Extraction parameters and optimization in organic solar cell by solving transcendental equations in circuital models combined with a neuroprocessing-based procedure

The parameters identification of a complex OSC nonlinear circuits is very crucial to enhance the performance predictions in terms of output I-V curves of conventional and new generation OSC. It is very difficult the selection of a neural network based algorithm as MLP for a complex OSC circuital modeling then the relative parameters extraction. Thus we propose in the paper accurate nonlinear equations solution with one final single NN error driven based implementation for the concerning OSC modeling problems. The novelty of the proposed approach lies in coupling error driven neural networks based procedure in circuital modeling by an overall implementation and programming with the final procedure for parameters extraction and mainly the optimization. This was done via the solution of a transcendental equations set The approach reveals very appropriate OSC circuital parameters extraction so as optimization The procedure has been outlined and illustrated through effective implementation then simulation results are enclosed. The polymer solar cells investigated in the paper was an OSC characterized in the laboratory of organic semiconductor devices named The Michel Mamon Microelectronics Laboratory at the Ben-Gurion University of the Negev in Beer Sheva.