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.