  Commercialization of printed photovoltaics requires knowledge of the optimal
composition and microstructure of the single layers, and the ability to control
these properties over large areas under industrial conditions. While
microstructure optimization can be readily achieved by lab scale methods, the
transfer from laboratory scale to a pilot production line ('lab to fab') is a
slow and cumbersome process. Here, we show how we can optimize the performance
of organic solar cells and at the same time assess process performance in a 2D
combinatorial approach directly on an industrially relevant slot die coating
line. This is enabled by a multi-nozzle slot die coating head allowing
parameter variations along and across the web. This modification allows us to
generate and analyze 3750 devices in a single coating run, varying the active
layer donor:acceptor ratio and the thickness of the electron transport layer
(ETL). We use Gaussian Process Regression (GPR) to exploit the whole dataset
for precise determination of the optimal parameter combination.
Performance-relevant features of the active layer morphology are inferred from
UV-Vis absorption spectra. By mapping morphology in this way, small undesired
gradients of process conditions (extrusion rates, annealing temperatures) are
detected and their effect on device performance is quantified. The correlation
between process parameters, morphology and performance obtained by GPR provides
hints to the underlying physics, which are finally quantified by automated
high-throughput drift-diffusion simulations. This leads to the conclusion that
voltage losses which are observed for very thin ETL coatings are due to
incomplete coverage of the electrode by the ETL, which cause enhanced surface
recombination.
