Diverse long-term potentiation and depression based on multilevel LiSiO x memristor for neuromorphic computing

Author:

Wu Zeyu,Li Zewen,Lin Xin,Shan Xin,Chen Gang,Yang Chen,Zhao Xuanyu,Sun Zheng,Hu Kai,Wang Fang,Ren Tianling,Song Zhitang,Zhang Kailiang

Abstract

Abstract Memristor-based neuromorphic computing is expected to overcome the bottleneck of von Neumann architecture. An artificial synaptic device with continuous conductance variation is essential for implementing bioinspired neuromorphic systems. In this work, a memristor based on Pt/LiSiO x /TiN structure is developed to emulate an artificial synapse, which shows non-volatile multilevel resistance state memory behavior. Moreover, the high nonlinearity caused by abrupt changes in the set process is optimized by adjusting the initial resistance. 100 levels of continuously modulated conductance states are achieved and the nonlinearity factors are reduced to 1.31. The significant improvement is attributed to the decrease in the Schottky barrier height and the evolution of the conductive filaments. Finally, due to the improved linearity of the long-term potentiation/long-term depression behaviors in LiSiO x memristor, a robust recognition rate (∼94.58%) is achieved for pattern recognition with the modified National Institute of Standards and Technology handwriting database. The Pt/LiSiO x /TiN memristor shows significant potential in high-performance multilevel data storage and neuromorphic computing systems.

Funder

National Natural Science Foundation of China

Research and Development Program of China

Natural Science Foundation of Tianjin City

Open Project of State Key Laboratory of Functional Materials for Information

Science and Technology Planning Project of Tianjin City

Publisher

IOP Publishing

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Mechanics of Materials,General Materials Science,General Chemistry,Bioengineering

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