• Electrical & Electronic Materials
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• v.8 no.6
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• pp.708-714
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• 1995

We have discussed the buried-channel(BC) behavior through the subthreshold characteristics of submicron PMOSFET device fabricated with twin well CMOS process. In this paper, we have guessed the initial conditions of ion implantation using process simulation, obtained the subthreshold characteristics as a function of process parameter variation such as threshold adjusting ion implant dose($D_c$), channel length(L), gate oxide thickness($T_ox$) and junction depth of source/drain($X_j$) using device simulation. The buried channel behavior with these process prarameter variation were showed apparent difference. Also, the fabricated pMOSFET device having different channel length represented good S.S value and low leakage current with increasing drain voltage.

• Journal of Electrical Engineering and Technology
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• v.2 no.2
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• pp.267-273
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• 2007

A depletion-mode MOSFET has been analyzed to evaluate its electrical behavior using a novel 3-D numerical simulation package. The characterizing analysis of the BC MOSFET was performed through short-channel narrow-channel and small-geometry effects that are investigated, in detail, in terms of the threshold voltage. The DIBL effect becomes significant for a short-channel device with a channel length of $<\;3({\mu}m)$. For narrow-channel devices the variation of the threshold voltage was sharp for $<4({\mu}m)$ due to the strong narrow-channel effect. In the case of small-geometry devices, the shift of the threshold voltage was less sensitive due to the combination of the DIBL and substrate bias effects, as compared with that observed from the short-channel and narrow-channel devices. The characterizing analysis of the narrow-channel and small-geometry devices, especially with channel width of $<\;4({\mu}m)$ and channel area of $<\;4{\times}4({\mu}m^2)$ respectively, can be accurately performed only from a 3-D numerical simulation due to their sharp variations in threshold voltages.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.