<title>Neural network and fuzzy models for real-time control of a CVD epitaxial reactor</title>

Abstract

Controlling variability at each of the several processing steps in a wafer fabrication facility is a key concern for a semiconductor manufacturer. All the variables controlling the desired output must be understood and optimized for high yield. In addition, the process controller must be quick and responsive. For typical semiconductor manufacturing processes, which are very complex, designing an effective controller meeting these requirements is a challenging job. Several process control techniques are being pursued. In one of the common approaches, the statistical model based on empirical data and linear models such as auto-regression and moving averages are used. However, these models represent a complex process only in relatively small neighborhoods in the state space. Another approach is to use artificial neural network and fuzzy logic techniques to produce non-linear process models for real time process control. This paper provides a comparison of these different techniques for application to semiconductor manufacturing. The specific process chosen is the CVD-epitaxial deposition of silicon in a horizontal reactor. Analytical model is used to generate data under simulated production conditions. The input parameters are inlet concentration of silane, inlet velocity, susceptor temperature, and the downstream position. The output is the silicon deposition rate. Eighty four data sets are used to train both the neural net and fuzzy logic models. These models are then used to predict the output as a function of input parameters for fourteen additional data sets. A comparison of these predictions with the physical model's computational results and the experimental data shows good agreement. Further work is in progress to fully exploit the potential of physico-neural and physico-fuzzy models for run-to- run real-time process control.