Diverse long-term potentiation and depression based on multilevel LiSiOxmemristor for neuromorphic computing

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

doi:10.1088/1361-6528/acf0c8

Diverse long-term potentiation and depression based on multilevel LiSiO_xmemristor for neuromorphic computing

Nanotechnology. 2023 Sep 4;34(47). doi: 10.1088/1361-6528/acf0c8.

Authors

Zeyu Wu¹, Zewen Li¹, Xin Lin¹, Xin Shan¹, Gang Chen¹, Chen Yang¹, Xuanyu Zhao², Zheng Sun¹, Kai Hu¹, Fang Wang¹, Tianling Ren³, Zhitang Song⁴, Kailiang Zhang¹

Affiliations

¹ School of Integrated Circuit Science and Engineering, Tianjin Key Laboratory of Film Electronic and Communication Devices, Tianjin University of Technology, Tianjin 300384, People's Republic of China.
² School of Microelectronics, Fudan University, Shanghai 200433, People's Republic of China.
³ Beijing National Research Center for Information Science and Technology, Institute of Microelectronics, Tsinghua University, Beijing 100084, People's Republic of China.
⁴ Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, People's Republic of China.

PMID: 37586343
DOI: 10.1088/1361-6528/acf0c8

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_xmemristor, 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.

Keywords: LiSiO x; memristor; neuromorphic computing; pattern recognition; synaptic simulation.