Artificial neural network modeling for potential performance enhancement of a planar perovskite solar cell with a novel TiO2/SnO2 electron transport bilayer using nonlinear programming

The performance of a planar Perovskite Solar Cell (PSC), having a novel TiO2/SnO2 bilayer as Electron Transport Layer (ETL), has been optimized. To achieve this, an artificial neural network (ANN) was trained to predict the Power Conversion Efficiency (PCE) as a function of five technological parameters of the PSC namely; Active layer thickness, Hole Transport Layer (HTL) thickness, HTL dopant concentration, Electron Transport Layer (ETL) thickness, and ETL dopant concentration. The model was able to predict the data accurately, with an overall Mean Square Error (MSE) of 0.0002302, Root Mean Square Error of (RMSE) of 0.01502, Sum of Squares Error of (SSE) of 0.4712, and correlation coefficient (R) of 0.9991. Subsequently, the optimum values of independent variables giving the maximum efficiency were determined by applying the Particle Swarm Optimization algorithm to maximize the ANN-derived model. The optimum solutions predicted were then simulated using the SCAPS 1-D program, yielding a PCE of 14.94%. This paper presents a straightforward and efficient methodology by which ANN modeling and nonlinear programming can be applied to carry out multi-property optimization of PSC performance.