optimization and Process Assessment in Roll-to-Roll Slot Die Coating of Printed Photovoltaics