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Proceedings Paper

Using genetic algorithms for developing amorphous silicon atomistic model
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Paper Abstract

In this paper, the author presents a computer algorithm for the generation of a high-quality continuous random networks using a genetic algorithm (GA). Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence. As a one can guess, genetic algorithms are inspired by Darwin's theory about evolution. Simply said, solution to a problem solved by genetic algorithms is evolved. This paper formulates the amorphous silicon atomistic model problem such that a genetic algorithm can be designed to solve it. A population of models are generated randomally at the start. A sequence of genetic processes such as individuals regeneration, feature cross-over and mutation are performed to produce new generations of the models. After many generations the optimal solution is reached. A series of computer simulations are used to predict many of the structural and electronic properties of the amorphous silicon. The results are compared with the experimental values for these physical parameters mentioned in the literature for testing the model accuracy. Also, a comparison between the suggested model and the other famous computer-based algorithms is presented. The results are discussed.

Paper Details

Date Published: 25 July 2003
PDF: 9 pages
Proc. SPIE 4986, Physics and Simulation of Optoelectronic Devices XI, (25 July 2003); doi: 10.1117/12.473070
Show Author Affiliations
Somia Mostafa El-Hefnawy, Mansoura Univ. (Egypt)

Published in SPIE Proceedings Vol. 4986:
Physics and Simulation of Optoelectronic Devices XI
Marek Osinski; Hiroshi Amano; Peter Blood, Editor(s)

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