• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2006.06a
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• pp.192-193
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• 2006

The wet coating process could easily be made from large area film with printing paste mixed with semiconductor and binder material at room temperature. Semiconductor film fabricated about 25mm thickness was evaluated by field emissions-canning electron microscopy (FE-SEM). X-ray performance data such as dark current, sensitivity and signal to noise ratio (SNR) were evaluated. The $Hgl_2$ semiconductor was shown in much lower dark current than the others, but the best sensitivity. In this paper, reactivity and combination character of semiconductor and binder material that affect electrical and X-ray detection properties would prove out though experimental results.

• Journal of the Semiconductor & Display Technology
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• v.15 no.3
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• pp.40-45
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• 2016

In this paper, the fire engine pump is controlled and monitored by the AJAX (Asynchronous Javascript and Xml) in the web server. The embedded system with built-in system having a processor and a memory of high performance occurs many problems in transmitting the large amount of data in real time through the web server. The AJAX is different from HTML (Hyper Text Makeup Language) with java script technology and can make RIA (Rich Internet Application). It process the necessary data by using asynchronous and it take advantage of usefulness, accessibility, a fast response time. Using AJAX can build up web server with real time and monitoring that fire engine pump status, check processing pump memory in the event of fire, also remotely monitors can do. The web server system can control the fire engine pump as like the black box. The experimental results show the effectiveness and commercialize possibility.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.27-31
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• 2021

VI probe, which is one of various in-situ plasma monitoring sensor, is frequently used for in-situ process monitoring in mass production environment. In this paper, we correlated the plasma etch results with VI probe data with the small amount of gas flow rate changes to propose usefulness of the VI probe in real-time process monitoring. Several different sized contact holes were employed for the etch experiment, and the etched profiles were measured by scanning electron microscope (SEM). Although the shape of etched hole did not show satisfactory relationship with VI probe data, the chamber status changed along the incremental/decremental modification of the amount of gas flow was successfully observed in terms of impedance monitoring.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.168-173
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• 2022

As the function of a product is advanced and the process is refined, the yield in the fine manufacturing process becomes an important variable that determines the cost and quality of the product. Since a fine manufacturing process generally produces a product through many steps, it is difficult to find which process or equipment has a defect, and thus it is practically difficult to ensure a high yield. This paper presents the system architecture of how to build a smart manufacturing system to analyze the big data of the manufacturing plant, and the equipment factor analysis methodology to increase the yield of products in the smart manufacturing system. In order to improve the yield of the product, it is necessary to analyze the defect factor that causes the low yield among the numerous factors of the equipment, and find and manage the equipment factor that affects the defect factor. This study analyzed the key factors of abnormal equipment that affect the yield of products in the manufacturing process using the data mining technique. Eventually, a methodology for finding key factors of abnormal equipment that directly affect the yield of products in smart manufacturing systems is presented. The methodology presented in this study was applied to the actual manufacturing plant to confirm the effect of key factors of important facilities on yield.

• Proceedings of the Safety Management and Science Conference
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• 2009.11a
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• pp.187-201
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• 2009

Semiconductor industry is based on equipment industry and timing industry. In particular, semiconductor process is very complex and as semiconductor-chip width tails and is becoming equipment gradually more as a high technology. Equipment operation is primarily engaged in semiconductor manufacturing (engineers and operator) of being conducted by, equipment errors have also been raised. Equipment operational data related to the error of korea occupational safety and health agency were based on data and production engineers involved in the operator's questionnaire was drawn through the error factor. Equipment operating in the error factor of 9 big item and 36 detail item detailed argument based on the errors down, and 9 big item the equipment during operation of the correlation error factor was conducted. Each of the significance level was correlated with the tabulation and analysis. Using the maximum correlation coefficient, the correlation between the error factors to derive the relationship between factors were analyzed. Facility operating with the analysis of error factors (big and detail item) derive a relationship between the model saw. The end of the operation of the facility in operation on the part of the two factors appeared as prevention. Safety aspects and ergonomics aspects of the approach should be guided to the conclusion.

