Integrating Spc and Epc Methods for Quality Improvement
Integrating SPC and EPC Methods for Quality Improvement.
Abstract: This term paper are mainly based on the research paper, which was written by Wei Jiang and John V. Farr. Process variations are classified into common cause and assignable cause variations in the manufacturing and services industries. Firstly, the authors pointed out attention, that 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. Statistical process control (SPC) methods has been successfully utilized in discrete parts industry through identification and elimination of the assignable cause of variations, while engineering process control methods (EPC) are widely employed in continuous process industry to reduce common cause variations. 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, which have been successfully implement in the semiconductor manufacturing.
Keywords: Automatic process control, chemical mechanical planarization, control charts, run-to run control, semiconductor manufacturing. Introduction
Two categories of research and applications have been developed independetely to achieve process control. Statistical process control (SPC) uses measurements to monitor the process and look for major changes in order to eliminate the root causes of the changes. Statistical process control has found widespread application in the manufacturing of discrete parts industries for process improvement, process parameter estimation, and process capability determination. Engineering process control (EPC), on the other hand, uses measurements to adjust the process inputs intended to bring the process outputs closer to targets. By using feedback/feedforward controllers for
Abstract: This term paper are mainly based on the research paper, which was written by Wei Jiang and John V. Farr. Process variations are classified into common cause and assignable cause variations in the manufacturing and services industries. Firstly, the authors pointed out attention, that 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. Statistical process control (SPC) methods has been successfully utilized in discrete parts industry through identification and elimination of the assignable cause of variations, while engineering process control methods (EPC) are widely employed in continuous process industry to reduce common cause variations. 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, which have been successfully implement in the semiconductor manufacturing.
Keywords: Automatic process control, chemical mechanical planarization, control charts, run-to run control, semiconductor manufacturing. Introduction
Two categories of research and applications have been developed independetely to achieve process control. Statistical process control (SPC) uses measurements to monitor the process and look for major changes in order to eliminate the root causes of the changes. Statistical process control has found widespread application in the manufacturing of discrete parts industries for process improvement, process parameter estimation, and process capability determination. Engineering process control (EPC), on the other hand, uses measurements to adjust the process inputs intended to bring the process outputs closer to targets. By using feedback/feedforward controllers for