Abstract
In this paper we devise a neural-network-based model to improve the production workflow of organic solar cells (OSCs). The investigated neural model is used to reckon the relation between the OSC’s generated power and several device’s properties such as the geometrical parameters and the active layers thicknesses. Such measurements were collected during an experimental campaign conducted on 80 devices. The collected data suggest that the maximum generated power depends on the active layer thickness. The mathematical model of such a relation has been determined by using a feedforward neural network (FFNN) architecture as a universal function approximator. The performed simulations show good agreement between simulated and experimental data with an overall error of about 9%. The obtained results demonstrate that the use of a neural model can be useful to improve the OSC manufacturing processes.
Highlights
The basic design constraints in organic solar cells (OSCs) influence significantly the energy absorption by light trapping and the consequent conversion efficiency
The feed-forward neural network (FFNN) proposed in this paper is developed in order to obtain an approximate mathematical expression of OSC’s maximum power depending on the device’s geometrical parameters and the active layer’s thickness (PEDOT:PSS and phenyl-C61-butyric acid methyl ester (PCBM):P3HT)
The proposed neural network has been trained to minimize its root mean squared error (RMSE) by means of a Gradient Descend with Momentum Algorithm (GDMA) [29], using the geometrical parameters and the measured thicknesses as inputs and the measured maximum power as target
Summary
The basic design constraints in organic solar cells (OSCs) influence significantly the energy absorption by light trapping and the consequent conversion efficiency. OSC electrical characteristics strongly depend on the device’s geometry [1,2,3,4] Among such geometrical values, the cell surface dimension and the thickness, the active layer thickness, influences the performances of the solar cell. In [13], the authors present a particle swarm optimization algorithm to devise a two-dimensional model for multilayer bulk heterojunction organic nanoscale solar cells. Such a model takes into account the active layer thickness and the device’s morphology in order to Energies 2018, 11, 1221; doi:10.3390/en11051221 www.mdpi.com/journal/energies.
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