• Electrical & Electronic Materials
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• v.20 no.2
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• pp.3-11
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• 2007

• Proceedings of the Korean Society of Laser Processing Conference
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• 2002.11a
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• pp.65-67
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• 2002

• Journal of the Semiconductor & Display Technology
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• v.10 no.2
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• pp.1-5
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• 2011

In recent years, worldwide production of solar wafers increased rapidly. Therefore, the solar wafer technology in the developed countries already has become an industry, and related industries such as solar wafer manufacturing equipment have developed rapidly. In this paper we propose the color classification method of the polycrystalline solar wafer that needed in manufacturing equipment. The solar wafer produced in the manufacturing process does not have a uniform color. Therefore, the solar wafer panels made with insensitive color uniformity will fall off the aesthetics. Gaussian mixture models (GMM) are among the most statistically mature methods for clustering and we use the Gaussian mixture models for the classification of the polycrystalline solar wafers. In addition, we compare the performance of the color feature vector from various color space for color classification. Experimental results show that the feature vector from YCbCr color space has the most efficient performance and the correct classification rate is 97.4%.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.30A no.11
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• pp.99-104
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• 1993

We analyzed the D.I. water used in wet cleaning process of semiconductor device manufacturing both at the D.I. water plant and at the wafer cleaning bath to detect the impurity source of D.I. water contamination. This shows that the quantity of impurity is related to the resistivity of D.I. water, and we found that the cleanliness of the wafer surface processed in D.I. water bath was affected by the degree of the ionic impurity contamination. So we evaluated the cleaning effect as different method for Fe ion, having the best adsoptivity on wafer surface. Moreover the temperature effect of the D.I. water is investigated in case of anion in order to remove the chemical residue after wet process. In addition to the control of D.I. water resistivity, chemical analysis of impurity control in D.I. water should be included and a suitable cleaning an drinsing method needs to be investigated for a high yielding semiconductor device.

• IE interfaces
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• v.21 no.2
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• pp.229-236
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• 2008

Recently, ubiquitous computing paradigm considers as a tool for making innovation and competitive strength in manufacturing industry like other industries. Particularly, the location-based service that enables us to trace real-time logistics make effective management of schedules for inventory control, facilities and equipments, jobs planning, and facilitate the processes of information management and intelligence, which relate with ERP and SCM in organizations. Our study tries to build the location-based system for products of semiconductors in manufacturing place and suggests the good conditions and effective tracking procedures for positions of products. Our study show that the system is good for the saving of time in tracking products, however, it has to be improved in terms of accuracy. The study verifies the application of RFID technology in manufacturing industry and suggests the improvement of photograph process through RFID. In addition, our research introduces the future operation of FAB in semiconductors' processes that relate with real-time automation and RFID in manufacturing company.

• Clean Technology
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• v.2 no.2
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• pp.201-213
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• 1996

Environmental regulation has traditionally focused on specific phenomena and adopted the so-called end-of-pipe approach. Recently, however, the new environmental paradigm is more concerned with minimization of waste generation, efficient material and energy use, pollution prevention, etc. The basis of above concept is that one must consider the environmental impacts of a product not only during its manufacturing stage, but during all life stages. In the present study, the current status of LCA and its importance to environmental impacts have been reviewed. In the usual approach to LCA, screening LCA method has been used to promote international competition and define environmental concerns during semiconductor manufacturing. In the present study, a review of semiconductor manufacturing process and its environmental implication has been conducted to quantify the material and energy requirements, minimize the waste generation, and evaluate production cost. Recommended activities are also specified for process modification to improve the process efficiency.

• Journal of the Semiconductor & Display Technology
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• v.12 no.4
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• pp.39-44
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• 2013

In the semiconductor heat-treatment process, the temperature uniformity determines the film quality of a wafer. This film quality effects on the overall yield rate. The heat transfer of the wafer surface in the heat-treatment process equipment is occurred by convection and radiation complexly. Because of this, there is the nonlinearity between the wafer temperature and reactor. Therefore, the accurate prediction of temperature on the wafer surface is difficult without the direct measurement. The thermal camera and the T/C wafer are general ways to confirm the temperature uniformity on the heat-treatment process. As above ways have limit to measure the temperature in the precise domain under the micro-scale. In this study, we developed the thin film type temperature sensor using the MEMS technology to establish the system which can measure the temperature under the micro-scale. We combined the experiment and numerical analysis to verify and calibrate the system. Finally, we measured the temperature on the wafer surface on the semiconductor process using the developed system, and confirmed the temperature variation by comparison with the commercial T/C wafer.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.1
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• pp.113-122
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• 1996

As integrity of semiconductor device is increased, accurate and efficient modeling and recipe generation of semiconductor fabrication procsses are necessary. Among the major semiconductor manufacturing processes, dry etc- hing process using gas plasma and accelerated ion is widely used. The process involves a variety of the chemical and physical effects of gas and accelerated ions. Despite the increased popularity, the complex internal characteristics made efficient modeling difficult. Because of difficulty to determine the control input for the desired output, the recipe generation depends largely on experiences of the experts with several trial and error presently. In this paper, the optimal control of the etching is carried out in the following two phases. First, the optimal neural network models for etching process are developed with genetic algorithm utilizing the input and output data obtained by experiments. In the second phase, search for optimal control inputs in performed by means of using the optimal neural network developed together with genetic algorithm. The results of study indicate that the predictive capabilities of the neural network models are superior to that of the statistical models which have been widely utilized in the semiconductor factory lines. Search for optimal control inputs using genetic algorithm is proved to be efficient by experiments. (author). refs., figs., tabs.

• IE interfaces
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• v.20 no.3
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• pp.288-297
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• 2007

Success of semiconductor/LCD industry depends on its yield and quality of product. For the purpose, FDC (Fault Detection and Classification) system is used to diagnose fault state in main manufacturing processes by monitoring time series data collected by equipment sensors which represent various conditions of the equipment. The data set is segmented at the start and end of each product lot processing by a trigger event module. However, in practice, segmented sensor data usually have the features of data asynchronization such as different start points, end points, and data lengths. Due to the asynchronization problem, false alarm (type I error) and missed alarm (type II error) occur frequently. In this paper, we propose a robust process fault detection system by integrating a process event detection method and a similarity measuring method based on dynamic time warping algorithm. An experiment shows that the proposed system is able to recognize abnormal condition correctly under the asynchronous data situation.

• Korean Journal of Materials Research
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• v.31 no.12
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• pp.719-726
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• 2021

Through the process of chemical vapor deposition, Tungsten Hexafluoride (WF₆) is widely used by the semiconductor industry to form tungsten films. Tungsten Hexafluoride (WF₆) is produced through manufacturing processes such as pulverization, wet smelting, calcination and reduction of tungsten ores. The manufacturing process of Tungsten Hexafluoride (WF₆) is required thorough quality control to improve productivity. In this paper, a real-time detection system for oxidation defects that occur in the manufacturing process of Tungsten Hexafluoride (WF₆) is proposed. The proposed system is implemented by applying YOLOv5 based on Convolutional Neural Network (CNN); it is expected to enable more stable management than existing management, which relies on skilled workers. The implementation method of the proposed system and the results of performance comparison are presented to prove the feasibility of the method for improving the efficiency of the WF₆ manufacturing process in this paper. The proposed system applying YOLOv5s, which is the most suitable material in the actual production environment, demonstrates high accuracy (mAP@0.5 99.4 %) and real-time detection speed (FPS 46).