• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.189-190
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• 2011

Cu가 기존 배선물질인 Al을 대체함에 따라 resistance-capacitance (RC) delay나 electromigration (EM) 등의 문제들이 어느 정도 해결되었다. 그러나 지속적인 배선 폭의 감소로 배선의 저항 증가, EM 현상 강화 그리고 stability 악화 등의 문제가 지속적으로 야기되고 있다. 이를 해결하기 위한 방법으로 Cu alloy seed layer를 이용한 barrier 자가형성 공정에 대한 연구를 진행하였다. 이 공정은 Cu 합금을 seed layer로 사용하여 도금을 한 후 열처리를 통해 SiO2와의 계면에서 barrier를 자가 형성시키는 공정이다. 이 공정은 매우 균일하고 얇은 barrier를 형성할 수 있고 별도의 barrier와 glue layer를 형성하지 않아 seed layer를 위한 공간을 추가로 확보할 수 있는 장점을 가지고 있다. 또한, via bottom에 barrier가 형성되지 않아 배선 전체 저항을 급격히 낮출 수 있다. 합금 물질로는 초기 Al이나 Mg에 대한 연구가 진행되었으나, 낮은 oxide formation energy로 인해 SiO2에 과도한 손상을 주는 문제점이 제기되었다. 최근 Mn을 합금 물질로 사용한 안정적인 barrier 형성 공정이 보고 되고 있다. 하지만, barrier 형성을 하기 위해 300도 이상의 열처리 온도가 필요하고 열처리 시간 또한 긴 단점이 있다. 본 실험에서는 co-sputtering system을 사용하여 Cu-V 합금을 형성하였고, barrier를 자가 형성을 위해 300도에서 500도까지 열처리 온도를 변화시키며 1시간 동안 열처리를 실시하였다. Cu-V 공정 조건 확립을 위해 AFM, XRD, 4-point probe system을 이용하여 표면 거칠기, 결정성과 비저항을 평가하였다. Cu-V 박막 내 V의 함량은 V target의 plasma power density를 변화시켜 조절 하였으며 XPS를 통해 분석하였다. 열처리 후 시편의 단면을 TEM으로 분석하여 Cu-V 박막과 SiO2 사이에 interlayer가 형성된 것을 확인 하였으며 EDS를 이용한 element mapping을 통해 Cu-V 내 V의 거동과 interlayer의 성분을 확인하였다. PVD Cu-V 박막은 기판 온도에 큰 영향을 받았고, 200 도 이상에서는 Cu의 높은 표면에너지에 의한 agglomeration 현상으로 거친 표면을 가지는 박막이 형성되었다. 7.61 at.%의 V함량을 가지는 Cu-V 박막을 300도에서 1시간 열처리 한 결과 4.5 nm의 V based oxide interlayer가 형성된 것을 확인하였다. 열처리에 의해 Cu-V 박막 내 V은 SiO2와의 계면과 박막 표면으로 확산하며 oxide를 형성했으며 Cu-V 박막 내 V 함량은 줄어들었다. 300, 400, 500도에서 열처리 한 결과 동일 조성과 열처리 온도에서 Cu-Mn에 의해 형성된 interlayer의 두께 보다 두껍게 성장 했다. 이는 V의 oxide formation nergyrk Mn 보다 작으므로 SiO2와의 계면에서 산화막 형성이 쉽기 때문으로 판단된다. 또한, V+5 이온 반경이 Mn+2 이온 반경보다 작아 oxide 내부에서 확산이 용이하며 oxide 박막 내에 여기되는 전기장이 더 큰 산화수를 가지는 V의 경우 더 크기 때문으로 판단된다.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.256-256
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• 2012

