• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.4
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• pp.203-212
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• 2018

Quality requirements of manufactured products or parts are given in the form of specification limits on the quality characteristics of individual units. If a product is to meet the customer's fitness for use criteria, it should be produced by a process which is stable or repeatable. In other words, it must be capable of operating with little variability around the target value or nominal value of the product's quality characteristic. In order to maintain and improve product quality, we need to apply statistical process control techniques such as histogram, check sheet, Pareto chart, cause and effect diagram, or control charts. Among those techniques, the most important one is control charting. The cumulative sum (CUSUM) control charts have been used in statistical process control (SPC) in industries for monitoring process shifts and supporting online measurement. The objective of this research is to apply Taguchi's quality loss function concept to cost based CUSUM control chart design. In this study, a modified quality loss function was developed to reflect quality loss situation where general quadratic loss curve is not appropriate. This research also provided a methodology for the design of CUSUM charts using Taguchi quality loss function concept based on the minimum cost per hour criterion. The new model differs from previous models in that the model assumes that quality loss is incurred even in the incontrol period. This model was compared with other cost based CUSUM models by Wu and Goel, According to numerical sensitivity analysis, the proposed model results in longer average run length in in-control period compared to the other two models.

• Journal of Korean Society for Quality Management
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• v.23 no.3
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• pp.33-44
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• 1995

This paper presents an automated visual inspection system for the electronic parts manufacturing process. In this system, a statistical process control (SPC) method is integrated into the automated inspection method on a real time base. It shows how the collected data can be analyzed with the SPC to provide process information. Also presented are studies of subpixel image processing technology to improve the accuracy of parts measurements, and the cumulative-sum (CUSUM) control chart for fraction defectives. An application of the developed system to connector manufacturing process as a part of computer integrated manufacturing (CIM) is presented.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2010.06a
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• pp.369-369
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• 2010

소자제조 공정의 질과 생산성을 향상시키기 위하여 플라즈마 감시가 필요하다. 본 연구에서는 광반사분광기(Optical Emission Spectroscopy)를 이용하여 소스전력의 변화에 따른 플라즈마 상태 데이터를 수집하였다. 수집된 데이터를 이용한 시계열 신경망 감시 모델을 개발하였으며, 개발된 모델과 CUSUM(Cumulative Sum Control Chart)를 결합하여 플라즈마의 이상 상태를 실시간으로 감시하는 기법을 개발하였다. 매우 우수한 감시 성능을 확인할 수 있었다.

• The Korean Journal of Applied Statistics
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• v.22 no.6
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• pp.1191-1201
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• 2009

The cumulative sum(CUSUM) control charts are typically used for detecting small level shifts in process control. To control an auto-correlated process, the model-based control methods can be employed, in which the residuals from fitting a time series model are applied to the CUSUM chart. However, the persistent level shifts in the original process may lead to varying mean shifts in residuals, which may deteriorate detection performance significantly. Therefore, in this paper, focussing on ARMA(1,1), we propose a new CUSUM type control method which can detect the dynamic mean shifts in residuals especially with oscillating pattern effectively and, through the simulation study, evaluate its performance by comparing with other various CUSUM type control methods introduced so far.

• Journal of Radiation Protection and Research
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• v.44 no.2
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• pp.64-71
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• 2019

