• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.08a
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• pp.148-148
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• 2011

Recently, there have been many research activities to develop the large-area plasma source, which is able to generate the high-density plasma with relatively good uniformity, for the plasma processing in the thin-film solar cell and display panel industries. The large-area CCP sources have been applied to the PECVD process as well as the etching. Especially, the PECVD processes for the depositions of various films such as a-Si:H, ${\mu}c$-Si:H, Si3N4, and SiO2 take a significant portion of processes. In order to achieve higher deposition rate (DR), good uniformity in large-area reactor, and good film quality (low defect density, high film strength, etc.), the application of VHF (>40 MHz) CCP is indispensible. However, the electromagnetic wave effect in the VHF CCP becomes an issue to resolve for the achievement of good uniformity of plasma and film. Here, we propose a new electrode as part of a method to resolve the standing wave effect in the large-area VHF CCP. The electrode is split up a series of strip-type electrodes and the strip-type electrodes and the ground ones are arranged by turns. The standing wave effect in the longitudinal direction of the strip-type electrode is reduced by using the multi-feeding method of VHF power and the uniformity in the transverse direction of the electrodes is achieved by controlling the gas flow and the gap length between the powered electrodes and the substrate. Also, we provide the process results for the growths of the a-Si:H and the ${\mu}c$-Si:H films. The high DR (2.4 nm/s for a-Si:H film and 1.5 nm/s for the ${\mu}c$-Si:H film), the controllable crystallinity (~70%) for the ${\mu}c$-Si:H film, and the relatively good uniformity (1% for a-Si:H film and 7% for the ${\mu}c$-Si:H film) can be obtained at the high frequency of 40 MHz in the large-area discharge (280 mm${\times}$540 mm). Finally, we will discuss the issues in expanding the multi-electrode to the 8G class large-area plasma processing (2.2 m${\times}$2.4 m) and in improving the process efficiency.

• Applied Chemistry for Engineering
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• v.25 no.6
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• pp.624-631
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• 2014

In this study, a new series of conjugated polymer 3-(5-(5,6-bis(octyloxy)-7-(thiophen-2-yl)benzo[c][1,2,5]thiadiazol-4-yl)thiophen-2-yl)-10-(4-(octyloxy)phenyl)-10H-phenothiazine (P1) and 3-(5-(5,6-bis(octyloxy)-7-(thiophen-2-yl)benzo[c][1,2,5]thiadiazol-4-yl)thiophen-2-yl)-10-(4-((2-ethylhexyl)oxy)phenyl)-10H-phenothiazine (P2) were synthesised and organic photovoltaics (OPVs) properties were characterized. The push-pull structure polymer consisted of phenothiazine derivative as an electron donor and benzothiadiazole derivative as an electron acceptor. The aliphatic chain substituted aromatic ring was substituted at the position of N in phenothiazine for the electron-rich and improved solubility. Excellent thermal stabilities of P1 and P2 were confirmed by measured Td values as 321.9 and $323.7^{\circ}C$, respectively and the degrees of polymerization were 4,911 (P1) and 5,294 (P2). The maximum absorption wavelength of P1 and P2 were 549 and 566 nm, respectively. The device was fabricated and the OPVs property was measured. As a result, the power efficiency of conversion for P1 and P2 were 0.96 and 0.90%, respectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.12
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• pp.2857-2863
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• 2014

IR-UWB has been developed as a standard of indoor ranging technology, because it has robust and good transmission characteristics in indoor environments and it can be operated with low power. In this paper, a IR-UWB packet analyzer is designed and implemented based on IEEE 802.15.4a, which is useful in developing IR-UWB real time location system with resolution of a few ten centimeters. A sniffer device of the packet analyzer monitors IR-UWB wireless networks, captures MAC packet frames, and transmits packet frames to the packet analyzing computer. The packet analyzing program in a computer analyzes received MAC packet frames and displays parsed packet information for developing engineers. Developed packet analyzer is used to analyze IEEE 802.15.4a MAC protocol, and also it can be used in other IEEE 802 series MAC protocol by modifying some functions.

• Asia pacific journal of information systems
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• v.23 no.4
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• pp.29-51
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• 2013

