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
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;
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
DOI :
10.1109/ICOCI.2006.5276510