Background: It is very difficult to distinguish between a radioactive contamination source and background radiation from natural radionuclides in the marine environment by means of online monitoring system. The objective of this study was to investigate a statistical process for triggering abnormal level of count rate data measured from our on-line seawater radioactivity monitoring. Materials and Methods: Count rate data sets in time series were collected from 9 monitoring posts. All of the count rate data were measured every 15 minutes from the region of interest (ROI) for $^{137}Cs$ ($E_{\gamma}=661.6keV$) on the gamma-ray energy spectrum. The Shewhart ($3{\sigma}$), CUSUM, and Bayesian S-R control chart methods were evaluated and the comparative analysis of determination methods for count rate data was carried out in terms of the false positive incidence rate. All statistical algorithms were developed using R Programming by the authors. Results and Discussion: The $3{\sigma}$, CUSUM, and S-R analyses resulted in the average false positive incidence rate of $0.164{\pm}0.047%$, $0.064{\pm}0.0367%$, and $0.030{\pm}0.018%$, respectively. The S-R method has a lower value than that of the $3{\sigma}$ and CUSUM method, because the Bayesian S-R method use the information to evaluate a posterior distribution, even though the CUSUM control chart accumulate information from recent data points. As the result of comparison between net count rate and gross count rate measured in time series all the year at a monitoring post using the $3{\sigma}$ control charts, the two methods resulted in the false positive incidence rate of 0.142% and 0.219%, respectively. Conclusion: Bayesian S-R and CUSUM control charts are better suited for on-line seawater radioactivity monitoring with an count rate data in time series than $3{\sigma}$ control chart. However, it requires a continuous increasing trend to differentiate between a false positive and actual radioactive contamination. For the determination of count rate, the net count method is better than the gross count method because of relatively a small variation in the data points.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.436-437
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• 2012

Double patterning technology (DPT) has been suggested as a promising candidates of the next generation lithography technology in FLASH and DRAM manufacturing in sub-40nm technology node. DPT enables to overcome the physical limitation of optical lithography, and it is expected to be continued as long as e-beam lithography takes place in manufacturing. Several different processes for DPT are currently available in practice, and they are litho-litho-etch (LLE), litho-etch-litho-etch (LELE), litho-freeze-litho-etch (LFLE), and self-aligned double patterning (SADP) [1]. The self-aligned approach is regarded as more suitable for mass production, but it requires precise control of sidewall space etch profile for the exact definition of hard mask layer. In this paper, we propose etch end point detection (EPD) in spacer etching to precisely control sidewall profile in SADP. Conventional etch EPD notify the end point after or on-set of a layer being etched is removed, but the EPD in spacer etch should land-off exactly after surface removal while the spacer is still remained. Precise control of real-time in-situ EPD may help to control the size of spacer to realize desired pattern geometry. To demonstrate the capability of spacer-etch EPD, we fabricated metal line structure on silicon dioxide layer and spacer deposition layer with silicon nitride. While blanket etch of the spacer layer takes place in inductively coupled plasma-reactive ion etching (ICP-RIE), in-situ monitoring of plasma chemistry is performed using optical emission spectroscopy (OES), and the acquired data is stored in a local computer. Through offline analysis of the acquired OES data with respect to etch gas and by-product chemistry, a representative EPD time traces signal is derived. We found that the SE-EPD is useful for precise control of spacer etching in DPT, and we are continuously developing real-time SE-EPD methodology employing cumulative sum (CUSUM) control chart [2].

• Journal of the Korea Society for Simulation
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• v.18 no.3
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• pp.103-112
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• 2009

In a nuclear power plant (NPP), periodic sensor calibrations are required to assure sensors are operating correctly. However, only a few faulty sensors are found to be calibrated. For the safe operation of an NPP and the reduction of unnecessary calibration, on-line calibration monitoring is needed. In this paper, principal component-based Auto-Associative support vector regression (PCSVR) was proposed for the sensor signal validation of the NPP. It utilizes the attractive merits of principal component analysis (PCA) for extracting predominant feature vectors and AASVR because it easily represents complicated processes that are difficult to model with analytical and mechanistic models. With the use of real plant startup data from the Kori Nuclear Power Plant Unit 3, SVR hyperparameters were optimized by the response surface methodology (RSM). Moreover the statistical techniques are integrated with PCSVR for the failure detection. The residuals between the estimated signals and the measured signals are tested by the Shewhart Control Chart, Exponentially Weighted Moving Average (EWMA), Cumulative Sum (CUSUM) and generalized likelihood ratio test (GLRT) to detect whether the sensors are failed or not. This study shows the GLRT can be a candidate for the detection of sensor drift.