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 ne ural 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 value s of set-point fluctuations for the control variables of the plasma re actor, and the output is the oxide thickness remaining after the etch, The model is thus abstracted by several levels of reality. The real-w orld process results in a film thickness about 24 000 Angstrom and a s tandard deviation of about 730 Angstrom. We demonstrate that a neural network model can predict the post-etch oxide thickness to within 480 Angstrom and that inherent noise in the training/testing data is 416 A ngstrom. We also demonstrate that the de bias and the etch timese are the most important variables to determine the final product quality.