In-situ prediction of reactive ion etch endpoint using neural networks

A reactive ion etching system uses a back-propagation neural network-based system for predicting the etch endpoint of a silicon dioxide film in a CHF3/O2 plasma. The neural network correlates the etch rate with the dc bias to determine the remaining time necessary to etch a known thickness of silicon dioxide. Monitoring of the dc bias is done using a real time data acquisition system, where process conditions are sent from a Plasma Therm 700 series reactive ion etching system. Results obtained using the network are discussed.