• 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 of Industrial and Systems Engineering
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• v.25 no.2
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• pp.50-56
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• 2002

In this study, we establish bootstrap control limits for EWMA chart by applying the bootstrap method, called resampling, which could not demand assumptions about pre-distribution when the process is skewed and/or the normality assumption is doubt. The results obtained in this study are summarized as follows : bootstrap EWMA control chart is developed for applying bootstrap method to EWMA chart, which is more sensitive to small shifts of process. With the purpose of eliminating a skewness of the resampling distribution, the bootstrap control limits are established by using a modified residual, and its performance is analyzed by ARL. It is shown that the bootstrap EWMA control chart developed in this study includes the properties of standard EWMA control chart that is sensitive to a small shift, and detects process in out of control more quickly than standard EWMA chart.

• Proceedings of the Safety Management and Science Conference
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• 2012.11a
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• pp.445-451
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• 2012

제조공정에서 사용되어지는 SPC(Statistical Process Control)관리 기법은 가피원인을 탐지하여 변동을 감소시키는 통계적 공정관리 시스템이다. SPC의 대표적인 관리기법으로는 Shewhart관리도, Cusum관리도, EWMA관리도가 있으며 이러한 관리 기법들은 공정을 보다 안정적으로 관리 할 수 있도록 유지 및 예측하는데 사용 되어 진다. 본 논문에서는 일반적으로 사용되어 지는 Shewhart관리도와 공정 예측에 유리한 EWMA 관리도에 대해 연구해보고 공정변화에 민감하게 반응하는 EWMA 관리도의 적용 사례를 제시하고자 한다.

• IE interfaces
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• v.21 no.2
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• pp.141-150
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• 2008

In this paper, an efficient control method for semiconductor fabrication process is presented. Generally, control is performed with data which is under the influence of process disturbance. EWMA is one of the most popular control methods in semiconductor fabrication that effectively deals with varying process condition. A new method using EWMA, called the Dual Damping EWMA, is presented in this study to reduce over-control by separating weight factor of input and output. The goal is to reflect Drift but reduce the effects of White noise in run to run control. Simulation is performed to evaluate the performance of DPEWMA and to compare with EWMA and Double EWMA.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.33 no.3
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• pp.146-153
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• 2010

When the production run is short and process parameters change 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 shifts in the magnitude of CV. The CV-EWMA(exponentially weighted moving average) control chart which was developed recently is effective in detecting a small shifts 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. In this paper, we propose an FIR(Fast initial response) CV-EWMA control chart to improve the sensitivity of a CV-EWMA scheme at process start-up or out-of-control process. Moreover, we suggest the values of design parameters and show the results of the performance study of FIR CV-EWMA control chart by the use of average run length(ARL). Also, we compared the performance of FIR CV-EWMA control chart with that of the CV-EWMA control chart and we found that the CV-EWMA control chart gives longer in-control ARL and much shorter out-of-control ARL.

• Journal of Integrative Natural Science
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• v.15 no.1
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• pp.1-8
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• 2022

In a multivariate normal production process N_p(µ,Σ), a chart combining three EWMA charts with accumulate-combine method for µ, variance components of Σ, and off-diagonal elements of Σ, into a EWMA (exponentially weighted moving average) chart is considered, which is called a combined EWMA chart. Through simulation work, the proposed combined EWMA chart's numerical performance and properties are examined. The simulation results show that the proposed combined EWMA chart, which is simultaneously monitoring all the process parameters of multivariate normal production process, works effectively in the perspective of means, variances and correlation coefficients. In addition, the combined EWMA chart is extended to VSI chart.

• Journal of the Korean Data and Information Science Society
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• v.4
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• pp.31-44
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• 1993

Multivariate exponentially weighted moving average (EWMA) control charts for monitoring the variance-covariance matrix are investigated. A variable sampling interval (VSI) feature is considered in these charts. Multivariate EWMA control charts for monitoring the variance-covariance matrix are compared on the basis of their average time to signal (ATS) performances. The numerical results show that multivariate VSI EWMA control charts are more efficient than corrsponding multivariate fixed sampling interval (FSI) EWMA control charts.

• Communications for Statistical Applications and Methods
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• v.4 no.3
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• pp.715-723
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• 1997

Multivariate control statistics to simultaneously monitor both means and variances for several quality variables under multivariate normal process are proposed. Performances of the proposed multivariate charts are evaluated in terms of average run length(ARL). Multivariate Shewhart chart is also proposed to compare the performances of multivariate exponentially weighted moving average(EWMA) charts. A numerical comparison shows that multivariate EWMA charts are more efficient than multivariate Shewhart chart for small and moderate shifts and multivariate EWMA scheme based on accumulate-combine approach is more efficient than corresponding multivariate EWMA chart based on combine-accumulate approach.

• Journal of Korean Institute of Industrial Engineers
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• v.28 no.2
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• pp.171-177
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• 2002

In this paper, we propose a new data fusion method to improve the performance of individual prediction models for time series data. Individual models used are ARIMA and neural network and their results are combined based on the weight reflecting the inverse of EWMA of squared prediction error of each individual model. Monte Carlo simulation is used to identify the situation where the proposed approach can take a vintage point over typical fusion methods which utilize MSE for weight. Study results indicate the following: EWMA performs better than MSE fusion when the data size is large with a relatively big amplitude, which is often observed in intra-cranial pressure data. Additionally, EWMA turns out to be a best choice among MSE fusion and the two individual prediction models when the data size is large with relatively small random noises, often appearing in tax revenue data.

• 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.