Abstract
We compare several parameter identification methods for data-driven identification and validation of an empirical linear dynamic model for a helicon plasma reactor. The model relates easily measurable process variables to ellipsometry measurements from which the etch depth can be determined in real time. The potential use of such a model for process control is obvious. The model developed shows improvement over a neural network model developed in a previous study based upon the same data.
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