http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Low-temperature Atomic Layer Deposition of TiO₂, Al₂O₃, and ZnO Thin Films
Taewook Nam,김재민,김민규,Woo-Hee Kim,김형준 한국물리학회 2011 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.59 No.21
We studied low-temperature atomic layer deposition (LT-ALD) of TiO₂, Al₂O₃, and ZnO thin films at temperatures down to room temperature, mainly focusing on the growth characteristics and the film’s properties. Here, two kinds of ALD deposition systems were introduced. Initially,for the thermal ALD (T-ALD) process using a commercial ALD system, a very long purging time of up to 300 s was required to entirely evacuate the remaining H₂O vapors at room temperature due to the large volume and the complicated inner structure of the commercial ALD chamber. For the realization of LT-ALD with a short process time, a plasma-enhanced ALD (PE-ALD) process using O2 plasma was employed, which enabled us to effectively remove the residual reactants at temperatures down to room temperature. As another method, we specifically designed a homemade ALD system with a small volume and a simple inner structure, thereby being able to use T-ALD to synthesize TiO₂, Al₂O₃, and ZnO thin films by using H₂O with very short H₂O purging times even at room temperature, which reveals that the chamber size and design are the critical factors enabling LT-ALD with a short process time. The LT-ALD processes produced highly-pure Al₂O₃,TiO₂, and ZnO films without any C and N impurities by complete elimination of ligands and exhibited excellent conformality in 3-dimensional nanoscale via holes.
Multidimensional Analysis of Consumers' Opinions from Online Product Reviews
Taewook Kim,김동성,Donghyun Kim,김종우 한국경영정보학회 2019 Asia Pacific Journal of Information Systems Vol.29 No.4
Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.