A neural network model of a contact plasma etch process for VLSI production

The etch process for preparation of via contacts in VLSI manufacturing is described along with a neural network model of the process. The neural network is a two hidden layer network (23-3-3-1) trained by error back-propagation. The input variables to the model are the mean values of set-point fluctuations for the control variables of the plasma reactor, and the output is the oxide thickness remaining after the etch. The model is thus abstracted by several levels of reality. The real-world process results in a film thickness about 24 000 /spl Aring/ and a standard deviation of about 730 /spl Aring/. We demonstrate that a neural network model can predict the post-etch oxide thickness to within 480 /spl Aring/ and that inherent noise in the training/testing data is 416 /spl Aring/. We also demonstrate that the dc bias and the etch times are the most important variables to determine the final product quality.

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