This paper demonstrates the incorporation of a multilayer neural network in semiconductor thin film deposition processes. As a first step toward neural network-based process control, we present results from neural network pattern classification and beam analysis of reflection high energy electron diffraction RHEED images of GaAs/AlGaAs crystal surfaces during molecular beam epitaxy growth. For beam analysis, we used the neural network to detect and measure the intensity of the RHEED beam spots during the growth process and, through Fourier transformation, determined the thin film deposition rate. The neural network RHEED pattern classification and intensity analysis capability allows, powerful in situ real time monitoring of epitaxial thin film deposition processes. Our results show that a three layer network with sixteen hidden neurons and three output neurons had the highest correct classification rate with a success rate of 100% during testing and training on 13 examples.
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