Abstract
A feed-forward artificial neural network was employed in order to predict the growth rate and composition of AlxGa1-xN metalorganic vapor deposition-grown layers depending on the flow of a precursor gas (trimethylaluminum, trimethylgallium, and ammonia). The results showed a good agreement with experimental data. The obtained dependencies seem to be reasonable in the context of well-known effects: the presence of parasitic reactions between trimethylaluminum and ammonia and GaN decomposition at low ammonia (high hydrogen) flow.
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