SGER: Sequential Bayesian Decision Making for End-Point Detection and Control in Chemical Mechanical Planarization (CMP) Processes

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The objective of this Small Grant for Exploratory Research (SGER) project is to apply (probabilistic) decision theory to find a quantitative relationship that connects the measured sensor signal features with the dynamic state and the material removal rate in chemical mechanical planarization process for end-point detection and control. The approach will be to use a sequential Bayesian model to estimate at each time step the state of the process from the measured vibration and other sensor signals. A utility function (of the process state and decisions) will be defined, and evaluated at each time step. A decision on whether to stop the machine or to continue polishing will be made at every time step based on the alternative that minimizes the expected utility. The research will combine the complementary expertise of the PIs in chemical mechanical polishing science, sensors and signal analysis, and Bayesian decision theory.If successful, the exploratory effort will introduce a radically new approach, rooted in decision theory, for real-time prediction and control of quality and productivity in semiconductor manufacturing processes. This methodology can be extended to prediction and control of quality and performance in other ultra-precision manufacturing processes and complex systems. The sensor-networked polishing machine can serve as a test bed for instruction and training of students as well as industry personnel. This research will be integrated with the PIs' graduate courses. The PIs will continue to involve undergraduate and graduate students from underrepresented groups as part of their research. They will be exposed to fundamental multi-disciplinary research and industry practices in advanced manufacturing processes. The research highlights will be made available in the PIs' website and published in various journals, trade magazines, and conference proceedings.