• Journal of the Korea Society of Computer and Information
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• v.12 no.2 s.46
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• pp.151-159
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• 2007

It is very important to collect all production-related status values during the manufacturing process of semiconductor. The analysis results of the collected data can be used for the operation rate, fault diagnosis, process control and removal of predicted obstacles of equipments, eventually contributing to the improvement of production efficiency. For this propose. many IC makers have adopted EES(Equipment Engineering System). As the use of web has become a daily lift activity lately, it has been suggested to expand the scope of monitoring equipments using HTTP or SOAP protocols. To fulfill the web-based EES, EDA(Equipment Data Aquisition) should be facilitated first by integrating and standardizing various forms of messages generated from many different semiconductor equipments. In this paper, a method for integration between different types of information is suggested based on the analysis of various protocols used for the communication between semiconductor equipments. In addition, a software architecture to support the method is desisted.

• IE interfaces
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• v.25 no.2
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• pp.264-275
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• 2012

The process of semiconductor(IC Package) manufacturing usually includes lots of complex and sequential processes. Many kinds of equipments are installed with the mixed concept of serial and parallel manufacturing system. The business environments of the semiconductor industry have been changed frequently, because new technologies are developed continuously. It is the main reason of new investment plan and layout consideration. However, it is difficult to change the layout after installation, because the major equipments are expensive and difficult to move. Furthermore, it is usually a multiple-objective problem. Thus, new investment or layout change should be carefully considered when the production environments likewise product mix and production quantity are changed. This paper introduces a simulation case study of a Korean company that produces packaging substrates(especially lead frames) and requires multi-objective decision support. $QUEST^{(R)}$ is used for simulation modelling and AHP(Analytic Hierarchy Process) and DEA(Data Envelopment Analysis) are used for weighting of qualitative performance measures and solving multiple-objective layout problem, respectively.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2009.11a
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• pp.44-44
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• 2009

Fault detection is necessary for yield enhancement and cost reduction in semiconductor manufacturing. Sensory data acquired from the semiconductor processing tool is too large to analyze for the purpose of fault detection and classification(FDC). We studied the techniques of fault detection using statistical method. Multiple regression analysis smoothly detected faults and can be easy made a model. For real-time and fast computing time, the huge data was analyzed by each step. We also considered interaction and critical factors in tool parameters and process.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.12 no.5
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• pp.141-148
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• 2013

There are several problems in the conventional 2-D image processing and 3-D measurement systems. In the case of the 2-D image processing system, it is not possible to detect elevation data. In a 3-D measurement system, it requires a skillful operator and a lot of time for measuring data. Also, there exist data errors depending on operators. The limitation of detecting elevation data in the 2-D image processing system can be solved by laser diodes. In this study an algorithm that measures the accurate data in a subject face to be detected by combining laser diodes and a commercial CCD camera is developed. In the development process, a planar equation is developed using laser diodes and the equation is used to obtain a normal vector. Based on the results, an algorithm that transforms commercial CCD camera coordinates to 3-D coordinates is proposed. The completed measurement method will be applied to replace a manual measurement system for vehicle bodies and parts by an automated system.

• IE interfaces
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• v.22 no.3
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• pp.252-262
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• 2009

It is crucial to prevent low yields in the semiconductor industry. Since many factors affect variation in yield and they are deeply related, preventing low yield is difficult. There have been substantial researches in the field of yield prediction. Many researchers had used the statistical methods. Many studies have shown that artificial neural network (ANN) achieved better performance than traditional statistical methods. However, despite ANN's superior performance some problems such as over-fitting and poor explanatory power arise. In order to overcome these limitations, a relatively new machine learning technique, support vector machine (SVM), is introduced to classify the yield. SVM is simple enough to be analyzed mathematically, and it leads to high performances in practical applications. This study presents a new efficient classification methodology, Stepwise-SVM (SSVM), for detecting high and low yields. SSVM is step-by-step adjustment of parameters to be precisely the classification for actual high and low yield lot. The objective of this paper is to examine the feasibility of SVM and SSVM in the yield classification. The experimental results show that SVM and SSVM provides a promising alternative to yield classification for the field data.