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

SGER：化学机械平坦化 (CMP) 工艺中终点检测和控制的顺序贝叶斯决策

• 批准号: 0830023
• 负责人: Satish Bukkapatnam
• 金额: $ 8万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2009-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830023&HistoricalAwards=false
• 关键词: SGER; Sequential; Bayesian; Decision; Making

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.

这个探索性研究（SGER）项目的目的是应用（概率）决策理论，找到一个定量的关系，连接测得的传感器信号的功能与动态状态和材料去除率在化学机械平坦化过程中的终点检测和控制。该方法将使用顺序贝叶斯模型来估计在每个时间步的状态的过程中从测量的振动和其他传感器信号。将定义（流程状态和决策的）效用函数，并在每个时间步进行评估。在每一个时间步，将根据使预期效用最小化的替代方案来决定是停止机器还是继续抛光。该研究将结合联合收割机在化学机械抛光科学、传感器和信号分析以及贝叶斯决策理论方面的专业知识。如果成功，探索性的努力将引入一种全新的方法，植根于决策理论，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的质量和性能预测和控制。传感器网络抛光机可以作为学生以及行业人员的教学和培训的试验台。这项研究将与PI的研究生课程相结合。PI将继续让来自代表性不足群体的本科生和研究生参与其研究。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PI的网站上提供，并发表在各种期刊，行业杂志和会议论文集上。