• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.23 no.6
• /
• pp.449-453
• /
• 2010

In this paper, the electrical characteristics of Fin-type SONOS(silicon-oxide-nitride-oxide-silicon) flash memory device with different trapping layers are analyzed in depth. Two kinds of trapping layers i.e., silicon nitride($Si_3N_4$) and hafnium oxide($HfO_2$) are applied. Compared to the conventional Fin-type SONOS device using the $Si_3N_4$ trapping layer, the Fin-type SOHOS(silicon-oxide-high-k-oxide-silicon) device using the $HfO_2$ trapping layer shows superior program/erase speed. However, the data retention properties in SOHOS device are worse than the SONOS flash memory device. Degraded data retention in the SOHOS device may be attributed to the tunneling leakage current induced by interface trap states, which are supported by the subthreshold slope and low frequency noise characteristics.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.14 no.1
• /
• pp.34-39
• /
• 2014

This paper discusses the 3-level charge pumping (CP) method in planar-type Silicon-Oxide-High-k-Oxide-Silicon (SOHOS) and Silicon-Oxide-Nitride-Oxide-Silicon (SONOS) devices to find out the reason of the degradation of data retention properties. In the CP technique, pulses are applied to the gate of the MOSFET which alternately fill the traps with electrons and holes, thereby causing a recombination current Icp to flow in the substrate. The 3-level charge pumping method may be used to determine not only interface trap densities but also capture cross sections as a function of trap energy. By applying this method, SOHOS device found to have a higher interface trap density than SONOS device. Therefore, degradation of data retention characteristics is attributed to the many interface trap sites.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.20 no.1
• /
• pp.101-116
• /
• 2018

Sustainability science is an emerging transdisciplinary research which necessitates not only the communication and collaboration of scientists, practitioners and stakeholders from different disciplines and interests, but also the paradigm shift from deterministic and reductionist approaches to the old basic. Ecological-societal systems (ESS) are co-evolving complex systems having many interacting parts (or agents) whose random interactions at local scale give rise to spontaneous emerging order at global scale (i.e., self-organization). Here, the flows of energy, matter and information between the systems and their surroundings play a key role. We introduce a conceptual framework for such continually morphing dynamical systems, i.e. self-organizing hierarchical open systems (SOHOs). To understand the structure and functionality of SOHOs, we revisit the two fundamental laws of physics. Re-interpretation of these principles helps understand the destiny and better path toward sustainability, and how to reconcile ecosystem integrity with societal vision and value. We then integrate the so-called visioneering (V) framework with that of SOHOs as feedback/feedforward loops so that 'a nudged self-organization' may guide systems' agents to work together toward sustainable ESS. Finally, example is given with newly endorsed Sustainable Development Goals (SDG) Lab (i.e., 'Rural systems visioneering') by Future Earth, which is now underway in rural villages in Tanzania.

• Journal of Intelligence and Information Systems
• /
• v.13 no.2
• /
• pp.15-26
• /
• 2007

In this study, a SOHO (Small Office Home Office) bankruptcy prediction model is proposed using Modified Bagging Predictors which is modification of traditional Bagging Predictors. There have been several studies on bankruptcy prediction for large and middle size companies. However, little studies have been done for SOHOs. In commercial banks, loan approval processes for SOHOs are usually less structured than those for large and middle size companies, and largely depend on partial information such as credit scores. In this study, we use a real SOHO loan approval data set of a Korean bank. First, decision tree induction techniques and artificial neural networks are applied to the data set, and the results are not satisfactory. Bagging Predictors which has been not previously applied for bankruptcy prediction and Modified Bagging Predictors which is proposed in this paper are applied to the data set. The experimental results show that Modified Bagging Predictors provides better performance than decision tree inductions techniques, artificial neural networks, and Bagging Predictors.