Cu가 기존 배선물질인 Al을 대체함에 따라 resistance-capacitance delay와 electromigration (EM) 등의 문제들이 어느 정도 해결되었다. 그러나 지속적인 배선 폭의 감소로 배선의 저항 증가, EM 현상 강화 그리고 stability 악화 등의 문제가 지속적으로 야기되고 있다. 이를 해결하기 위한 방법으로 Cu alloy seed layer를 이용한 barrier 자가형성 공정에 대한 연구를 진행하였다. 이 공정은 Cu 합금을 seed layer로 사용하여 도금을 한 후 열처리를 통해 $SiO_2$와의 계면에서 barrier를 자가 형성시키는 공정이다. 이 공정은 매우 균일하고 얇은 barrier를 형성할 수 있고 별도의 barrier와 glue layer를 형성하지 않아 seed layer를 위한 공간을 추가로 확보할 수 있는 장점을 가지고 있다. 또한, via bottom에 barrier가 형성되지 않아 배선 전체 저항을 급격히 낮출 수 있다. 합금 물질로는 초기 Al이나 Mg에 대한 연구가 진행되었으나, 낮은 oxide formation energy로 인해 SiO2에 과도한 손상을 주는 문제점이 제기되었다. 최근 Mn을 합금 물질로 사용한 안정적인 barrier 형성 공정이 보고 되고 있다. 하지만, barrier 형성을 하기 위해 300도 이상의 열처리 온도가 필요하고 열처리 시간 또한 긴 단점이 있다. 본 실험에서는 co-sputtering system을 사용하여 Cu-V 합금을 형성하였고, barrier를 자가 형성을 위해 300도에서 500도까지 열처리 온도를 변화시키며 1시간 동안 열처리를 실시하였다. Cu-V 공정 조건 확립을 위해 AFM, XRD, 4-point probe system을 이용하여 표면 거칠기, 결정성과 비저항을 평가하였다. Cu-V 박막 내 V의 함량은 V target의 plasma power density를 변화시켜 조절 하였으며 XPS를 통해 분석하였다. 열처리 후 시편의 단면을 TEM으로 분석하여 Cu-V 박막과 $SiO_2$ 사이에 interlayer가 형성된 것을 확인 하였으며 EDS를 이용한 element mapping을 통해 Cu-V 내 V의 거동과 interlayer의 성분을 확인하였다. PVD Cu-V 박막은 기판 온도에 큰 영향을 받았고, 200도 이상에서는 Cu의 높은 표면에너지에 의한 agglomeration 현상으로 거친 표면을 가지는 박막이 형성되었다. 7.61 at.%의 V함량을 가지는 Cu-V 박막을 300도에서 1시간 열처리 한 결과 4.5 nm의 V based oxide interlayer가 형성된 것을 확인하였다. 열처리에 의해 Cu-V 박막 내 V은 $SiO_2$와의 계면과 박막 표면으로 확산하며 oxide를 형성했으며 Cu-V 박막 내 V 함량은 줄어들었다. 300, 400, 500도에서 열처리 한 결과 동일 조성과 열처리 온도에서 Cu-Mn에 의해 형성된 interlayer의 두께 보다 두껍게 성장했다. 이는 V의 oxide formation energy가 Mn 보다 작으므로 SiO2와의 계면에서 산화막 형성이 쉽기 때문으로 판단된다. 또한, $V^{+5}$이온 반경이 $Mn^{+2}$이온 반경보다 작아 oxide 내부에서 확산이 용이하며 oxide 박막 내에 여기되는 전기장이 더 큰 산화수를 가지는 V의 경우 더 크기 때문으로 판단된다.

• The KIPS Transactions:PartD
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• v.8D no.5
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• pp.507-512
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• 2001

Feature-based similarity has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects. the performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increase. The $R^{*}-Tree$ is the most successful variant of the R-Tree. In this paper, we propose a SOM-based $R^{*}-Tree$ as a new indexing method for high-dimensional feature vectors. The SOM-based $R^{*}-Tree$ combines SOM and $R^{*}-Tree$ to achieve search performance more scalable to high-dimensionalties. Self-Organizingf Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. We experimentally compare the retrieval time cost of a SOM-based $R^{*}-Tree$ with of an SOM and $R^{*}-Tree$ using color feature vectors extracted from 40,000 images. The results show that the SOM-based $R^{*}-Tree$ outperform both the SOM and $R^{*}-Tree$ due to reduction of the number of nodes to build $R^{*}-Tree$ and retrieval time cost.

• Child Health Nursing Research
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• v.20 no.1
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• pp.49-57
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• 2014

Purpose: The purpose of this qualitative study was to explore how adolescent survivors of childhood cancer grieve the death of cancer peers. Methods: Data were obtained from Korean adolescents with cancer between the ages of 13 and 18 (N=12) through semi-structured interviews (face-to-face, telephone, and Internet chatting), observations of the social dynamics of participants in self-help groups, and retrieval of personal Web journals. Based on the grounded theory methodology, data collection and analysis were conducted simultaneously, and constant comparative methods were used. Clarke's situational analysis was adopted, and this paper focused on presenting "how to" and "what we can learn" from this analytic strategy. Results: Mapping examples were visualized using of three modes of maps. Adolescent cancer survivors coped with reminders of the "darkness" that ultimately featured their overall grief. Additionally, adolescents' encounters and avoidance of grief were triggered by introspection and interactions with family and friends. Conclusion: Situational analysis provided an efficient way to analyze the experiences of adolescent survivors of childhood cancer by systematizing possible information within the relational social contexts of the research phenomenon.

