Time series modeling of reactive ion etching using neural networks

Neural networks have been used to model the behavior of real-time tool data in a reactive ion etch (RIE) process. An etch monitoring and data acquisition system for transferring data from the RIE chamber to a remote workstation was designed and implemented on a Plasma Therm Series 700 Dual Chamber etcher. This system monitors gas flow rates, RF power, temperature, pressure, and dc bias voltage. A neural network was trained on the monitored data using the feed-forward, error backpropagation algorithm. This network was used to perform three distinct modeling tasks. First, the network was trained on a subset of ten samples of the time series representing a single process run, and subsequently used to forecast the next data point. In the second task, the network was trained as in the first task, but used to predict the next ten values of the data sequence. In each of the first two tasks, the trained network yielded errors of less than 5%. In the final task, a neural net was used to generate a malfunction alarm when the sampled data did not conform to its previously established pattern