Samsung

About: Samsung is a company organization based out in Seoul, South Korea. It is known for research contribution in the topics: Layer (electronics) & Signal. The organization has 134067 authors who have published 163691 publications receiving 2057505 citations. The organization is also known as: Samsung Group & Samsung chaebol.

High performance 5nm radius Twin Silicon Nanowire MOSFET (TSNWFET) : fabrication on bulk si wafer, characteristics, and reliability

Abstract: For the first time, we have successfully fabricated gate-all-around twin silicon nanowire transistor (TSNWFET) on bulk Si wafer using self-aligned damascene-gate process With 10nm diameter nanowire, saturation currents through twin nanowires of 264 mA/mum, 111 mA/mum for n-channel TSNWFET and p-channel TSNWFET are obtained, respectively No roll-off of threshold voltages, ~70 mV/dec of substhreshold swing (SS), and ~20 mV/V of drain induced barrier lowering(DIBL) down to 30 nm gate length are observed for both n-ch and p-ch TSNWFETs

Quantum confinement effect of silicon nanocrystals in situ grown in silicon nitride films

Abstract: Silicon nanocrystals were in situ grown in a silicon nitride film by plasma-enhanced chemical vapor deposition. The size and structure of silicon nanocrystals were confirmed by high-resolution transmission electron microscopy. Depending on the size, the photoluminescence of silicon nanocrystals can be tuned from the near infrared (1.38eV) to the ultraviolet (3.02eV). The fitted photoluminescence peak energy as E(eV)=1.16+11.8∕d2 is evidence for the quantum confinement effect in silicon nanocrystals. The results demonstrate that the band gap of silicon nanocrystals embedded in silicon nitride matrix was more effectively controlled for a wide range of luminescent wavelengths.

Reliability investigations for manufacturable high density PRAM

Abstract: In this paper, PRAM (phase-change memory), exploiting new memory materials called chalcogenides, is introduced. The reliability issues for high-density commercial memory products such as disturbance immunity, endurance, and data retention are addressed and evaluated by using a 64 Mb PRAM with 0.12 /spl mu/m technology. Moreover, observed degradation modes and underlying physical mechanisms are investigated.

Rotating your face using multi-task deep neural network

Abstract: Face recognition under viewpoint and illumination changes is a difficult problem, so many researchers have tried to solve this problem by producing the pose- and illumination- invariant feature. Zhu et al. [26] changed all arbitrary pose and illumination images to the frontal view image to use for the invariant feature. In this scheme, preserving identity while rotating pose image is a crucial issue. This paper proposes a new deep architecture based on a novel type of multitask learning, which can achieve superior performance in rotating to a target-pose face image from an arbitrary pose and illumination image while preserving identity. The target pose can be controlled by the user's intention. This novel type of multi-task model significantly improves identity preservation over the single task model. By using all the synthesized controlled pose images, called Controlled Pose Image (CPI), for the pose-illumination-invariant feature and voting among the multiple face recognition results, we clearly outperform the state-of-the-art algorithms by more than 4∼6% on the MultiPIE dataset.