• 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 information and communication convergence engineering
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• v.9 no.4
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• pp.379-384
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• 2011

The scope of the problems that could be solved by monitoring and the improvement of the recognition time is directly correlated to the performance of the management function of the business process. However, the current monitoring process of business activities decides whether to apply warnings or not by assuming a fixed environment and showing expressions based on the design rules. Also, warnings are applied by carrying out the measuring process when the event attribute values are inserted at every point. Therefore, there is a limit for distinguishing the range of occurrence and the level of severity in regard to the new external problems occurring in a complicated environment. Such problems cannot be ed. Also, since it is difficult to expand the range of problems which can be possibly evaluated, it is impossible to evaluate any unexpected situation which could occur in the execution period. In this paper, a process-evaluating model based on the goal scenario is suggested to provide constant services through the current monitoring process in regard to the service demands of the new scenario which occurs outside. The new demands based on the outside situation are analyzed according to the goal scenario for the process activities. Also, by using the meta-heuristic algorithm, a similar process model is found and identified by combining similarity and interrelationship. The process can be stopped in advance or adjusted to the wanted direction.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.16-21
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• 2020

How accurately reproducible energy is delivered to the wafer in the process of making thin films using PE-CVD (Plasma enhanced chemical vapor deposition) during the semiconductor process. This is the most important technique, and most of the reaction on the wafer surface is made by thermal energy. In this study, we studied the method of monitoring the change of thermal energy transferred to the wafer surface by monitoring the temperature change according to the change of the thin film formed on the showerhead facing the wafer. Through this research, we could confirm the monitoring of wafer thin-film which is changed due to abnormal operation and accumulation of equipment, and we can expect improvement of semiconductor quality and yield through process reproducibility and equipment status by real-time monitoring of problem of deposition process equipment performance.

• Korean Chemical Engineering Research
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• v.46 no.2
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• pp.233-247
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• 2008

Process monitoring technology is able to detect the faults and the process changes which occur in a process unpredictably, which makes it possible to find the reasons of the faults and get rid of them, resulting in a stable process operation, high-quality product. Statistical process monitoring method based on data set has a main merit to be a tool which can easily supervise a process with the statistics and can be used in the analysis of process data if a high quality of data is given. Because a real process has the inherent characteristics of nonlinearity, non-Gaussianity, multiple operation modes, sensor faults and process changes, however, the conventional multivariate statistical process monitoring method results in inefficient results, the degradation of the supervision performances, or often unreliable monitoring results. Because the conventional methods are not easy to properly supervise the process due to their disadvantages, several advanced monitoring methods are developed recently. This review introduces the theories and application results of several remarkable monitoring methods, which are a nonlinear monitoring with kernel principle component analysis (KPCA), an adaptive model for process change, a mixture model for multiple operation modes and a sensor fault detection and reconstruction, in order to tackle the weak points of the conventional methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.474-478
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• 1996

Process monitoring in laser welding is essential for automation and quality control of products. Various signals from laser welding, such as plasma, sound, optical signals, etc., are utilized for monitoring the process and detecting abnormal weld conditions. In this study, both W light from plasma formed above the weld pool and IR signal from the melting pool are detected with photodiodes and PC-based A/D board, and analyzed to give a guidance about the weld quality. Experimental results show the possibility of using the signals for predicting and evaluating the weld qualify and adapting into the system for on-line process monitoring.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.230-235
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• 1997

During laser welding, a laser induced matal vapour and plasuma is formed. The plasma shows strong fluctuation combined with acoustic sound emission. On-line monitoring of the process is possible by measuring and analysing the plasma and acoustic sound emission. This paper introduce the method of on line process monitoring in the laser beam welding and analysis being monitoring signal. The results show the complementary information on the process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.17 no.32
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• pp.145-154
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• 1994

In this paper, monitoring processor in a circuit switched network is considered. Monitoring processor monitors communication links, and offers a grade of service in each link to controller. Such an information is useful for an effective maintenance of system. Two links with nonsymmetric system Parameters are considered. each link is assumed independent M/M/1/1 type. The Markov process is introduced to compute busy and idle portions of monitoring processor and monitored rate of each link. Inter-idle times and inter-monitoring times of monitoring processor between two links are respectively computed. A recursive formula is introduced to make computational procedure rigorous.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.431-438
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• 2024

Monitoring unsafe activities on construction sites is challenging due to a variety of factors including the diversity of tasks and workers involved, the potential of human error and lack of real-time hazard detection. With technological advancements, several digital technologies have been proposed and applied to improve the monitoring process. Despite the potential of these technological advancements to reduce manual effort in traditional monitoring, the challenge lies in selecting and implementing the technology that best meets the specific needs of contractors. This paper aims to streamline the research of digital technologies in the construction domain by achieving three key objectives: (1) classify the types of unsafe activities that can be monitored automatically, (2) determine the specific data required for effective monitoring processes, and (3) identify the technologies that can facilitate such data collection process. We conduct a systematic literature review on cutting-edge technological studies to achieve the research aims. The findings of this research serve as a valuable resource for construction practitioners, offering insights into both the benefits and limitations of digital technologies in enhancing the monitoring process. Moreover, the study recommends preparatory elements that practitioners should undertake to integrate these technologies effectively into their monitoring frameworks. The study empowers practitioners by providing a deep understanding, enabling them to create a comprehensive safety management program aligned with the digital transformation process.

• Transactions of Materials Processing
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• v.31 no.1
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• pp.5-10
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• 2022

In multi-stage cold forging process, to enhance the productivity and product quality, in-site process monitoring technique by implanting sensors such as piezo-sensor and acoustic emission sensor has been continuously studied. For accurate analysis of the process, the selection of appropriate sensors and implantation positions are very important. Until now, in a multi-state forging machine, wedge parts located at the end of punch-set are used but it is difficult to analyze minute changes in die block-set. In this study, we also implanted sensors to the die part (die spacer) and compared signals from both sensors and found that sensing signals from die part showed enhanced process monitoring results.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.12
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• pp.1048-1054
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• 2003

In laser surface hardening processes, the geometrical parameters such as the depth and the width of a hardened layer can be utilized to assess the hardened layer quality. However, accurate monitoring of the geometrical parameters for on-line process control as well as for on-line quality evaluation is very difficult because the hardened layer is formed beneath a material surface and is not visible. Therefore, temperature monitoring of a point of specimen surface has most frequently been used as a process monitoring method. But, a hardened layer depends on the temperature distribution and the thermal history of a specimen during laser surface hardening processing. So, this paper describes the estimation results of the geometric parameters using multi-point surface temperature monitoring. A series of hardening experiments were performed to find the relationships between the geometric parameters and the measured temperature. Estimation results using a neural network show the enhanced effectiveness of multi-point surface temperature monitoring compared to one-point monitoring.