• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.247-251
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• 2004

반도체 소자의 제조에 있어 실리콘 표면에 성장한 자연산화막을 제거하기 위해 일반적으로 습식 세정 기술이 이용되어 왔다. 하지만 소자의 최소 선폭(design rule)이 nano급으로 고집적화 됨에 따라 contact hole 바닥의 자연산화막을 깨끗이 제거하는데 있어서 그 한계를 나타나고 있다. 이에 대한 효과적인 대안 공정으로 가스 건식 세정 기술이 연구되고 있다. 본 논문에서는 한 번에 50매 이상의 웨이퍼를 처리함으로써 생산성 측면에서 월등한 배치식 설비에서 원거리 플라즈마(remote plasma) 장치에서 2.450Hz의 마이크로웨이브(${\mu}$-wave)에 의해 형성시킨 수소라디칼과 $NF_3$ 가스를 이용하여 실리콘에 결함을 주지 않고 자연산화막을 선택적으로 제거하는 공정에 대해 고찰하였다. AFM을 이용한 표면분석, TEM을 이용한 물성분석, 그리고 ToF-SIMS 및 XPS를 이용한 화학 분석을 습식 및 건식 세정을 비교 평가한 결과, 건식 세정 공정이 실리콘 표면에 결함을 주지 않고 자연산화막을 제거 할 수 있음을 확인하였다. 산화막$(SiO_2)$, 질화막$(Si_3N_4)$, 그리고 다결정 실리콘(Poly-Si) 등의 각 막질별 식각 특성을 고찰하였으며, $NH_3$의 캐리어 가스인 $N_2$의 주입량을 조절함으로써 수소라디칼 형성 효율의 개선이 가능하였으며, 이로부터 게이트와 소스/드레인 사이를 절연하기 위해 이용되는 질화막의 식각 선택비를 2배 정도 개선할 수 있었다. nano급 소자에 실장하여 평가한 결과에서 불산(HF)에 의한 습식 세정 방식에 비하여 약 $20{\sim}50%$ 정도의 contact 저항 감소 효과가 있음이 확인되었다.두 소자 모두 $40mA/cm^2$ 에서 이상적인 화이트 발란스와 같은(0.33,0.33)의 색좌표를 보였다.epsilon}_0=1345$의 빼어난 압전 및 유전특성과 $330^{\circ}C$의 높은 $T_c$를 보였고 그 조성의 vibration velocity는 약4.5 m/s로 나타났다.한 관심이 높아지고 있다. 그러나 고 자장 영상에서의 rf field 에 의한 SAR 증가는 중요한 제한 요소로 부각되고 있다. 나선주사영상은 SAR 문제가 근원적으로 발생하지 않고, EPI에 비하여 하드웨어 요구 조건이 낮아 고 자장에서의 고속영상방법으로 적합하다. 본 논문에서는 고차 shimming 을 통하여 불균일도를 개선하고, single shot 과 interleaving 을 적용한 multi-shot 나선주사영상 기법으로 $100{\times}100$에서 $256{\times}256$의 고해상도 영상을 얻어 고 자장에서 초고속영상기법으로 다양한 적용 가능성을 보였다. 연구에서 연구된 $[^{18}F]F_2$가스는 친핵성 치환반응으로 방사성동위원소를 도입하기 어려운 다양한 방사성의 약품개발에 유용하게 이용될 수 있을 것이다.었으나 움직임 보정 후 영상을 이용하여 비교한 경우, 결합능 변화가 선조체 영역에서 국한되어 나타나며 그 유의성이 움직임 보정 전에 비하여 낮음을 알 수 있었다. 결론: 뇌활성화 과제 수행시에 동반되는 피험자의 머리 움직임에 의하여 도파민 유리가 과대평가되었으며 이는 이 연구에서 제안한 영상정합을 이용한 움직임 보정기법에 의해서 개선되었다. 답이 없는 문제, 문제 만들기, 일반화가 가능한 문제 등으로 보고, 수학적 창의성 중 특히 확산적 사고에 초점을 맞추어 개방형 문제가 확

• Molecules and Cells
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• v.21 no.1
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• pp.52-62
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• 2006

