Multi-Level Pseudo-Random Signal Design and 'Model-on-Demand' Estimation Applied to Nonlinear Identification of a RTP Wafer Reactor

Guidelines are presented for specifying the design parameters of multi-level pseudo-random sequences in a manner useful for “plant-friendly” nonlinear system identification. These multi-level signals are introduced into a rapid thermal processing wafer reactor simulation and compared against a well-designed pseudo-random binary sequence (PRBS). The resulting data serves as a database for a “model on demand” (MoD) predictor. MoD estimation is attractive because it requires less engineering effort to model a nonlinear plant, compared to global nonlinear models such as neural networks. The improved fit of multi-level signals over the PRBS signal, as well as the usefulness of the MoD estimator, is demonstrated on validation data