• DocumentCode
    1609255
  • Title

    A multi-layer perseptron network model for a quantum-well laser diode

  • Author

    Celebi, Fatih V. ; Danisman, Kenan

  • Author_Institution
    Fac. of Eng., Baskent Univ., Ankara, Turkey
  • fYear
    2006
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    This study presents a different approach to compute the modal peak gain, differential modal refractive index change, and the linewidth enhancement factor (alpha parameter) of an InGaAs deep quantum-well laser sample from a single model. Each of these optical quantities requires many calculations with the use of different theories, assumptions, and estimations in addition to strong background knowledge. The approach is based on artificial neural networks (ANNs) which are capable of representing complex input/output relationship. The model results agree well with the measured data from an InGaAs quantum-well (QW) laser sample.
  • Keywords
    III-V semiconductors; gallium arsenide; indium compounds; multilayer perceptrons; optical computing; quantum well lasers; InGaAs; artificial neural networks; differential modal refractive index; linewidth enhancement factor; multilayer perceptron network model; quantum-well laser diode; Artificial neural networks; Diodes; Estimation theory; Indium gallium arsenide; Optical computing; Optical refraction; Optical variables control; Quantum computing; Quantum well lasers; Refractive index;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing & Informatics, 2006. ICOCI '06. International Conference on
  • Conference_Location
    Kuala Lumpur
  • Print_ISBN
    978-1-4244-0219-9
  • Electronic_ISBN
    978-1-4244-0220-5
  • Type

    conf

  • DOI
    10.1109/ICOCI.2006.5276510
  • Filename
    5276510