• 제어로봇시스템학회논문지
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• 제21권9호
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• pp.875-880
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• 2015

Traditional semiconductor process control has been performed through statistical process control techniques in a constant process-recipe conditions. However, the complexity of the interior of the etching apparatus plasma physics, quantitative modeling of process conditions due to the many difficult features constraints apply simple SISO control scheme. The introduction of the Advanced Process Control (APC) as a way to overcome the limits has been using the APC process control methodology run-to-run, wafer-to-wafer, or the yield of the semiconductor manufacturing process to the real-time process control, performance, it is possible to improve production. In addition, it is possible to establish a hierarchical structure of the process control made by the process control unit and associated algorithms and etching apparatus, the process unit, the overall process. In this study, the research focused on the methodology and monitoring improvements in performance needed to consider the process management of future developments in the semiconductor manufacturing process in accordance with the age of the APC analysis in real applications of the semiconductor manufacturing process and process fault diagnosis and control techniques in progress.

• 품질경영학회지
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• 제39권2호
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• pp.234-243
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• 2011

Multivariate control charts are widely used to monitor the performance of a multivariate process over time to maintain control of the process. Although existing multivariate control charts provide control limits to monitor the process and detect any extraordinary events, it is a challenge to identify the causes of an out-of-control alarm when the number of process variables is large. Several fault identification methods have been developed to address this issue. However, these methods require a normality assumption of the process data. In the present study, we propose a bootstrapped-based $T^2$ decomposition technique that does not require any distributional assumption. A simulation study was conducted to examine the properties of the proposed fault identification method under various scenarios and compare it with the existing parametric $T^2$ decomposition method. The simulation results showed that the proposed method produced better results than the existing one, especially in nonnormal situations.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2012년도 제42회 동계 정기 학술대회 초록집
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• pp.241-241
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• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• 한국가스학회지
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• 제4권4호
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• pp.42-49
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• 2000

대부분의 화학공정은 매우 크고 복잡하기 때문에 전체 공정에 대한 진단시스템을 만드는 것은 매우 어렵다. 따라서, 대형공정을 몇 개의 부공정으로 분할하여 진단하는 체계적인 방법이 필요하다. 이 논문에서는 이상-결과 트리모델에 기반하여 정성적 이상진단을 위한 공정분할전략을 제안하였다. 분할기준으로 유연한 진단, 지식베이스의 크기축소, 및 복잡한 지식베이스의 일관된 구축을 사용하였다 부공정간의 인과관계를 연결하기 위해 통로변수를 도입한 다음 오프라인 분석을 통해 통로변수의 이상-결과 트리모델을 구축하였다 계분할이 없는 경우와 같은 진단결과를 얻을 수 있도록 온라인 진단전략을 수립하였다 제안된 방법의 유용성을 대형 보일러 공정에 대한 이상진단시스템을 통해 보였다.

• Korean Chemical Engineering Research
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• 제60권2호
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• pp.223-228
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• 2022

화학공정에서 의도되지 않게 발생하는 이상은 큰 사고를 유발할 수 있다. 이러한 문제를 해결하기 위해, 신속하게 이상의 원인을 감지하고 판별하는 이상 진단 모델이 필요하다. 하지만, 이상 진단을 연구하는 대부분 연구의 경우, 상용프로그램에서 공정 시뮬레이션을 이용하여 이상 데이터를 생성하고 이를 이용하여 연구한 방법론을 적용하고 있다. 이는 실제 공정상에서 이상을 포함하는 실제 데이터를 얻는 데 많은 제약이 있음을 의미한다. 본 연구에서는 실제 폴리스티렌 반응기에서 얻은 이상 데이터와 정상 데이터를 분석하여 적절한 이상 진단 모델을 설계하고자 하였다. 먼저, 정상 데이터를 분석하여 세 가지의 조업 모드가 존재함을 확인하였으며, 모드 판별을 위한 모델을 SVM (Support Vector Machine)을 이용하여 만들었다. 각 조업 모드 별로 PCA (Principal Component Analysis)를 이용하여 이상 진단 모델을 만들었으며, 실제 이상 데이터를 이용하여 계산한 결과 신속하게 이상을 진단할 수 있음을 확인하였다. 본 연구에서 제안한 모델을 통해, 실제 사고가 발생하는 경우 신속한 대처가 가능하며, 이는 잠재적인 손실의 감소에 기여할 수 있음을 의미한다.

