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Open AccessArticle

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

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Department of Electrical, Electronics and Informatics Engineering, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
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Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
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Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev, P.O.B. 653 Beer-Sheva, Israel
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Institute of Mathematics, Silesian University of Technology, Kaszubska, 23, 44-100 Gliwice, Poland
*
Author to whom correspondence should be addressed.
Energies 2018, 11(5), 1221; https://doi.org/10.3390/en11051221
Received: 12 April 2018 / Revised: 5 May 2018 / Accepted: 8 May 2018 / Published: 10 May 2018
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. View Full-Text
Keywords: nanotechnologies; photonics; nanoplasmonics; neural networks nanotechnologies; photonics; nanoplasmonics; neural networks
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MDPI and ACS Style

Capizzi, G.; Lo Sciuto, G.; Napoli, C.; Shikler, R.; Woźniak, M. Optimizing the Organic Solar Cell Manufacturing Process by Means of AFM Measurements and Neural Networks. Energies 2018, 11, 1221.

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