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Porous Silica-Based Optoelectronic Elements as Interconnection Weights in Molecular Neural Networks

  • Magdalena Laskowska
  • Łukasz LaskowskiEmail author
  • Jerzy Jelonkiewicz
  • Henryk Piech
  • Zbigniew Filutowicz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)

Abstract

The paper describes a unique approach to optoelectronic elements application in artificial intelligence. Previously we considered molecular neural networks on the base of the functional porous silica thin films. But, for the successful molecular neural network design, we need efficient connections among them. Therefore we are presenting a material with tuneable non-linear optical (NLO) properties to be used for the optical signal transfer. The idea is briefly described and then followed by an experimental part to validate its feasibility. Promising results show that it is possible to design and synthesize the material with tuneable NLO properties.

Keywords

Artificial intelligence Functional materials Hopfield neural network Spin-glass Molecular magnet 

Notes

Aknowledgement

Financial support for this investigation has been provided by the National Centre of Science (Grant-No: 2015/17/N/ST5/03328).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Magdalena Laskowska
    • 1
  • Łukasz Laskowski
    • 2
    Email author
  • Jerzy Jelonkiewicz
    • 2
  • Henryk Piech
    • 3
  • Zbigniew Filutowicz
    • 4
    • 5
  1. 1.Institute of Nuclear Physics, Polish Academy of SciencesKrakowPoland
  2. 2.Department of Microelectronics and NanotechnologyCzestochowa University of TechnologyCzestochowaPoland
  3. 3.Institute of Computer Science, Czestochowa University of TechnologyCzestochowaPoland
  4. 4.Information Technology InstituteUniversity of Social SciencesŁodzPoland
  5. 5.Clark UniversityWorcesterUSA

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