This grant provides funding for developing models to understand the influences and contributions of sub-process outputs on product performance characteristics, such as yield, conformance to specifications, defectivity, customer returns, or other quality measures in a semiconductor manufacturing environment. Modeling relationships between fabrication processes and post-fabrication assessment is important. However, high dimensional complex systems, such as this, present a number of unique challenges in data analysis for understanding. Methodological approaches will be developed to solve the complex modeling problems that arise in semiconductor manufacturing by utilizing Generalized Linear Models (GLMs) in a hierarchical structure. This will be accomplished by first developing the foundation for integrating data structures that will be subsequently used in building and evaluating hierarchical models. This research will also extend the theory of flat, linear models into a hierarchical structure.
If successful, the results of this research will lead to improvements in operational modeling and hierarchical models. Characterizing the relationships at the operational level will improve productivity, identify and validate quality control parameters, and enable evaluation of competing process models. The results of this research will impact manufacturing operations in that it will improve the utilization of technical resources and provide the means to examine the validity and effectiveness of current operating methods and metrics. Methodological advances will be made in several areas: advance the state of the art in using data to estimate high-dimensional systems by means of hierarchical modeling; exploring the interaction between data manipulation strategies and modeling large and complex manufacturing systems; and developing high dimensional modeling algorithms in combination with advances in nonlinear statistical modeling.
