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

探索性研究小资助金(SGER)项目的目标是应用(概率)决策理论，找到一种量化关系，将测量的传感器信号特征与化学机械平坦化过程中的动态和材料去除速率联系起来，用于终点检测和控制。该方法将使用序贯贝叶斯模型根据测量的振动和其他传感器信号在每个时间步长估计过程的状态。将定义(流程状态和决策的)效用函数，并在每个时间步对其进行评估。是否停止机器或继续抛光将根据将预期效用最小化的替代方案在每个时间步长做出决定。这项研究将结合化学机械抛光科学、传感器和信号分析以及贝叶斯决策理论方面的PI的互补专业知识。如果成功，这项探索性努力将引入一种根植于决策理论的全新方法，用于实时预测和控制半导体制造过程中的质量和生产率。该方法可以推广到其他超精密制造过程和复杂系统中的质量和性能的预测和控制。传感器联网抛光机可以作为指导和培训学生以及行业人员的试验台。这项研究将与私人投资机构的研究生课程相结合。PIs将继续让来自代表性不足群体的本科生和研究生作为其研究的一部分。他们将接触到先进制造工艺中的基础多学科研究和行业实践。研究重点将在PIS的网站上提供，并在各种期刊、行业杂志和会议记录上发表。

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.