• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.92-96
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• 2004

In this paper, polishing method using bonded magnetic abrasive particle has been applied to the micro mold polishing. Through process control using the Run-to-Run control, it tried to form the surface roughness In order to grasp the influence of the surface roughness which is reached by selection of control factor and the factor, a design of experiment was been processed. The study is processed with a purpose of to embody and to maintain the surface roughness of nano scale by the basis of an influence between a control factor and the factors which has been selected in this way. As a result, the result of the process control converged at a target value of surface roughness Ra 10nm and Rmax 50nm

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.519-524
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• 2005

A numerical process to simulate SiO2 dry etching with inductively coupled C2F6 plasmas has been constructed using a commercial CFD code as a first step to design a run-to-run control system. The simulator was tuned to reasonably predict the reactive ion etching behavior and used to investigate the effects of plasma operating variables on the etch rate and uniformity. The relationship between the operating variables and the etching characteristics was mathematically modeled through linear regression for future run-to-run control system design.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.22-28
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• 2009

In order to optimize the polishing process, Run to Run control scheme has been applied to the micro mold polishing in this study. Also, to fully understand the effect of parameters on the surface roughness a design of experiment is performed. By linear approximation of main factors such as gap and rotational speed of micro quill, EWMA (Exponential Weighted Moving Average) gradual mode controller is adopted as a optimizing tool. Consequently, the process converged quickly at a target value of surface roughness Ra 10nm and Rmax 50nm, and was hardly affected by unwanted process noises like initial surface quality and wear of magnetic abrasives.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.1
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• pp.104-111
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• 2000

A fuzzy logic Run-by-Run(RbR) controller and an in -line wafer characteristics prediction scheme for the rapid thermal processing system have been developed for the study of process repeatability. The fuzzy logic RbR controller provides a framework for controlling a process which is subject to disturbances such as shifts and drifts as a normal part of its operation. The fuzzy logic RbR controller combines the advantages of both fuzzy logic and feedback control. It has two components : fuzzy logic diagnostic system and model modification system. At first, a neural network model is constructed with the I/O data collected during the designed experiments. The wafer state after each run is assessed by the fuzzy logic diagnostic system with featuring step. The model modification system updates the existing neural network process model in case of process shift or drift, and then select a new recipe based on the updated model using genetic algorithm. After this procedure, wafer characteristics are predicted from the in-line wafer characteristics prediction model with principal component analysis. The fuzzy logic RbR controller has been applied to the control of Titanium SALICIDE process. After completing all of the above, it follows that: 1) the fuzzy logic RbR controller can compensate the process draft, and 2) the in-line wafer characteristics prediction scheme can reduce the measurement cost and time.

• Journal of Korean Society for Quality Management
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• v.38 no.2
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• pp.190-201
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• 2010

This research proposes a method for designing VSI (Variable Sampling Interval) control charts with supplementary run rules. The basic idea is to apply various run rules and the VSI scheme to a control chart in order to increase the sensitivity. The sampling process of the VSI run rules chart is constructed by Markov chain approach. A procedure for designing the VSI run rules chart is proposed based on Lorenzen and Vance's model. Sensitivity study shows that the VSI run rules charts outperform the FSI (Fixed Sampling Interval) run rules charts for wide range of process mean shifts. A major advantage of the VSI run rules chart over other charts such as CUSUM, EWMA, and adaptive charts is it's simplicity in implementation. Some useful guidelines are proposed based on the sensitivity study.

• Journal of the Korea Safety Management & Science
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• v.12 no.4
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• pp.255-263
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• 2010

The research developes short-run standardized control charts(SSCC) and short-run acceptance control charts(SACC) under the various demand patterns. The demand patterns considered in this paper are three types such as high-variety and repetitive low-volume pattern, extremely-high-variety and nonrepetitive low-volume pattern, and high-variety and extremely-low-volume pattern. The short-run standardized control charts developed by extending the long-run ${\bar{x}}$-R, ${\bar{x}}$-s and I-MR charts have strengths for practioners to understand and use easily. Moreover, the short-range acceptance control charts developed in the study can be efficiently used through combining the functions of the inspection and control chart. The weighting schemes such as Shewhart, moving average (MA) and exponentially weighted moving average (EWMA) can be considered by the reliability of data sets. The two types according to the use of control chart are presented in the short-range standardized charts and acceptance control charts. Finally, process capability index(PCI) and process performance index(PPI) classified by the demand patterns are presented.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.10a
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• pp.85-85
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• 2003

• Korean Management Science Review
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• v.31 no.3
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• pp.27-39
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• 2014

We derive a generalized statistic form of Q control chart, which is especially suitable for short run productions and start-up processes, for the detection of process mean shifts. The generalization means that the derived control chart statistic concurrently uses within lot variability and between lot variability to explain the process variability. The latter variability source is noticeably prevalent in lot type production processes including semiconductor wafer fabrications. We first obtain the generalized Q control chart statistic when both the process mean and process variance are unknown, which represents the case of implementing statistical process control charting for short run productions and start-up processes. Also, we provide the corresponding generalized Q control chart statistics for the rest of three cases of previous Q control chart statistics : (1) both the process mean and process variance are known (2) only the process mean is unknown and (3) only the process variance is unknown.

• Industrial Engineering and Management Systems
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• v.9 no.3
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• pp.262-274
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• 2010

This article investigates how to adaptively predict the time-varying metrology delay that could realistically occur in the semiconductor manufacturing practice. Metrology delays pose a great challenge for the existing run-to-run (R2R) controllers, driving the process output significantly away from target if not adequately predicted. First, the expected asymptotic double exponentially weighted moving average (DEWMA) control output, by using the EWMA and recursive least squares (RLS) prediction methods, is derived. It has been found that the relationships between the expected control output and target in both estimation methods are parallel, and six cases are addressed. Within the context of time-varying metrology delay, this paper presents a modified recursive least squares-linear trend (RLS-LT) controller, in combination with runs test. Simulated single input-single output (SISO) R2R processes subject to various time-varying metrology delay scenarios are used as a testbed to evaluate the proposed algorithms. The simulation results indicate that the modified RLS-LT controller can yield the process output more accurately on target with smaller mean squared error (MSE) than the original RLSLT controller that only deals with constant metrology delays.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1487-1498
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• 2016

In monitoring a process, in-control process parameters must be estimated from the Phase I data. When we design the control chart based on the estimated process parameters, the control limits are usually chosen to satisfy a specific in-control average run length (ARL). However, as the run length distribution is skewed when the process is either in-control or out-of-control, the median run length (MRL) can be used as alternative measure instead of the ARL. In this paper, we evaluate the performance of Shewhart $\bar{X}$ chart with estimated parameters in terms of the average of median run length (AMRL) and the standard deviation of MRL (SDMRL) metrics. In simualtion study, the grand sample mean is used as a process mean estimator, and several competing process standard deviation estimators are used to evaluate the in-control performance for various amounts of Phase I data.