In this article we present a setup for the combinatorial vapor deposition of thin-film multilayerdevices as well as methods for the fast and efficient analytic screening of the libraries obtained. The preparation setup is based on a commercially available evaporation chamber equipped with various evaporation sources for both organic and metallic materials. The combinatorial approach is realized by the combination of a rotation stage for the substrate, a five-mask sampler, and an additional mask whose position can be deliberately varied along one axis during the evaporation process. The latter is used to evaporate linear as well as step gradients by continuous or stepwise movement of a shutter mask. The mask sampler allows to define the sectors of the library and to evaporate more complex structures, e.g., an electrode layout. Finally, the simultaneous evaporation of two or more materials enables us to produce layers of varying composition ratio in general and doped materials, in particular. For the control of the evaporation process we have developed an automation software, which is particularly helpful for complex library designs and which grants excellent repeatability of experiments. Efficient and fast characterization of the obtained libraries is realized by (i) a purely optical setup and (ii) an electro-optical setup. (i) The UV/vis reader FLASHScan 530 permits to map out the UV/vis absorbance or fluorescence of the whole library. The UV/vis absorbance is primarily used to determine layer thicknesses and to confirm thickness uniformity across larger regions. The fluorescence measurements are used to determine the composition of layers containing fluorescent dyes. (ii) For a detailed short- and long-term electro-optical analysis we have developed an automated measurement system, which allows the characterization of 8 X 8 optoelectronic devices and to study their degradation behavior. Both solar cells and organic light-emitting diodes can be tested. Finally, we have developed a data analysis software to extract characteristic values from the huge amount of data and with this facilitate the finding of systematic dependencies.
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