Prediction of wafer state after plasma processing using real-time tool data

Empirical models based on real-time equipment signals are used to predict the outcome (e.g., etch rates and uniformity) of each wafer during and after plasma processing. Three regression and one neural network modeling methods were investigated. The models are verified on data collected several weeks after the initial experiment, demonstrating that the models built with real-time data survive small changes in the machine due to normal operation and maintenance. The predictive capability can be used to assess the quality of the wafers after processing, thereby ensuring that only wafers worth processing continue down the fabrication line. Future applications include real-time evaluation of wafer features and economical run-to-run control