In previous reports we demonstrated that ginsenosides, active ingredients of Panax ginseng, affect some subsets of voltage-dependent $Ca^{2+}$ channels in neuronal cells expressed in Xenopus laevis oocytes. However, the major component(s) of ginseng that affect cloned $Ca^{2+}$ channel subtypes such as ${\alpha}_{1C}$(L)-, ${\alpha}_{1B}$(N)-, ${\alpha}_{1A}$(P/Q)-, ${\alpha}_{1E}$(R)- and ${\alpha}_{1G}$(T) have not been identified. Here, we used the two-microelectrode voltage clamp technique to characterize the effects of ginsenosides and ginsenoside metabolites on $Ba^{2+}$ currents ($I_{Ba}$) in Xenopus oocytes expressing five different $Ca^{2+}$ channel subtypes. Exposure to ginseng total saponins (GTS) induced voltage-dependent, dose-dependent and reversible inhibition of the five channel subtypes, with particularly strong inhibition of the ${\alpha}_{1G}$-type. Of the various ginsenosides, $Rb_1$, Rc, Re, Rf, $Rg_1$, $Rg_3$, and $Rh_2$, ginsenoside $Rg_3$ also inhibited all five channel subtypes and ginsenoside $Rh_2$ had most effect on the ${\alpha}_{1C}$- and ${\alpha}_{1E}$-type $Ca^{2+}$ channels. Compound K (CK), a protopanaxadiol ginsenoside metabolite, strongly inhibited only the ${\alpha}_{1G}$-type of $Ca^{2+}$ channel, whereas M4, a protopanaxatriol ginsenoside metabolite, had almost no effect on any of the channels. $Rg_3$, $Rh_2$, and CK shifted the steady-state activation curves but not the inactivation curves in the depolarizing direction in the ${\alpha}_{1B}$- and ${\alpha}_{1A}$-types. These results reveal that $Rg_3$, $Rh_2$ and CK are the major inhibitors of $Ca^{2+}$ channels in Panax ginseng, and that they show some $Ca^{2+}$ channel selectivity.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.141-154
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• 2019

Rapid growth of internet technology and social media is progressing. Data mining technology has evolved to enable unstructured document representations in a variety of applications. Sentiment analysis is an important technology that can distinguish poor or high-quality content through text data of products, and it has proliferated during text mining. Sentiment analysis mainly analyzes people's opinions in text data by assigning predefined data categories as positive and negative. This has been studied in various directions in terms of accuracy from simple rule-based to dictionary-based approaches using predefined labels. In fact, sentiment analysis is one of the most active researches in natural language processing and is widely studied in text mining. When real online reviews aren't available for others, it's not only easy to openly collect information, but it also affects your business. In marketing, real-world information from customers is gathered on websites, not surveys. Depending on whether the website's posts are positive or negative, the customer response is reflected in the sales and tries to identify the information. However, many reviews on a website are not always good, and difficult to identify. The earlier studies in this research area used the reviews data of the Amazon.com shopping mal, but the research data used in the recent studies uses the data for stock market trends, blogs, news articles, weather forecasts, IMDB, and facebook etc. However, the lack of accuracy is recognized because sentiment calculations are changed according to the subject, paragraph, sentiment lexicon direction, and sentence strength. This study aims to classify the polarity analysis of sentiment analysis into positive and negative categories and increase the prediction accuracy of the polarity analysis using the pretrained IMDB review data set. First, the text classification algorithm related to sentiment analysis adopts the popular machine learning algorithms such as NB (naive bayes), SVM (support vector machines), XGboost, RF (random forests), and Gradient Boost as comparative models. Second, deep learning has demonstrated discriminative features that can extract complex features of data. Representative algorithms are CNN (convolution neural networks), RNN (recurrent neural networks), LSTM (long-short term memory). CNN can be used similarly to BoW when processing a sentence in vector format, but does not consider sequential data attributes. RNN can handle well in order because it takes into account the time information of the data, but there is a long-term dependency on memory. To solve the problem of long-term dependence, LSTM is used. For the comparison, CNN and LSTM were chosen as simple deep learning models. In addition to classical machine learning algorithms, CNN, LSTM, and the integrated models were analyzed. Although there are many parameters for the algorithms, we examined the relationship between numerical value and precision to find the optimal combination. And, we tried to figure out how the models work well for sentiment analysis and how these models work. This study proposes integrated CNN and LSTM algorithms to extract the positive and negative features of text analysis. The reasons for mixing these two algorithms are as follows. CNN can extract features for the classification automatically by applying convolution layer and massively parallel processing. LSTM is not capable of highly parallel processing. Like faucets, the LSTM has input, output, and forget gates that can be moved and controlled at a desired time. These gates have the advantage of placing memory blocks on hidden nodes. The memory block of the LSTM may not store all the data, but it can solve the CNN's long-term dependency problem. Furthermore, when LSTM is used in CNN's pooling layer, it has an end-to-end structure, so that spatial and temporal features can be designed simultaneously. In combination with CNN-LSTM, 90.33% accuracy was measured. This is slower than CNN, but faster than LSTM. The presented model was more accurate than other models. In addition, each word embedding layer can be improved when training the kernel step by step. CNN-LSTM can improve the weakness of each model, and there is an advantage of improving the learning by layer using the end-to-end structure of LSTM. Based on these reasons, this study tries to enhance the classification accuracy of movie reviews using the integrated CNN-LSTM model.