Multitasking Memristor for High Performance and Ultralow Power Artificial Synaptic Device Application

作者：Ni Yang, Ji Zhang, Jing‐Kai Huang, Yang Liu, Junjie Shi, Qianli Si, Jack Yang, Sean Li · 发表于：ACS Applied Electronic Materials · 年份：2022 · DOI：10.1021/acsaelm.2c00663 · 被引用次数：30 · 研究领域：Advanced Memory and Neural Computing、Neural Networks and Reservoir Computing、Neural dynamics and brain function

The emergence of in-memory computing has shed light on solving high-power consumption and low computation efficiency of the traditional computers built with von Neumann architecture. Memristor, which exhibits history-dependent conductivity modulation, can simulate the synaptic behaviors in the biological brain. However, it remains as a key challenge to fabricate devices that can demonstrate a wide range of synaptic plasticity and maintain stable switching responses over repetitive operating cycles. Hereby, the memristor made of Au/Ti/TiO 2 /Nb:SrTiO 3 (Nb:STO) heterojunction shows a partial nonvolatile bipolar resistive switching behavior with an initial high on/off switching ratio of ∼10 4, and “writing” and “erasing” with long endurance across 1.5 × 10 4 cycles. Furthermore, we experimentally developed a single device that possesses a 5-bits (32-states) reservoir computing system to recognize the binary patterns. We also demonstrated the multidata storage for neuromorphic computing in a 10 × 10 neuromorphic array to recognize the patterns of multilevel resistance states with an ultralow operation power of 4.1 pJ. In addition, various synaptic dependent plasticity performances, including spike-duration, -interval, and -number dependent plasticity, have been realized. Such an on-demand neuromorphic device exhibits a multitask shifting potential for analog bipolar memory and bistate and multistate neuromorphic networks and paves a way to develop the highly efficient memristor ...