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.
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 process regulation, EPS has gained a lot of popularity in continuous process industries. Practitioners of SPC argue that because of the complexity of most manufacturing process, EPC methods can over-control a process and increase process variability before decreasing it. Moreover, important quality events may be masked by frequent adjustments and become difficult to be detected and removed. Conversely, practitioners of EPC criticize SPC method as being exclusive of the opportunities for reducing the variability in the process output. Traditional SPC methods generate many false alarms and fail to discriminate quality deterioration from the in-control state defined by SPC rules. Recently EPC and SPC has been integrated in the semiconductor manufacturing and resulted in a tremendous improvement of industrial efficiency. Box and Luceno  refer to EPC activities as process adjustment and SPC activities as process monitoring. While the two approaches have been applied independently in different areas for decades, the relationship between them has not been clearly explored yet. Section 2 of this paper reviews various SPC and EPC techniques for industrial process control. Section 3 presents the integrated model of EPC/SPC. Section 4 reviews several cutting-edge statistical process control methods for monitoring auto correlated and EPC processes. Section 5 presents a case study of a chemical mechanical planarization (CMP) process to demonstrate the utility of the EPC/SPC method. Section 6 presents some concluding remarks.
Two Process Control Approaches
Engineering Process Control
Engineering process control is a popular strategy for process optimization and improvement. It describes the manufacturing or information manipulation process as an input-output system where the input variables (recipes) can be manipulated (or adjusted) to counteract the uncontrollable disturbances to maintain the process target. The output of the process...