• Journal of Microbiology and Biotechnology
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• v.10 no.4
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• pp.554-558
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• 2000

Bacteria were isolated and identified from an advanced high-purity water system that supplies ultra-high purity water (UHPW) for 16-megabyte DRAM semiconductor manufacturing. Scanning electron microscopic and microbiological observations revealed that the primary source of the bacteria isolated from the UHPW was detached cells from biofilms developed on the pipe wall through which the UHPW, a man-made and extremely nutrient poor environment, was passing. About 63-65% of the bacteria isolated from the UHPW and the pipe wall were Gram-positive, whereas only 10% of the bacteria isolated from the feed water were Gram-positive. The of Gram-positive bacteria and seven genera of Gram-negative bacteria. Strains of the UHPW bacteria effectively adhered to and formed a biofilm on the surface of polyvinyl chloride (PVC) pipe.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.68-72
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• 2020

A new method for array testing of TFT-CD panel with the integrated gate driver circuits is presented. As larger size/high resolution TFT-LCD with the peripheral driver circuits has emerged, one of the important problems for manufacturing is array testing on the panel. This paper describes the technology of detecting defective arrays and optimizing the array testing process. For the effective characterization of pixel array, the pixel storage capability is simulated and measured with voltage imaging system. This technology permits full functional testing during the manufacturing process, enabling fabrication of large TFT-LCD panels with the integrated driver circuits.

• Journal of the Semiconductor & Display Technology
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• v.15 no.2
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• pp.70-73
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• 2016

Previously proposed contamination detecting sensor was revisited for the investigation of the liquid tendency. Experiments revealed different output voltages for several kinds of liquid input, but showed same values for various flow rates of each liquid. The transmittance of the liquid was measured, and it is well correlated with the voltages. Linearity in values and the compensation of the sensor to sensor deviation were tried to obtain. And, long term test was performed as attached at the manufacturing equipment in the field.

• Journal of the Semiconductor & Display Technology
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• v.7 no.2
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• pp.39-43
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• 2008

Productivity is one of the performance indices of the semiconductor equipment in manufacturing viewpoint. Among many ways tried and adopted for improvement of the productivity of the FAB equipment, variation of equipment configuration was considered and its effect on the throughput was analyzed. Parallel machine cycle charts that were generated based on the equipment log were used in the analysis. Efficiency of the equipment due to change of the structure and the probability of the usage in the manufacturing process were examined. The results showed that the modification of the control algorithm in the equipment and the redistribution of the process time for each process and transfer module along to the change in the structure enhance the throughput of the equipment.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1678-1679
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• 2007

This paper is the electric power system remote control of semiconductor plasma manufacturing equipment using PLC(power line communication). PLC is useful for economical data link but various problems and limitations are caused in using power lines for communications channel Develop of Semiconductor plasma manufactur ing equipment and remote automation technologies of tool develops day after day and standards. Also, Remote electric power control and device module control by GUIRCS(Graphic User Interface Remote Control System) of tool are monitoring in real time.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.121-125
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• 2020

With miniaturization of semiconductor, the manufacturing process become more complex, and undetected small changes in the state of the equipment have unexpectedly changed the process results. Fault detection classification (FDC) system that conducts more active data analysis is feasible to achieve more precise manufacturing process control with advanced machine learning method. However, applying machine learning, especially in supervised learning criteria, requires an arduous data labeling process for the construction of machine learning data. In this paper, we propose a semi-supervised learning to minimize the data labeling work for the data preprocessing. We employed equipment status variable identification (SVID) data and optical emission spectroscopy data (OES) in silicon etch with SF6/O2/Ar gas mixture, and the result shows as high as 95.2% of labeling accuracy with the suggested semi-supervised learning algorithm.

• Industrial Engineering and Management Systems
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• v.12 no.4
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• pp.306-316
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• 2013

The semiconductor industry has grown rapidly, and subsequently production planning problems have raised many important research issues. The reentrant flow-shop (RFS) scheduling problem with time windows constraint for harddisk devices (HDD) manufacturing is one such problem of the expanded semiconductor industry. The RFS scheduling problem with the objective of minimizing the makespan of jobs is considered. Meeting this objective is directly related to maximizing the system throughput which is the most important of HDD industry requirements. Moreover, most manufacturing systems have to handle the quality of semiconductor material. The time windows constraint in the manufacturing system must then be considered. In this paper, we propose a hybrid genetic algorithm (HGA) for improving chromosomes/offspring by checking and repairing time window constraint and improving offspring by left-shift routines as a local search algorithm to solve effectively the RFS scheduling problem with time windows constraint. Numerical experiments on several problems show that the proposed HGA approach has higher search capability to improve quality of solutions.

• Proceedings of the SAREK Conference
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• 2008.06a
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• pp.844-849
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• 2008

In recent semiconductor manufacturing clean rooms, in order to improve clean room air quality, air washers are used to remove airborne gaseous contaminants such as $NH_3$, SOx and organic gases from outdoor air introduced into clean room. Meanwhile, there is a large quantity of exhaust air from clean room. From the energy saving point of view, heat recovery is useful for the reduction of air conditioning energy consumption for clean room. Therefore it is desirable to recover heat from the exhaust air and use it to reheat the outdoor air. However, so far there have not been sufficient studies of analyzing the comparison of the amounts of energy consumption and saving. In the present study, an experiment was conducted to investigate the energy consumption and heat recovery of a fin-coil type air washer system for semiconductor manufacturing clean rooms.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.5
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• pp.40-46
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• 2017

A plasma gas control apparatus for semiconductor plasma etching processes securely holds a cathode for forming a plasma, confines the plasma during the plasma etching process, and discharges gas after etching. It is a key part of the etching process. With the advancement of semiconductor technology, there is increasing interest in parts for semiconductor manufacturing that directly affect wafers. Accordingly, in order to replace the plasma gas control device with a CVD-SiC material superior in mechanical properties to existing SiCs (Sintered-SiC, RB-SiC), a study on the grinding characteristics of CVD-SiC was carried out. It is confirmed that the optimal grinding condition was obtained when the result table feed rate was 2 m/min and the infeed depth was $5{\mu}m$.

• IE interfaces
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• v.22 no.2
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• pp.126-134
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• 2009

As product quality and yield are essential factors in semiconductor manufacturing, monitoring the main manufacturing steps is a critical task. For the purpose, FDC(Fault detection and classification) is used for diagnosing fault states in the processes by monitoring data stream collected by equipment sensors. This paper proposes an FDC model based on decision tree which provides if-then classification rules for causal analysis of the processing results. Unlike previous decision tree approaches, we reflect the structural aspect of the data stream to FDC. For this, we segment the data stream into multiple subregions, define structural features for each subregion, and select the features which have high relevance to results of the process and low redundancy to other features. As the result, we can construct simple, but highly accurate FDC model. Experiments using the data stream collected from etching process show that the proposed method is able to classify normal/abnormal states with high accuracy.