A systematic equipment modeling and characterization methodology has been developed for automated VLSI manufacturing. This methodology is based on the development of generic first principle process models. These generic models are subsequently refined and fitted to specific manufacturing equipment, using a multi-stage D-optimal experimental design. The methodology has been successfully applied to a low pressure chemical vapor deposition (LPCVD) furnace for undoped polysilicon deposition. A 2-stage D-optimal experiment with 24 runs has yielded statistical fitted models for the film growth rate and film residual stress. The calibrated models agree well with the experimental data, and account for the observed process variations. Achieving consistent and high quality operation for each VLSI manufacturing process step can be challenging. The task of optimizing the process yield can be simplified through the application of statistical semi-empirical equipment models for automated process design and control. In order to accommodate the multiple objectives of semiconductor manufacturing, a numerical optimizer is integrated with a highly interactive interface. The combination can assist the process engineer in choosing the best compromise of equipment settings in terms of product and equipment performance. Currently, the statistical LPCVD models we developed are being used in a computer-aided design system that synthesizes optimal manufacturing process steps for the LPCVD of undoped polysilicon. The system will generate recipes that achieve objectives not just related to the average value of the film properties, but to their uniformity as well.