MODEL IDENTIFICATION IN RAPID THERMAL-PROCESSING SYSTEMS

Of the various techniques for controlling the temperature in rapid thermal processing (RTP), model-based control has the greatest potential for attaining the best performance, when the model is accurate. In this paper, some so-called system identification methods are introduced to help obtain more accurate models from measured input-output data. In the first identification, techniques are presented to estimate the parameters (time constant and gain) of a particular physics-based model. In the other, we also show how to use the input-output measurements to obtain a black-box model (autoregressive exogenous model) of the RTP system, which turns out to have better predictive capability. For each problem, a brief summary of the theoretical derivation of the identification technique and assumptions on which it is based is given and experimental results based on data collected from an RTP system are described. The identified model is useful not only for control law design but also to study certain system characteristics. Studying the so-called DC response in this way led us to reconfigure the chamber geometry of the existing RTP system to more effectively redistribute the light energy from the lamps.