숙명여자대학교 비교과통합관리시스템

 

Emerging Computing Paradigm beyond Silicon CMOS

The conventional scaling of today’s silicon-CMOS technology cannot satisfy the performance and energy-efficiency needs of abundant-data applications such as deep learning, as they require real-time analytics on enormous quantities of user data. Therefore, research is essential on novel computing hardware to tackle the fundamental limitation of either the conventional semiconducting materials or von Neumann computing architecture. The nano MTJ (nanoscale magnetic tunnel junction) possesses the great potential to advance the emerging computing paradigm due to its excellent scalability, tunability and compatibility, alongside the intrinsic benefits in high-speed and low-power operation originating from the fundamentals of nanomagnetism. In this talk, cutting-edge spintronics research with nano MTJ is introduced as a novel device platform for embedded memory (STT-MRAM) and neuromorphic computing (spintronic synapses and neurons) applications. For STT-MRAM, some pioneering works on improving the manufacturability of nano MTJ as well as understanding the spin-dependent transport in nanomagnets are presented. For neuromorphic computing, a novel experimental approach to the reconfigurable spintronic synaptic element is demonstrated by using the perpendicular MTJ. The other recent research projects of the UTSA Nanoelectronics Laboratory, including the development of all-spin logic based on 2D materials, are also discussed briefly. 

<강사분 소개>

Ethan C. Ahn : 2017-현재 Assistant Professor of Electrical Engineering University of Texas at San Antoni

                       2016        Engineer at Apple, CA USA

                   2015        PhD in Electrical Engineering, Stanford University

                   2007        MS in Electrical Engineering, KAIST

                   2005        BS in Electrical Engineering, KAIST

※ 공개강의로 관심있는 학생들의 많은 참여바랍니다.

※ 간단한 다과가 제공됩니다.