Spatially Resolved Recombination Analysis of CuInxGa1-xSe2 Absorbers With Alkali Postdeposition Treatments

In this contribution, we probe spatial variations in charge-carrier recombination in CuInxGa1-x Se2 (CIGS) absorbers grown on soda–lime glass (SLG) and alkali-free sapphire substrates with NaF and KF postdeposition treatments (PDTs). Temperature-and illumination-dependent device measurements are used to track interface recombination and recombination in the quasi-neutral region. The analysis of these data reveals that the benefit of alkali PDTs depends on the substrate: interface recombination is reduced in devices grown on sapphire substrates, whereas recombination in the quasi-neutral regions is reduced in devices grown on SLG substrates. Cathodoluminescence (CL) spectrum imaging is used to study the spatial distribution of recombination with respect to the grain structure. The grain-boundary CL contrast is similar in films with no PDT, NaF PDT, or KF PDT. A reduced grain-boundary contrast is observed with a NaF + KF PDT; however, suggesting a reduced recombination strength at the grain boundaries (GBs) for combined NaF + KF treatment. CL spectra indicate band tailing, consistent with the fluctuating potential model. Fluctuating potentials are believed to reduce open-circuit voltage, but their spatial distribution has not been studied. Here, CL spectrum imaging data are used to generate maps of the root-mean-square value of the potential energy fluctuations—γ. These maps reveal a bimodal γ distribution for all samples: γ is generally in the range ∼15–50 meV or ∼100–180 meV. The higher γ range is more significantly affected by the PDTs; after the PDTs, it is strongly correlated with GBs. The lower γ range is correlated with higher emission intensity regions, typically grain interiors, and increases in area fraction after the PDTs. These results demonstrate how spatially resolved luminescence and device characterization measurements can be used to monitor changes in recombination in CIGS films and photovoltaic devices. Such measurements can complement empirical device optimization and help improve device performance.