journal article·2024-01-01Open access

Process dispersion monitoring: Innovative AEWMA control chart in semiconductor manufacturing

Imad Khan, Muhammad Noor-ul-Amin, Muhammad Usman Aslam, Almetwally M. Mostafa, Bakhtiyar Ahmad

AIP Advances· Volume 14· Issue 1

Abstract

This study introduces a novel adaptive exponentially weighted moving average (AEWMA) control chart for monitoring process dispersion, employing adaptation to determine the smoothing constant. It is designed to effectively track shifts within expected ranges in process dispersion by computing the smoothing constant through a suggested adaptive approach. To determine its efficacy, the chart’s performance is evaluated by using smaller run-length profiles derived from Monte Carlo simulations. A key feature is the utilization of an unbiased estimator to calculate the smoothing constant via the proposed function, enhancing the chart’s ability to detect various magnitudes of decreasing and increasing process dispersion shifts. A comparison with an existing adaptive EWMA dispersion chart underscores the significant efficiency of the proposed chart across various types of process dispersion shift magnitudes. Moreover, the study encompasses a real-life dataset application, demonstrating the practical implementation and ease of use in real-world scenarios.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Advanced Statistical Process Monitoring · Advanced Statistical Methods and Models · Fault Detection and Control Systems

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Statistics, Abdul Wali Khan University Mardan 1 , Mardan 23200, Pakistan

Department of Statistics, COMSATS University Lahore Campus 2 , Lahore 54000, Pakistan

Department of Statistics, Xi’an Jiaotong University 3 , Xi’an 710032, China

Department of Information Systems, College Computers and Information Science, King Saud University 4 , Riyadh 11574, Saudi Arabia

Higher Education Department 5 , Kabul 1001, Afghanistan

Funding

King Saud University

References (26)

Show source-provided references
1. The application of statistics as an aid in maintaining quality of a manufactured product10.1080/01621459.1925.10502930 ↗
2. Control chart tests based on geometric moving averages10.1080/00401706.1959.10489860 ↗
3. Continuous inspection schemes10.1093/biomet/41.1-2.100 ↗
4. An EWMA for monitoring a process standard deviation10.1080/00224065.1992.11979369 ↗
5. A new EWMA chart for monitoring process dispersion10.1080/00224065.2008.11917737 ↗
6. New EWMA control charts for monitoring process dispersion10.1016/j.csda.2010.03.011 ↗
7. On proper choice of variability control chart for normal and non-normal processes10.1002/qre.1244 ↗
8. Memory-type control charts for monitoring the process dispersion10.1002/qre.1514 ↗
9. The double sampling S2 chart with estimated process variance10.1080/03610926.2015.1066814 ↗
10. A control chart for monitoring process variation using multiple dependent state sampling10.1080/03610918.2017.1341524 ↗
11. New EWMA S2 control charts for monitoring process dispersion10.24200/sci.2017.4041 ↗
12. A new adaptive EWMA control chart for monitoring the process dispersion10.1002/qre.2294 ↗
13. Monitoring process variation using modified EWMA10.1002/qre.2576 ↗
14. A S2-GWMA control chart for monitoring the process variability10.1080/08982112.2021.1936553 ↗
15. A maximum adaptive exponentially weighted moving average control chart for monitoring process mean and variability10.1080/16843703.2018.1530181 ↗
16. A Bayesian control chart for monitoring process variance10.3390/app11062729 ↗
17. A new S2-TEWMA control chart for monitoring process dispersion10.1002/qre.2798 ↗
18. Enhanced dispersion monitoring structures based on modified successive sampling: Application to fertilizer production process10.3390/sym15051108 ↗
19. Generalized new exponentially weighted moving average control charts (NEWMA) for monitoring process dispersion10.1108/ijqrm-08-2022-0257 ↗
20. Control charts for profile monitoring of within-profile correlations using the Tweedie exponential dispersion process model10.1080/00949655.2022.2111428 ↗
21. A ridge penalized likelihood ratio chart for Phase II monitoring of high-dimensional process dispersion under measurement system inaccuracy
22. An adaptive exponentially weighted moving average control chart for monitoring process variances10.1080/00949650601108000 ↗
23. Adaptive CUSUM procedures with EWMA-based shift estimators10.1080/07408170801961412 ↗
24. An efficient adaptive EWMA control chart for monitoring the process mean10.1002/qre.2272 ↗
25. Design of a new adaptive EWMA control chart10.1002/qre.3141 ↗
26. 2009