Organic solar cells defects detection by means of an elliptical basis neural network and a new feature extraction technique

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

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

The study proposed in this paper devises to develop a new methodology based on elliptical basis neural network (EBNN) and on a new feature extraction technique in order to recognize the organic solar cells (OSCs) defects. The feature extraction procedure has been obtained by using the co-occurrence matrices and the SVD decomposition applied to atomic microscope force imagery. The polymer-based OSCs used for this work have been produced at the optoelectronic organic semiconductor devices laboratory at Ben Gurion University of the Negev. The tests performed show that with our approach it is possible to obtain a correct classification percentage of 95.4% proving that the proposed feature extraction technique based on the co-occurrence Matrix and the SVD decomposition is very effective in the detection of different types of OSC surface defects.

Original languageEnglish
Article number163038
JournalOptik
Volume194
DOIs
StatePublished - 1 Oct 2019

Keywords

  • Atomic force microscope (AFM)
  • Elliptical basis neural network (EBNN)
  • Feature extraction procedure
  • Gray level co-occurrence matrix (GLCM)
  • Organic solar cells (OSCs)
  • Singular value decomposition (SVD)

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering

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