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Strained Si/Relaxed SiGe/SiO2/Si 구조 FD n-MOSFET의 전자이동에 Ge mole fraction과 strained Si 층 두께가 미치는 영향
백승혁,심태헌,문준석,차원준,박재근 대한전자공학회 2004 電子工學會論文誌-SD (Semiconductor and devices) Vol.41 No.10
SOI 구조에서 형성된 MOS 트랜지스터의 장점과 strained Si에서 전자의 이동도가 향상되는 효과를 동시에 고려하기 위해 buried oxide(BOX)층과 Top Si층 사이에 Ge을 삽입하여 strained Si/relaxed SiGe/SiO₂Si 구조를 형성하고 strained Si fully depletion(FD) n-MOSFET를 제작하였다. 상부 strained Si층과 하부 SiGe층의 두께의 합을 12.8nm로 고정하고 상부 strained Si 층의 두께에 변화를 주어 두께의 변화가 electron mobility에 미치는 영향을 분석하였다. Strained Si/relaxed SiGe/SiO2/Si (strained Si/SGOI) 구조위의 FD n-MOSFET의 전자 이동도는 Si/SiO₂/Si (SOI) 구조위의 FD n-MOSFET 에 비해 30-80% 항상되었다. 상부 strained Si 층과 하부 SiGe 층의 두께의 합을 12.8nm 로 고정한 shrined Si/SGOI 구조 FD n-MOSFET에서 상부층 strained Si층의 두께가 감소하면 하부층 SiGe 층 두께 증가로 인한 Ge mole fraction이 증가함에 의해 inter-valley scattering 이 감소함에도 불구하고 n-channel 층의 전자이동도가 감소하였다. 이는 strained Si층의 두께가 감소할수록 2-fold valley에 있는 전자가 n-channel 층에 더욱더 confinement 되어 intra-valley phonon scattering 이 증가하여 전자 이동도가 감소함이 이론적으로 확인되었다. In order to enhance the electron mobility in SOI n-MOSFET, we fabricated fully depletion(FD) n-MOSFET on the strained Si/relaxed SiGa/SiO$_2$/Si structure(strained Si/SGOI) formed by inserting SiGe layer between a buried oxide(BOX) layer and a top silicon layer. The summated thickness of the strained Si and relaxed SiGe was fixed by 12.8 nm and then the dependency of electron mobility on strained Si thickness was investigated. The electron mobility in the FD n-MOSFET fabricated on the strained Si/SGOI enhanced about 30-80% compared to the FD n-MOSFET fabricated on conventional SOI. However, the electron mobility decreased with the strained Si thickness although the inter-valley phonon scattering was reduced via the enhancement of the Ge mole fraction. This result is attributed to the increment of intra-valley phonon scattering in the n-channel 2-fold valley via the further electron confinement as the strained Si thickness was reduced.
백승혁,이경석,정승용,박영규,김홍수,이동호,오한진,김병천 대한의학회 2009 Journal of Korean medical science Vol.24 No.-
A systematic and effective welfare system for people with digestive system impairments is required. In Korea, an objective and scientific rating guideline does not exist to judge the digestive system impairments. Whether the impairments exist or not and the degree of it need to be examined. Thus, with these considerations we need a scientific rating guideline for digestive system impairments to fit our cultural and social background. In 2007, a research team, for the development of rating impairment guidelines, was organized under the supervision of Korean Academy of Medical Sciences. The rating guidelines for digestive system impairments was classified into upper and lower gastrointestinal tracts impairments and liver impairment. We developed objective rating guidelines for the upper gastrointestinal tract, the impairment generated after surgery for the stomach, duodenum, esophagus, and for the lower gastrointestinal tract, the impairment generated after construction and surgery for colon, rectum, anus, and intestinal stomas. We tried to make the rating impairment guidelines to include science, objectivity, convenience, rationality, and actuality. We especially emphasized objectivity as the most important value. We worked to make it easy and convenient to use for both the subjects who received the impairment ratings and the doctors who will give the ratings.
계층적 폐색 모델링을 통한 미학습 물체의 아모달 인스턴스 분할
백승혁,이주순,김태원,이규빈 제어로봇시스템학회 2021 제어로봇시스템학회 각 지부별 자료집 Vol.2021 No.12
Instance-aware segmentation of unseen objects is essential for a robotic system in an unstructured environment. Although previous works achieved encouraging results, they were limited to segmenting the only visible regions of unseen objects. For robotic manipulation in a cluttered scene, amodal perception is required to handle the occluded objects behind others. This paper addresses Unseen Object Amodal Instance Segmentation (UOAIS) to detect 1) visible masks, 2) amodal masks, and 3) occlusions on unseen object instances. For this, we propose a Hierarchical Occlusion Modeling (HOM) scheme designed to reason about the occlusion by assigning a hierarchy to the feature fusion and prediction order. We evaluated our method on various benchmarks and achieved state-of-the-art performance. Robot demos for picking up occluded objects, codes, and datasets are available at http://sites.google.com/view/uoais.