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1998
Conference Paper
Titel
Neuronale Modellbildung und Temperaturregelung eines Vertikalofens für die Halbleiterfertigung
Abstract
Due to rising costs and the call for innovations control technology in semiconductor equipment gains in importance. Compared to PID-control systems which have been most frequently deployed in manufacturing equipment so far, new intelligent control systems increase flexibility and yield. shorten ramp-up times as well as cycle times, and reduce therefore the production costs. The key word frequently used in this context is: model-based control. Since in many cases the physical model establishment of the controlled process is very complicated, a neural network serves in this work as a model which enables besides a fast model establishment also the adaptation to changing system conditions. The advantages of a predictive neural control system have been verified by simulating and controlling of an oxidation and LPCVD vertical furnace.