• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.1.2-6
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• 1994

A state or the arts in Japanese chemical process control is reviewed based on experience in applying advanced process control schemes to several industrial chemical processes. The applications validate model predictive control (MPC), the most popular advanced control scheme in the process control community, as, indeed, a powerful and practical control algorithm. However, at the same time, it is elucidated that MPC can solve only the control algorithm part of the problem and one needs chemical and systems engineering aspects to solve the entire problem. By illustrating several industrial process control problems, the need for chemical engineering aspects as well as the future direction for process control are addressed, especially in light or current attitudes toward product quality.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.9
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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.

• Journal of Korean Society for Quality Management
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• v.33 no.1
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• pp.22-31
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• 2005

With the common objective to improve process productivity and product quality, statistical process control (SPC) and engineering process control (EPC) have been widely used in the discrete-parts industry and the process industry, respectively. The major focus of SPC is on process monitoring, while that of EPC is on process adjustment. The emergence of the hybrid industry necessitates a synergistic combination of the two methods for an effective process control. This paper investigates the existing studies on SPC, EPC, and the integration of the two methods. This paper also presents future research issues in this field.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.836-843
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• 2003

The automatic train control (ATC) system development project for the Automated Guideway Transit (AGT) system has high technical risk because the system is unmanned train control system using wireless technology which was unprecedented in train control industry of Korea. To overcome the technical risk during concept design phase of the ATC system development project, the integrated product team(IPT) carried out a reverse and reengineering process using a systems engineering design model. The generic systems engineering process is incorporated in the both reverse and reengineering process. As a result of the systems engineering effort, the IPT has built top layer systems engineering design model of the ATC subsystem. The purpose of this paper is to deliver the reverse and reengineering process which was used to develop the systems engineering design model of ATC system using a computer aided systems engineering tool. This study also shows that the model based reverse and reengineering process can reduce the technical risk by identifying the differences of requirement, functional and physical architecture between a reference system and a target system.

• Communications for Statistical Applications and Methods
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• v.7 no.2
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• pp.435-446
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• 2000

Statistical process control(SPC) and engineering process control(EPC) are two strategies for quality improvement that have been developed independently. EPC seeks to minimize variability by adjusting compensatory variables in order to make the process level close to the target, while SPC seeks to reduce variability by monitoring and eliminating causes of variation. One purpose of this paper is to propose the IMA(0,1,1) model as the in-control process model. For the out-of-control process model we consider two cases; one is the case with a step shift in the level, and the other is the case with a change in the nonstationarity. Another purpose is to suggest the use of an integrated process control procedure with adjustment and monitoring, which can consider the proposed process model effectively. An integrated control procedure will improve the process control activity significantly for cases of the proposed model, when compared to the procedure of using either EPC or SPC, since EPC will keep the process close to the target and SPC will eliminate special causes.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.196-200
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• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used fur on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.2
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• pp.136-139
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• 2000

A neural controller for process control is proposed that combines a conventional multi-loop PID controller with a neural network. The concept of target signal based on feedback error is used for on-line learning of the neural network. This controller is applied to distillation column control to illustrate its effectiveness. The result shows that the proposed neural controller can cope well with disturbance, strong interactions, time delays without any prior knowledge of the process.

• JSTS:Journal of Semiconductor Technology and Science
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• v.11 no.1
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• pp.1-5
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• 2011

To enter next process control, numerous approaches, including run-to-run (R2R) process control and fault detection and classification (FDC) have been suggested in semiconductor manufacturing industry as a facilitation of advanced process control. This paper introduces a novel type of optical plasma process monitoring system, called plasma eyes chromatic system (PECSTM) and presents its potential for the purpose of fault detection. Qualitatively comparison of optically acquired signal levels vs. process parameter modifications are successfully demonstrated, and we expect that PECSTM signal can be a useful indication of onset of process change in real-time for advanced process control (APC).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.31 no.1
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• pp.92-100
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• 2008

This paper develops a process monitoring system for real-time process control. The practical case is studied on a small and medium marine equipment company. For business process reengineering of the company, we adopt an approach based on information engineering methodology, which consists of four stages : planning, analysis, design, and implementation. The system is developed for Client/Server environment. We discuss the constructing of hardware system for real-time process control at low cost. The developed system is composed of interrelated modules for item master and BOM management, process control, facility management, SQC and work report.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10b
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• pp.879-884
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• 1988

For more economical operation of chemical plants, optimal operating conditions are to be set and maintained as far as possible. For this purpose, optimizing control is applied to chemical plants. In this study, a process optimizer composed of a process simulator and an optimization routine using Successive Quadratic Programming as optimization technique is developed and the effect of optimizing control is tested on an example process, and a new process optimization strategy based on modified Jacobian matrix is developed.