Design For Manufacturing (DFM) is a TQM methodology by which inherently producible products can be manufactured with high yields, short turnaround time and great flexibility. The key to the success of any DFM program lies in increased accuracy in the modeling of the process and product designs, product simulations and effective manufacturing feedback of key parametric data. That is, properly modeling and simulating designs with data which reflects current fabrication capabilities has the most lasting influence in the performance of products. It is this area that is tackled in the methodology developed hereafter; a method by which to feedback and feedforward parametric data critical to the performance of Digital VLSI systems for performance prediction purposes. In this method, integrated circuit and applied statistics concepts are used jointly to perform analyses and inferences on response variables as a function of key processing and design variables that can be statistically controlled. Furthermore, an experimental design procedure utilizing electrical simulation is proposed to efficiently collect data and test previously proposed hypotheses. Conclusions are finally made with regard to the usefulness and outreach of this method, as well as those areas affected by the behavior of the performance predictors, both in the design and manufacturing stages of VLSI engineering
