3월 25일 (화) 개최되는 봄학기 신소재공학과 정기세미나를 아래와 같이 안내해드립니다.

= 아 래 =

1. 일 시 : 2014. 3. 25 (화), 16:00 ~
2. 장 소 : 응용공학동 1층 영상강의실
3. 연 사 : 황 현 상 교수 (포항공과대학교 신소재공학과)
4. 제 목 : ReRAM: Device Technology & Potential Application for Neuromorphic Computing
5. 발표내용요약(Abstract)

  To compete with NAND FLASH technology, we need to develop stackable, cross-point ReRAM device. Although various materials have been reported, it is difficult to meet device criteria such as high speed operation, low power switching, switching uniformity, endurance, long-term retention and selection device for cross-point array. We have investigated various ReRAM devices such as interface switching type (reactive metal/perovskite oxides), filament type (various single layer binary oxides, and bilayer oxides) and PMC type (Cu-doped carbon, Ag-doped oxide). We found that the scaling of device area and film thickness improve device performance. Understanding of switching mechanisms and atomic scale film control are necessary to meet the various requirements for future high density nonvolatile memory devices. To integrate cross-point (4F2) ReRAM device array, we need to develop bi-directional selector device to suppress the sneak current path through the unselected devices. Although various selector candidates were recently reported, several problems such as insufficient current density at set/reset operations for nano-scale devices, low selectivity, and poor endurance have been raised. In this talk, I will report two types of selection devices, called the varistor-type bidirectional switch (VBS) and NbOx based threshold switching device with excellent thermal stability. A highly non-linear VBS showed superior performances including high current density (>3x107A/cm2) and high selectivity (~104). Ultrathin NbO2 exhibits excellent TS characteristics such as high temperature stability (~160oC), good switching uniformity, and extreme scalability. In addition, we also investigated the feasibility of a high speed pattern recognition system using 1k-bit cross-point synaptic ReRAM array. Learning capability of a neuromorphic system comprising ReRAM synapses and CMOS neurons has been confirmed experimentally.