| Summary: | Organic metal halide perovskites have shown plenty of extraordinary optoelectronic properties which make them good candidates for various photovoltaic applications [1-5]. The fascinating optoelectronic properties of perovskite largely take credit to their low exciton binding energy, strong light absorption coefficient, relatively long carrier diffusion length, and carrier recombination lifetime [6-9]. However, even with an increasing number of studies carried out, perovskite solar cell is still facing plenty of challenges towards commercialization. Two main challenges towards large-area commercialization include first the harsh fabrication environment and cost of large-area coating; and second the redundant fabrication process with a huge labor force impelled. In this thesis study, an intermedia thin film layer tris(4-carbazoyl-9ylphenyl)amine (TcTa) with a thickness of 3 nm is discovered in a large-area compatible perovskite solar cell structure ITO/SnO2/(MAFACs)1Pb(IBrCl)3/PV2000/TcTa/Au that reaches a power conversion efficiency above 14%. The TcTa intermediate film is compatible with substituting gold top electrodes and preventing sputter damage while maintaining a similar solar cell performance (etc. sputtered Ni). In addition, a machine learning algorithm is developed to predict the solar cell current-voltage properties only based on the film stack optical properties before the solar cell is fabricated. The algorithm is developed and tested based on the 3D/2D perovskite solar cell structure [10] with resulting in an average prediction regression loss below 5% and a best prediction accuracy above 99%. Multiple different machine learning algorithm is also carried out to analyze the prediction results and learning weights for the model.
|
|---|