• KIPS Transactions on Software and Data Engineering
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• v.1 no.1
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• pp.19-30
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• 2012

The context aware service is the service to provide useful information to the users by recognizing surroundings around people who receive the service via computer based on computing and communication, and by conducting self-decision. But CAS(Context Awareness System) shows the weak point of small-scale context awareness processing capacity due to restricted mobile function under the current mobile environment, memory space, and inference cost increment. In this paper, we propose a mobile cloud context system with using Google App Engine based on PaaS(Platform as a Service) in order to get context service in various mobile devices without any subordination to any specific platform. Inference design method of the proposed system makes use of knowledge-based framework with semantic inference that is presented by SWRL rule and OWL ontology and Jess with rule-based inference engine. As well as, it is intended to shorten the context service reasoning time with mapping the regular reasoning of SWRL to Jess reasoning engine by connecting the values such as Class, Property and Individual which are regular information in the form of SWRL to Jess reasoning engine via JessTab plug-in in order to overcome the demerit of queries reasoning method of SparQL in semantic search which is a previous reasoning method.

• Korean Journal of Remote Sensing
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• v.21 no.3
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• pp.189-211
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• 2005

This paper makes an effort to compare the recently evolved soft classification method based on Linear Spectral Mixture Modeling (LSMM) with the traditional hard classification methods based on Iterative Self-Organizing Data Analysis (ISODATA) and Maximum Likelihood Classification (MLC) algorithms in order to achieve appropriate results for mapping, monitoring and preserving valuable coastal wetland ecosystems of southern India using Indian Remote Sensing Satellite (IRS) 1C/1D LISS-III and Landsat-5 Thematic Mapper image data. ISODATA and MLC methods were attempted on these satellite image data to produce maps of 5, 10, 15 and 20 wetland classes for each of three contrast coastal wetland sites, Pitchavaram, Vedaranniyam and Rameswaram. The accuracy of the derived classes was assessed with the simplest descriptive statistic technique called overall accuracy and a discrete multivariate technique called KAPPA accuracy. ISODATA classification resulted in maps with poor accuracy compared to MLC classification that produced maps with improved accuracy. However, there was a systematic decrease in overall accuracy and KAPPA accuracy, when more number of classes was derived from IRS-1C/1D and Landsat-5 TM imagery by ISODATA and MLC. There were two principal factors for the decreased classification accuracy, namely spectral overlapping/confusion and inadequate spatial resolution of the sensors. Compared to the former, the limited instantaneous field of view (IFOV) of these sensors caused occurrence of number of mixture pixels (mixels) in the image and its effect on the classification process was a major problem to deriving accurate wetland cover types, in spite of the increasing spatial resolution of new generation Earth Observation Sensors (EOS). In order to improve the classification accuracy, a soft classification method based on Linear Spectral Mixture Modeling (LSMM) was described to calculate the spectral mixture and classify IRS-1C/1D LISS-III and Landsat-5 TM Imagery. This method considered number of reflectance end-members that form the scene spectra, followed by the determination of their nature and finally the decomposition of the spectra into their endmembers. To evaluate the LSMM areal estimates, resulted fractional end-members were compared with normalized difference vegetation index (NDVI), ground truth data, as well as those estimates derived from the traditional hard classifier (MLC). The findings revealed that NDVI values and vegetation fractions were positively correlated ($r^2$= 0.96, 0.95 and 0.92 for Rameswaram, Vedaranniyam and Pitchavaram respectively) and NDVI and soil fraction values were negatively correlated ($r^2$ =0.53, 0.39 and 0.13), indicating the reliability of the sub-pixel classification. Comparing with ground truth data, the precision of LSMM for deriving moisture fraction was 92% and 96% for soil fraction. The LSMM in general would seem well suited to locating small wetland habitats which occurred as sub-pixel inclusions, and to representing continuous gradations between different habitat types.

• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.31-39
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• 2009

Purpose : This study proposes the keyhole method in order to improve the time resolution of the proton resonance frequency(PRF) MR temperature monitoring technique. The values of Root Mean Square (RMS) error of measured temperature value and Signal-to-Noise Ratio(SNR) obtained from the keyhole and full phase encoded temperature images were compared. Materials and Methods : The PRF method combined with GRE sequence was used to get MR temperature images using a clinical 1.5T MR scanner. It was conducted on the tissue-mimic 2% agarose gel phantom and swine's hock tissue. A MR compatible coaxial slot antenna driven by microwave power generator at 2.45GHz was used to heat the object in the magnetic bore for 5 minutes followed by a sequential acquisition of MR raw data during 10 minutes of cooling period. The acquired raw data were transferred to PC after then the keyhole images were reconstructed by taking the central part of K-space data with 128, 64, 32 and 16 phase encoding lines while the remaining peripheral parts were taken from the 1st reference raw data. The RMS errors were compared with the 256 full encoded self-reference temperature image while the SNR values were compared with the zero filling images. Results : As phase encoding number at the center part on the keyhole temperature images decreased to 128, 64, 32 and 16, the RMS errors of the measured temperature increased to 0.538, 0.712, 0.768 and 0.845$^{\circ}C$, meanwhile SNR values were maintained as the phase encoding number of keyhole part is reduced. Conclusion : This study shows that the keyhole technique is successfully applied to temperature monitoring procedure to increases the temporal resolution by standardizing the matrix size, thus maintained the SNR values. In future, it is expected to implement the MR real time thermal imaging using keyhole method which is able to reduce the scan time with minimal thermal variations.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.57-73
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• 2021

Maintenance and prevention of failure through anomaly detection of ICT infrastructure is becoming important. System monitoring data is multidimensional time series data. When we deal with multidimensional time series data, we have difficulty in considering both characteristics of multidimensional data and characteristics of time series data. When dealing with multidimensional data, correlation between variables should be considered. Existing methods such as probability and linear base, distance base, etc. are degraded due to limitations called the curse of dimensions. In addition, time series data is preprocessed by applying sliding window technique and time series decomposition for self-correlation analysis. These techniques are the cause of increasing the dimension of data, so it is necessary to supplement them. The anomaly detection field is an old research field, and statistical methods and regression analysis were used in the early days. Currently, there are active studies to apply machine learning and artificial neural network technology to this field. Statistically based methods are difficult to apply when data is non-homogeneous, and do not detect local outliers well. The regression analysis method compares the predictive value and the actual value after learning the regression formula based on the parametric statistics and it detects abnormality. Anomaly detection using regression analysis has the disadvantage that the performance is lowered when the model is not solid and the noise or outliers of the data are included. There is a restriction that learning data with noise or outliers should be used. The autoencoder using artificial neural networks is learned to output as similar as possible to input data. It has many advantages compared to existing probability and linear model, cluster analysis, and map learning. It can be applied to data that does not satisfy probability distribution or linear assumption. In addition, it is possible to learn non-mapping without label data for teaching. However, there is a limitation of local outlier identification of multidimensional data in anomaly detection, and there is a problem that the dimension of data is greatly increased due to the characteristics of time series data. In this study, we propose a CMAE (Conditional Multimodal Autoencoder) that enhances the performance of anomaly detection by considering local outliers and time series characteristics. First, we applied Multimodal Autoencoder (MAE) to improve the limitations of local outlier identification of multidimensional data. Multimodals are commonly used to learn different types of inputs, such as voice and image. The different modal shares the bottleneck effect of Autoencoder and it learns correlation. In addition, CAE (Conditional Autoencoder) was used to learn the characteristics of time series data effectively without increasing the dimension of data. In general, conditional input mainly uses category variables, but in this study, time was used as a condition to learn periodicity. The CMAE model proposed in this paper was verified by comparing with the Unimodal Autoencoder (UAE) and Multi-modal Autoencoder (MAE). The restoration performance of Autoencoder for 41 variables was confirmed in the proposed model and the comparison model. The restoration performance is different by variables, and the restoration is normally well operated because the loss value is small for Memory, Disk, and Network modals in all three Autoencoder models. The process modal did not show a significant difference in all three models, and the CPU modal showed excellent performance in CMAE. ROC curve was prepared for the evaluation of anomaly detection performance in the proposed model and the comparison model, and AUC, accuracy, precision, recall, and F1-score were compared. In all indicators, the performance was shown in the order of CMAE, MAE, and AE. Especially, the reproduction rate was 0.9828 for CMAE, which can be confirmed to detect almost most of the abnormalities. The accuracy of the model was also improved and 87.12%, and the F1-score was 0.8883, which is considered to be suitable for anomaly detection. In practical aspect, the proposed model has an additional advantage in addition to performance improvement. The use of techniques such as time series decomposition and sliding windows has the disadvantage of managing unnecessary procedures; and their dimensional increase can cause a decrease in the computational speed in inference.The proposed model has characteristics that are easy to apply to practical tasks such as inference speed and model management.