报告题目：Engineering-Knowledge-Driven Statistical Modeling for Spatial Data
Engineering-Knowledge-Driven Statistical Modeling for Spatial Data
Dr. Kaibo Wang
Department of Industrial Engineering
Beijing 100084, China
In certain complex manufacturing systems, the quality of a product is adequately characterized by a high-dimensional data map rather than by single or multiple variables. Such data maps also preserve unique spatial structures. Therefore, variation pattern analysis and statistical modeling based on the data map becomevery important for enhanced process understanding and quality improvement.
Using a real wafer example from semiconductor manufacturing and a carbon nano tube example from nano-manufacturing, we demonstrate how statistical models can be developed by incorporating engineering knowledge. In the wafer example, a three-stage hierarchical model is proposed. The wafer surface variation is decomposed into the macro- and micro-scale variations, which are modeled as a cubic curve and a first-order intrinsic Gaussian Markov random field, respectively. In the carbon nano tube example, a piece-wise polynomial model with spatial auto-regressive disturbance is developed. These examples show that engineering knowledge driven statistical modelingcan play an important role in quality control of complex systems, and is also a promising area for statistical research.
Dr. Kaibo Wang is a Professor in the Department of Industrial Engineering at Tsinghua University, Beijing, China. He received his Ph.D. degree in Industrial Engineering and Engineering Management from the Hong Kong University of Science and Technology (HKUST), Hong Kong. Dr. Wang’s research focuses on the modeling, monitoring and control of complex systems. He is on the Editorial Review Board of Journal of Quality Technology, and an Associate Editor for IISE Transactions,Quality Technology & Quantitative Management, and Quality Engineering. For more information about Dr. Wang, please visit http://www.ie.tsinghua.edu.cn/kbwang/ .