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Neuronale Modellbildung und Temperaturregelung eines Vertikalofens für die Halbleiterfertigung

: Benesch, N.; Hofmann, U.; Schneider, C.; Ryssel, H.

Pirsch, P. ; Institute of Electrical and Electronics Engineers -IEEE-; Univ. Hannover; Informationstechnische Gesellschaft -ITG-; VDE/VDI-Gesellschaft Mikroelektronik, Mikro- und Feinwerktechnik -GMM-:
Mikroelektronik für die Informationstechnik. Vorträge der ITG-Fachtagung am 3. und 4. März 1998 in Hannover
Berlin: VDE-Verlag, 1998 (ITG-Fachbericht 147)
ISBN: 3-8007-2325-5
Fachtagung Mikroelektronik für die Informationstechnik <8, 1998, Hannover>
Conference Paper
Fraunhofer IIS B ( IISB) ()
furnace control; neural control; neural system identification; predictive control; temperature control

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.