A study on failure prediction in a plasma reactor

It is shown that neural networks can induce, or self-build, Markov models from process data and these models can be used to predict system state to a significant distance in the future (e.g., 100 wafers).

Abstract

We use several approaches to demonstrate that neural networks can detect precursors to failure. That is, they can detect subtle changes in the process signals. In some cases these subtle changes are early warnings that a subsystem failure is imminent. The results on detection of precursors and faults with various types of time-delay neural networks are discussed. We also measure the noise inherent in our database and place bounds on neural network prediction in the presence of noise. We observe that the noise level can be as high as 40% for detection of failures and can be at 30% to still detect precursors to failure. We note that although self-organizing networks for classification of faults seems like a good idea, in fact they do not perform well in the presence of noise. Lastly, we show that neural networks can induce, or self-build, Markov models from process data and these models can be used to predict system state to a significant distance in the future (e.g., 100 wafers).