• Journal of the Korea Society for Simulation
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• v.16 no.2
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• pp.37-44
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• 2007

EWMA is one of the most popular controller method used in Run-to-Run control system for semiconductor manufacturing. The value of the control parameter in EWMA has major effect on the result. Therefore, it is important to use control parameter value fitting for the process state. When the process is unstable, it is more efficient to change EWMA control parameter dynamically to compensate for the changing process state than using fixed control parameter. In this paper, we review previous studies using dynamic EWMA control parameter and propose a new algorithm complementing the weaknesses of the previous studies. The performance of the proposed algorithm is validated using simulation.

• Journal of Korean Society for Quality Management
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• v.33 no.4
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• pp.117-122
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• 2005

Variable sampling rate(VSR) scheme varies the sampling rate for the current sample depending on the previous value of the control statistic. In this paper, we propose EWMA control charts with variable parameter(VP) scheme, which allows both the sample rate(the sample size or the sampling interval) and the weight to vary. We investigate the effectiveness of the VP scheme relative to the fixed parameter(FP) scheme and the VSR scheme in EWMA control charts. It is shown that using the VP scheme gives some improvements to the ability in detecting small and moderate shifts in the process normal mean.

• The Korean Journal of Applied Statistics
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• v.25 no.3
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• pp.465-470
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• 2012

EWMA(exponentially weighted moving average) charts and CUSUM(cumulative sum) charts are very effective to detect small shifts in the process mean. These charts have some control-chart parameters that allow the charts and be tuned and be more sensitive to certain shifts. The EWMA chart requires users to specify the value of a smoothing parameter, which can also be designed for the size of the mean shift. However, the size of the mean shift that occurs in applications is usually unknown and EWMA charts can perform poorly when the actual size of the mean shift is significantly different from the assumed size. In this paper, we propose the design procedure to find the optimal smoothing parameter of the EWMA chart when the size of the mean shift is unknown.

• The Korean Journal of Applied Statistics
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• v.35 no.6
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• pp.725-737
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• 2022

In the standard assumption of statistical process monitoring (SPM) under consideration, the in-control region of the control parameter of quality characteristic consists of a single point. However, if small deviations from the ideal situation may not be of practical importance, the parametric space can consist of three regions: In-control, indifference, and out-of-control. In this paper, we propose two exponentially weighted moving average (EWMA) charting procedures applicable to the situation with three parameter regions, and compare the efficiency of the proposed procedures with the Shewhart chart and the cumulative sum (CUSUM) chart.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.4
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• pp.114-120
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• 2008

The control chart is widely used statistical process control(SPC) tool that searches for assignable cause of variation and detects any change of process. Generally, ${\bar{X}}-R$ control chart and ${\bar{X}}-S$ are most frequently used. When the production run is short and process parameter changes frequently, it is difficult to monitor the process using traditional control charts. In such a case, the coefficient of variation (CV) is very useful for monitoring the process variability. The CV control chart is an effective tool to control the mean and variability of process simultaneously. The CV control chart, however, is not sensitive at small shift in the magnitude of CV. In this paper, we propose an CV-EWMA (exponentially weighted moving average) control chart which is effective in detecting a small shift of CV. Since the CV-EWMA control chart scheme can be viewed as a weighted average of all past and current CV values, it is very sensitive to small change of mean and variability of the process. We suggest the values of design parameters and show the results of the performance study of CV-EWMA control chart by the use of average run length (ARL). When we compared the performance of CV-EWMA control chart with that of the CV control chart, we found that the CV-EWMA control chart gives longer in-control ARL and much shorter out-of-control ARL.

• Journal of the Korean Operations Research and Management Science Society
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• v.20 no.3
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• pp.123-146
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• 1995

Several quality control schemes have been extensively compared using multivariate normal data sets simulated with various correlation structures. They include multiple univariate CUSUM charts, multivariate EWMA charts, multivariate CUSUM charts and Shewhart T$^{3}$ chart. This paper considers a new approach of the multivariate EWMA chart, in which the smoothing matrix has full elements instead of only diagonal elements. Performance of the schemes is measured by avaerage run length (ARL), coefficient of variation of run length (CVRL) and rank in order of signaling of off-target shifts in the process mean vector. The schemes are also compared by noncentrality parameter. The multiple univariate CUSUM charts are generally affected by the correlation structure. The multivariate EWMA charts provide better ARL performance. Especially, the new EWMA chart shows remarkable results in small shifts.

• Journal of the Korea Society for Simulation
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• v.16 no.4
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• pp.43-55
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• 2007

Moving average(MA) and exponentially weighted moving average(EWMA) are the two most popular control methods in manufacturing. Both methods are optimized under the assumption that the exact control equation is known. This paper focuses on the problems rising from estimation errors. Based on the accuracy of the estimated parameter and the range of the weight parameter $\lambda$, the limitations are identified and the performance of methods are evaluated. Optimal adjustment for process shift with misestimated control model and its application control methods to actual process is researched. The efficiency of proposed method is evaluated through simulation.

• Journal of the Korea Safety Management & Science
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• v.7 no.5
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• pp.255-273
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• 2005

Overlay parameter control of the semiconductor photolithography process is researched in this paper. Overlay parameters denote the error in superposing the current pattern to the pattern previously created. The reduction of the overlay deviation is one of the key factors in improving the quality of the semiconductor products. The semiconductor process is affected by numerous environment and equipment factors. Through process condition prediction and control, the overlay inaccuracy can be reduced. Generally, three types of process condition change exist; uncontrollable white noise, slowly changing drift, and abrupt condition shift. To effectively control the aforementioned process changes, control scheme using adaptive deadband is proposed. The suggested approach and existing control method are cross evaluated through simulation.

• Journal of Korea Society of Industrial Information Systems
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• v.6 no.3
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• pp.65-70
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• 2001

This Paper Propose the control chart Pattern to provide a more comprehensive scheme for detecting process shifts using individual observations in start-up process. In this paper, which uses the backpropagation algorithm two samples are fed into the trained neural network to provide outputs ranging from 0 to 1. The main advantage of using neural networks approach with a control chart is that the neural network has almost no delay in detecting small shift. This paper illustrates how neural networks can provide a useful method for optimizing parameter(connection weights) that affect process control. Simulation results show that the performance of the proposed control chart using the neural network (NNCC) is quite promising.