LiNbO3 dynamic memristors for reservoir computing

Frontiers in Neuroscience

Neuroscience

Shanpeng Xiao¹  Yizheng Li¹  Yuan Li¹  Chen Wang²  Huanglong Li³  Junwei An⁴  Yuanxi Zhao⁵  Wenrui Duan⁵ 

[1] China Mobile Research Institute, Beijing, China;Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, China;Department of Precision Instrument, Center for Brain Inspired Computing Research, Tsinghua University, Beijing, China;Chinese Institute for Brain Research, Beijing, China;School of Chemistry and Chemical Engineering, Jining Normal University, Ulanqab, China;Inner Mongolia Qingmeng Graphene Technology Co., Ltd, Ulanqab, China;School of Instrument Science and Opto Electronics Engineering, Beijing Information Science and Technology University, Beijing, China;

关键词: LiNbO3;    memristors;    volatile memristors;    dynamic memristors;    reservoir computing;   

DOI  :  10.3389/fnins.2023.1177118

 received in 2023-03-01, accepted in 2023-03-24,  发布年份 2023

来源: Frontiers

【 摘 要 】

Information in conventional digital computing platforms is encoded in the steady states of transistors and processed in a quasi-static way. Memristors are a class of emerging devices that naturally embody dynamics through their internal electrophyiscal processes, enabling nonconventional computing paradigms with enhanced capability and energy efficiency, such as reservoir computing. Here, we report on a dynamic memristor based on LiNbO3. The device has nonlinear I-V characteristics and exhibits short-term memory, suitable for application in reservoir computing. By time multiplexing, a single device can serve as a reservoir with rich dynamics which used to require a large number of interconnected nodes. The collective states of five memristors after the application of trains of pulses to the respective memristors are unique for each combination of pulse patterns, which is suitable for sequence data classification, as demonstrated in a 5 × 4 digit image recognition task. This work broadens the spectrum of memristive materials for neuromorphic computing.

【 授权许可】

Unknown   
Copyright © 2023 Zhao, Duan, Wang, Xiao, Li, Li, An and Li.