Integrating SPC and EPC Methods for Quality Improvement

Abstract

Process variations are classified into common cause and assignable cause variations in the manufacturing and services industries. Common cause variations are inherent in a process and can be described implicitly or explicitly by stochastic methods. Assignable cause variations are unexpected and unpredictable and can occur before the commencement of any special events. Reducing process variations are critical for industries with a low tolerance for variability such as semiconductor manufacturing. While engineering process control (EPC) methods such as feedback/feedforward controllers are widely employed in continuous process industry to reduce common cause variations, statistical process control (SPC) methods have been successfully utilized in discrete parts industry through identification and elimination of the assignable cause of variations. Recently, integration of EPC and SPC methods has emerged in the semiconductor manufacturing industry and has resulted reducing manufacturing waste and improving process efficiency. This paper provides a review of various control techniques and develops a unified framework to model the relationships among these well-known methods in EPC, SPC, and integrated EPC/SPC. A case study centered on chemical mechanical planarization process demonstrates the utility of this framework.

Key Words:

• Automatic process control
• chemical mechanical planarization
• control charts
• run-to-run control
• semiconductor manufacturing

Additional information

Notes on contributors

Wei Jiang

Wei Jiang Assistant Professor of Systems Engineering and Engineering Management at Stevens Institute of Technology. His interests are in the areas of statistical methods for quality control, business forecasting, and data mining for knowledge discovery, statistical methods for quality control, data mining and enterprise intelligence. He has published widely in the engineering and statistical literature. Dr. Jiang is currently serving as a council member for Data Mining section and Secretary/Treasurer of Quality, Statistics, and Reliability section in Institute of Operations Research and Management Sciences (INFORMS).

John V. Farr

John V. Farr Professor of Systems Engineering and Engineering Management at Stevens Institute of Technology, Hoboken, New Jersey. His education includes a BS in Civil Engineering (CE) from Mississippi State University, MSCE from Purdue University, and PhD in CE from the University of Michigan. He has authored or co-authored one book and over eighty other technical publications in wide variety of fields including military engineering, modeling and simulation, engineering and technology management, and infrastructure assessment and management. After 10 years in government and industry, Dr. Farr joined the faculty at the U.S. Military Academy in 1992 as an Assistant Professor of Engineering Management and was promoted to Professor in 2000. He was responsible for the Academy’s nationally recognized Engineering Management Program from 1997 to 2000. Dr. Farr joined the faculty of Stevens in 2000 as the Founding Director of the Department of Systems Engineering and Engineering Management. Dr. Farr is a Fellow in both the American Society of Engineering Management (ASEM) and the American Society of Civil Engineers and served as President of ASEM for 2002–2003. He is a registered Civil Engineer in the states of New York and Mississippi, serves on the Army Science Board, and as a consultant in the areas of data analysis and modeling and simulation.