CAREER: Multivariate Quality Control of Semiconductor Manufacturing Processes via Adaptive Optimizing Controllers

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9623669 Del Castillo This research aims to develop effective multivariate quality control (QC) methods for advanced semiconductor manufacturing processes. Specifically of concern is the development and implementation of a novel multiple-input-multiple-output quality controller for supervisory control of semiconductor manufacturing processes based on the idea of optimizing adaptive control. In many of these processes, the quality characteristics of a batch or run of silicon wafers are regulated with automatic controllers of the proportional-integral-derivative (PID) type, that regulates variables such as pressures, flows, temperatures, etc. The proposed optimizing adaptive quality controller (OAQC) provides a recipe of PID set points at each run by the on-line estimation and optimization of a nonlinear transfer function model. The OAQC will be developed and tested in close collaboration with Sematech's Statistical Methods and Advanced Process Control Group. The educational component of this project is integrated with the proposed research and reflects the PI's philosophy on teaching modern QC courses an interdisciplinary field on the intersection between applied statistics and control theory. The research and integrated educational plan is well thought out and the industrial collaboration excellent. The PI already has a strong track record and is felt to be extremely competent. The area of research, QC, is of particular importance to the semiconductor manufacturing industry.