Optimizing the Organic Solar Cell Manufacturing Process by Means of AFM Measurements and Neural Networks

Giacomo Capizzi, Grazia Lo Sciuto, Christian Napoli, Rafi Shikler and Marcin Woźniak
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Giacomo Capizzi: Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Grazia Lo Sciuto: Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Christian Napoli: Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Rafi Shikler: Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, P.O.B. 653 Beer-Sheva, Israel
Marcin Woźniak: Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy

Energies, 2018, vol. 11, issue 5, 1-13

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.

Keywords: nanotechnologies; photonics; nanoplasmonics; neural networks (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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