Optimizing the organic solar cell manufacturing process by means of AFM measurements and neural networks

Giacomo Capizzi, Grazia Lo Sciuto, Christian Napoli, Rafi Shikler, Marcin Wozniak

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

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.

Original languageEnglish
Article number1221
JournalEnergies
Volume11
Issue number5
DOIs
StatePublished - 1 Jan 2018

Keywords

  • Nanoplasmonics
  • Nanotechnologies
  • Neural networks
  • Photonics

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering

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