In line with network technology development and smart device penetration, the social network service (SNS) has expanded its influence. The SNS which is a service based on communication and sharing among people, has grown based on users' voluntary engagement and participation and its influence has appeared beyond the cyberspace into the overall areas of domestic and foreign culture and society. In particular, SNS-based real-time communication during diverse disasters, can help prevent further damage. By sharing information on social donation activities and environmental campaigns, people have used SNS as a tool to change the society in a more positive way. Such series of activities functioning as a power to change the society have been made much faster and wider through the help of a new media called SNS. To better understand such trends, we are required to study about the SNS and its user relationships first. In this context, this study sought to identify the effects of people's desire to build friendships through SNS on the voluntary and society-friendly activities of people. This study considers online pro-social behavior and proposes online citizenship behavior. Citizenship behavior has been examined in organization context. That is, organizational citizenship behavior explains an employee's pro-social behavior in an organization context. Organizational citizenship behavior is characterized by the individual's helping others and promoting the functioning of the organization. By applying organizational citizenship behavior to an online context, we propose online citizenship behavior, an individual's pro-social behavior in an online context. An individual's pro-social behavior, i.e., online citizenship behavior, could be considered as a way for the better management of online community and society. It also needs to examine the development of online citizenship behavior. This study examined online citizenship behavior from the friendship desire. Because online society or community is characterized by online relationships between members, the friendship between members would lead to pro-social behavior, i.e., helping others and promoting the functioning of the online society, in such online context. This study further examines the antecedents of friendship desire in terms of SNS interactivity with its four factors. The findings based on the survey from real SNS users explain that the three factors of SNS interactivity (connectivity, enjoyment, and synchronicity) increases online friendship desire which then increases online citizenship behavior significantly. This study contributes to the literature by examining the key role of online friendship desire in leading to online citizenship behavior and identifying its antecedents in terms of SNS characteristics. The findings in this study also provide guidance on how to manage online society and how to promote the effective functioning of SNS.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.4
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• pp.80-87
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• 2015

Memristor is a new kind of memory device whose resistance varies depending upon applied charge and whose previous resistance state is preserved even when its power is off. Ordinary memristor has a nonlinear programming characteristics about time when a constant voltage is applied. For the easiness of programming, it is desirable that resistance is programmed linearly about time. We had proposed previously a memristor bridge configuration with which weight can be programmed nicely in positive, negative or zero. In memristor bridge circuit, two memristors are connected in series with different polarity. Memristors are complementary each other and it follows that the memristance variation is linear with respect to time. In this paper, the linearization effect of weight programming of memristor bridge synapse is investigated and verified about both $TiO_2$ memristor from HP and a nonlinear memristor with a window function. Memristor bridge circuit would be helpful to conduct synaptic weight programming.

• Journal of Dental Rehabilitation and Applied Science
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• v.19 no.2
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• pp.87-96
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• 2003

The purposes of this study were to find out the optimum intensity of magnetic field where magnetism could promote the activity of osteoblast, and to discover the possibility of clinical application in the areas of dental implants and bone grafts by confirming the effect of clinically increasing bone formation. In this experiment, we used the Neodymium magnet, which had magnetic power six times as strong as the current ones and enabled the resistances against the demagnetization up to 20 to 50 times to be minimized with the size of 1mm in sight. In order to culture cells, a specially designed device was used. It was made to adjust the distance and accordingly to control the intensity of the magnetic field, by placing the cell culture plate in the center with a magnet of 1mm long and thick installed on the both ends. Using MC3T3-E1 cell, a kind of osteoblast-like cell, we cultured, for 24 hours, not only the test group which had been cultured under the magnetic fields with different intensity of 5, 10, 50, 100, 500, and 1000 Gauss, but also the control group excluding the influences of the magnetic field. After observing the cell's form and the density of the culture medium through an inverted microscope, we made a series of proceedings needed for the immunofluoroscence staining, such as fixation, normal serum reaction, primary antibody reaction, and secondary antibody reaction. And with a fluorescence microscope, we observed those-above and compared the frequency of expression of IFG-1 receptor. To make a Western immunoblotting analysis, the cells cultured under the same condition as the above had the procedure of the lysis buffer and the acrylamide gel electrophoresis was carried out. Protein transferred into the nitrocellulose membrane and tested on the primary and the secondary antibody reactions was observed and compared. The results were as follows: When observed through an inverted microscope, the nuclear divisions of the cells under the magnetic field of 10 Gauss were the most active, and the density of the cells could be observed the most enormously. As the result of an immunofluoroscence staining of IGF-1 receptor, the expression of IFG-1 was the most frequently observed under the magnetic field of 10 Gauss. On the other hand, few differences of consideration were made between the test group cultured under the magnetic fields of 5, 500, and 1000 Gauss and the control group. In respect of the expression of IFG-1 receptor, the test group cultured under the magnetic fields of 50 and 100 Gauss were higher than the control group, and lower than that cultured under the magnetic field of 10 Gauss.(p<0.05) According to the Western immunoblotting analysis, the band of IFG-1 receptor which had 85KDa of molecular weight was the darkest. Judging from the above-mentioned results, the growth factor receptor of an osteoblast cell which was an important criterion for the bone formation was increased in maximum under the magnetic field of 10 Gauss. Moreover it was observed that the optimum intensity of magnetic field in which magnetism made the activity of the osteoblast cell increase was about 10 Gauss.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.