Artificial neural-network-based diagnosis of CVD barrel reactor

This paper presents an artificial neural network (ANN) based diagnostic strategy applied to a chemical vapor deposition (CVD) barrel reactor of the type commonly used in silicon epitaxy. The strategy is based on the spatial variation of the rate of deposition of silicon on a facet of the reactor. Our hypothesis is that this spatial variation, quantified as a vector of variously measured standard deviations, encodes a pattern reflecting the state of the reactor. Therefore, a process fault (event) can be diagnosed by decoding the pattern by an ANN. We implemented this simple scheme by simulating different events by means of a regression model relating the rate of deposition to the process settings. Three different events were simulated and various ANNs were trained to detect and classify these events. It is shown that a single ANN or a combination of ANNs does an excellent job. We also demonstrate that the threshold rule for setting the threshold of a binary output neuron performing a classification task enhances the diagnostic performance. A novel multiple expert scheme that refers to several ANNs trained in the same classification task for decision-making in order to resolve ambiguities and improve the reliability of the final decision is presented and shown to be effective.