Effects of the Electrical Properties of SnO2 and C60 on the Carrier Transport Characteristics of p-i-n-Structured Semitransparent Perovskite Solar Cells.

Recently, metal halide perovskite-based top cells have shown significant potential for use in inexpensive and high-performance tandem solar cells. In state-of-the-art p-i-n perovskite/Si tandem devices, atomic-layer-deposited SnO2 has been widely used as a buffer layer in the top cells because it enables conformal, pinhole-free, and highly transparent buffer layer formation. In this work, the effects of various electrical properties of SnO2 and C60 layers on the carrier transport characteristics and the performance of the final devices were investigated using a numerical simulation method, which was established based on real experimental data to increase the validity of the model. It was found that the band alignment at the SnO2/C60 interface does, indeed, have a significant impact on the electron transport. In addition, as a general design rule, it was suggested that at first, the conduction band offset (CBO) between C60 and SnO2 should be chosen so as not to be too negative. However, even in a case in which this CBO condition is not met, we would still have the means to improve the electron transport characteristics by increasing the doping density of at least one of the two layers of C60 and/or SnO2, which would enhance the built-in potential across the perovskite layer and the electron extraction at the C60/SnO2 interface.

Optical Characterization and Prediction with Neural Network Modeling of Various Stoichiometries of Perovskite Materials Using a Hyperregression Method.

Quaternary perovskite solar cells are being extensively studied, with the goal of increasing solar cell efficiency and securing stability by changing the ratios of methylammonium, formamidinium, I3, and Br3. However, when the stoichiometric ratio is changed, the photoelectric properties reflect those of different materials, making it difficult to study the physical properties of the quaternary perovskite. In this study, the optical properties of perovskite materials with various stoichiometric ratios were measured using ellipsometry, and the results were analyzed using an optical simulation model. Because it is difficult to analyze the spectral pattern according to composition using the existing method of statistical regression analysis, an artificial neural network (ANN) structure was constructed to enable the hyperregression analysis of n-dimensional variables. Finally, by inputting the stoichiometric ratios used in the fabrication and the wavelength range to the trained artificial intelligence model, it was confirmed that the optical properties were similar to those measured with an ellipsometer. The refractive index and extinction coefficient extracted through the ellipsometry analysis show a tendency consistent with the color change of the specimen, and have a similar shape to that reported in the literature. When the optical properties of the unmodified perovskite are predicted using the verified artificial intelligence model, a very complex change in pattern is observed, which is impossible to analyze with a general regression method. It can be seen that this change in optical properties is well maintained, even during rapid variations in the pattern according to the change in composition. In conclusion, hyperregression analysis with n-dimensional variables can be performed for the spectral patterns of thin-film materials using a simple big data construction method.