• Journal of Mechanical Science and Technology
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• 제14권7호
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• pp.773-781
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• 2000

The focus of this study is the development of a credible fault detection system of the cylindrical plunge grinding process. The acoustic emission (AE) signals generated during machining were analyzed to determine the relationship between grinding-related faults and characteristics of changes in signals. Furthermore, a neural network, which has excellent ability in pattern classification, was applied to the diagnosis system. The neural network was optimized with a momentum coefficient, a learning rate, and a structure of the hidden layer in the iterative learning process. The success rates of fault detection were verified.

• 반도체디스플레이기술학회지
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• 제9권4호
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• pp.93-97
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• 2010

Fast incipient fault diagnosis is becoming one of the key requirements for economical and optimal process operation management in high-tech industries. Artificial neural networks have been used to detect faults for a number of years and shown to be highly successful in this application area. This paper presents a novel test technique for fault detection and classification for module process of TFT-LCD manufacture using support vector machines (SVMs). In order to evaluate SVMs, this paper examines the performance of the proposed method by comparing it with that of multilayer perception, one of the artificial neural network techniques, based on real benchmarking data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1993년도 하계학술대회 논문집 A
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• pp.430-432
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• 1993

In this paper, an algorithm for the fault diagnosis using simple FCM(Fuzzy Cognitive Map) is proposed FCMs which store uncertain causal knowledges are fuzzy signed graphs with feedback. The algorithm allows searching the origin of fault and the ways of propagating the abnormality throughout the process simply and has following characteristics. First, it can distinguish the cause of soft failure which can degenerate the process as well as hard failure. Second, it is proper for the processes which have difficulties to establish the exact quantative model. Finally, it has short amputation time in comparison with the fault tree or the other AI methods. The applicability of the proposed algorithm for the fault diagonosis to a tank or pipeline system is demonstrated

• Journal of Magnetics
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• 제21권3호
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• pp.413-420
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• 2016

The permanent magnet synchronous motor has high efficiency driving performance and high power density output characteristics compared with other motors. In addition, it has good regenerative operation characteristics during braking and deceleration driving condition. For this reason, permanent magnet synchronous motor is generally applied as a power train motor for electrical vehicle. In permanent magnet synchronous motor, the most probable causes of fault are demagnetization of rotor's permanent magnet and short of stator winding turn. Therefore, the demagnetization fault of permanent magnet and turn fault of stator winding should be detected quickly to reduce the risk of accident and to prevent the progress of breakdown of power train system. In this paper, the fault diagnosis method using high frequency low voltage injection was suggested to diagnose the demagnetization fault of rotor permanent magnet and the turn fault of stator winding. The proposed fault diagnosis method can be used to check the faults of permanent magnet synchronous motor during system check-up process at vehicle starting and idling stop mode. The feasibility and usefulness of the proposed method were verified by the finite element analysis.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.442-445
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• 1997

A challenging activity in the manufacturing industry is to perform in real time the continuous monitoring of the process state, the situation assessment and identification of the problem on line and diagnosis of the cause and importance of the problem if he process does not work properly. This paper describes LAT(Ladder Analysis Tool) system for fault tree generation to improving the fault diagnosis of CNC machine tools. The system consists of 4 steps which can automatically ladder analysis from ladder diagram to two diagnosis function models. The two diagnostic models based on he ladder diagram is switching function model and step switching function model. This system tries to overcome diagnosis deficiencies present machine tool.