Neural network structures for optimal control of LPCVD reactors

In this paper, a new approach of LPCVD reactor modelling and control is pre sented, based on the use of neural networks. We first present the developme nt of a hybrid networks model of the reactor. The objective is to provide a simulation model which can be used to compute online the film thickness on each wafer. In the second section, the thermal control of a LPCVD reactor is studied The objective is to develop a multivariable controller to contro l a space- and time-varying temperature profile inside the reactor. A neutr al network is designed using a methodology based on process inverse dynamic s modelling. Good control results have been obtained when tracking space-ti me temperature profiles inside the LPCVD reactor pilot plant. Finally globa l software is elaborated to achieve film thickness control in an experiment al LPCVD reactor pilot plant, in order to get a defined and uniform deposit ion thickness on the wafers all along the reactor. Experimental results are presented which confirm the efficiency of the optimal control strategy.