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Published online by Cambridge University Press: 22 September 2014
Plasma etching process plays a critical role in semiconductor manufacturing. Becausephysical and chemical mechanisms involved in plasma etching are extremely complicated,models supporting process control are difficult to construct. This paper uses a 35-runD-optimal design to efficiently collect data under well planned conditions for importantcontrollable variables such as power, pressure, electrode gap and gas flows ofCl2 and He andthe response, etching rate, for building an empirical underlying model. Since therelationship between the control and response variables could be highly nonlinear, ageneralized regression neural network is used to select important model variables andtheir combination effects and to fit the model. Compared with the response surfacemethodology, the proposed method has better prediction performance in training and testingsamples. A success application of the model to control the plasma etching processdemonstrates the effectiveness of the methods.