Mathematical Modeling of Photovoltaic Properties of Nipc/P-Si (Organic/Inorganic) Heterojunction by Using Artificial Neural Networks Model

In this research, the artificial neural network (ANN) and resilient back propagation (R-prop) training algorithm are utilized to model the photovoltaic properties of Nickel–phthalocyanine (NiPc/p-Si) heterojunction. The experimental data are extracted from experimental studies. Experimental data are utilized as inputs in the ANN model. Training of different structures of the ANN is processed to approach the minimum value of error. Eight artificial neural networks are trained to get a better mean square error (MSE) and best execution for the networks. The ANN performances are also investigated and their values are very small (MSE < 10-3). The simulation results of the current-voltage characteristics of NiPc films are produced and provided excellent matching with the corresponding experimental data. Utilization of ANN model for predictions is also processed and gives accurate results.  The equation which describes the relation between the inputs and outputs is obtained. The high accuracy of the ANN model has appeared in the major guessing power and the ability of generalization depending on the obtained equations.