A Multivariate Moving Average Control Chart for Photovoltaic Processes
For the electrical metrics that describe photovoltaic
cell performance are inherently multivariate in nature, use of a
univariate, or one variable, statistical process control chart can have
important limitations. Development of a comprehensive process
control strategy is known to be significantly beneficial to reducing
process variability that ultimately drives up the manufacturing cost
photovoltaic cells. The multivariate moving average or MMA chart,
is applied to the electrical metrics of photovoltaic cells to illustrate
the improved sensitivity on process variability this method of control
charting offers. The result show the ability of the MMA chart to
expand to as any variables as needed, suggests an application
with multiple photovoltaic electrical metrics being used in
concert to determine the processes state of control.
[1] Coleman J. (1999). Process Optimization and Control of a Photovoltaic
Manufacturing Process. Extended Abstracts and Papers 9th Workshop
on Crystalline Sillicon Solar Cell Material and Processes, 171-174.
[2] Goulding, P.R.,et al. (2000). Fault detection in continuous processes
using multivariate statistical methods. International Journal of Systems
Science 31(11), 1459-1471.
[3] Lucas, J.M.,&Saccucci, M.S.(1990). Exponential weight moving
average control schemes: Properties and enhancements Technometrics,
32.
[4] McCafferty, R.H.(2001). High road to process control: Multivariate
methods Semiconductor International, 24(8), 257-261.
[5] Minitab Inc. (2003). Statguide Minitab Statistical Software v14.State
College PA.
[6] Montgomery D, (2001). Introduction to Statistical Quality Control,
Fourth Editor, John Wiley, NY.
[7] Prabhu, S.S.,&Runger, G.C. (1997). Designing a multivariate EWMA
control chart. Journal of Quality Technology, 26.
[8] Skinner, K.R.et al. (2002). Multivariate statistical methods for modeling
and analysis of wafer probe test data. IEEE Transactions on
Semiconductor Manufacturing, 15(4), 523-530.
[9] Tseng, S.T., Chou, R.J.&Leee, S.P. (2002). A study on amultivariate
EWMA controller. IIE Transactions 34, 541-549.
[1] Coleman J. (1999). Process Optimization and Control of a Photovoltaic
Manufacturing Process. Extended Abstracts and Papers 9th Workshop
on Crystalline Sillicon Solar Cell Material and Processes, 171-174.
[2] Goulding, P.R.,et al. (2000). Fault detection in continuous processes
using multivariate statistical methods. International Journal of Systems
Science 31(11), 1459-1471.
[3] Lucas, J.M.,&Saccucci, M.S.(1990). Exponential weight moving
average control schemes: Properties and enhancements Technometrics,
32.
[4] McCafferty, R.H.(2001). High road to process control: Multivariate
methods Semiconductor International, 24(8), 257-261.
[5] Minitab Inc. (2003). Statguide Minitab Statistical Software v14.State
College PA.
[6] Montgomery D, (2001). Introduction to Statistical Quality Control,
Fourth Editor, John Wiley, NY.
[7] Prabhu, S.S.,&Runger, G.C. (1997). Designing a multivariate EWMA
control chart. Journal of Quality Technology, 26.
[8] Skinner, K.R.et al. (2002). Multivariate statistical methods for modeling
and analysis of wafer probe test data. IEEE Transactions on
Semiconductor Manufacturing, 15(4), 523-530.
[9] Tseng, S.T., Chou, R.J.&Leee, S.P. (2002). A study on amultivariate
EWMA controller. IIE Transactions 34, 541-549.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:58294", author = "Chunchom Pongchavalit", title = "A Multivariate Moving Average Control Chart for Photovoltaic Processes", abstract = "For the electrical metrics that describe photovoltaic
cell performance are inherently multivariate in nature, use of a
univariate, or one variable, statistical process control chart can have
important limitations. Development of a comprehensive process
control strategy is known to be significantly beneficial to reducing
process variability that ultimately drives up the manufacturing cost
photovoltaic cells. The multivariate moving average or MMA chart,
is applied to the electrical metrics of photovoltaic cells to illustrate
the improved sensitivity on process variability this method of control
charting offers. The result show the ability of the MMA chart to
expand to as any variables as needed, suggests an application
with multiple photovoltaic electrical metrics being used in
concert to determine the processes state of control.", keywords = "The multivariate moving average control chart,
Photovoltaic processes control, Multivariate system.", volume = "3", number = "8", pages = "578-4", }