뉴로모픽 시스템 향상을 위한 RRAM 기반 시냅스 소자 리뷰 (A Review of RRAM-based Synaptic Device to Improve Neuromorphic Systems)

서지정보

· 발행기관 : 한국반도체디스플레이기술학회
· 수록지 정보 : 반도체디스플레이기술학회지 / 21권 / 3호 / 50 ~ 56페이지
· 저자명 : 박건우, 김제규, 최건우

초록

In order to process a vast amount of data, there is demand for a new system with higher processing speed and lower energy consumption. To prevent ‘memory wall’ in von Neumann architecture, RRAM, which is a neuromorphic device, has been researched. In this paper, we summarize the features of RRAM and propose the device structure for characteristic improvement. RRAM operates as a synapse device using a change of resistance. In general, the resistance characteristics of RRAM are nonlinear and random. As synapse device, linearity and uniformity improvement of RRAM is important to improve learning recognition rate because high linearity and uniformity characteristics can achieve high recognition rate. There are many method, such as TEL, barrier layer, NC, high oxidation properties, to improve linearity and uniformity. We proposed a new device structure of TiN/Al doped TaO /AlO /Pt that will achieve high recognition rate. Also, with simulation, we prove that the improved properties show a high learning recognition rate.

영어초록

In order to process a vast amount of data, there is demand for a new system with higher processing speed and lower energy consumption. To prevent ‘memory wall’ in von Neumann architecture, RRAM, which is a neuromorphic device, has been researched. In this paper, we summarize the features of RRAM and propose the device structure for characteristic improvement. RRAM operates as a synapse device using a change of resistance. In general, the resistance characteristics of RRAM are nonlinear and random. As synapse device, linearity and uniformity improvement of RRAM is important to improve learning recognition rate because high linearity and uniformity characteristics can achieve high recognition rate. There are many method, such as TEL, barrier layer, NC, high oxidation properties, to improve linearity and uniformity. We proposed a new device structure of TiN/Al doped TaO /AlO /Pt that will achieve high recognition rate. Also, with simulation, we prove that the improved properties show a high learning recognition rate.

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