Artificial synapses with a sponge-like double-layer porous oxide memristor

Qin Gao,Xueli Geng,Qi Hu,Jingjing Zhang,Mei Wang,Jing Zhang,Xueliang Chen,Yuhang Ji,Anping Huang,Zhisong Xiao,Paul K Chu

doi:10.1038/s41427-020-00274-9

Abstract

Closely following the rapid development of artificial intelligence, studies of the human brain and neurobiology are focusing on the biological mechanisms of neurons and synapses. Herein, a memory system employing a nanoporous double-layer structure for simulation of synaptic functions is described. The sponge-like double-layer porous (SLDLP) oxide stack of Pt/porous LiCoO2/porous SiO2/Si is designed as presynaptic and postsynaptic membranes. This bionic structure exhibits high ON–OFF ratios up to 108 during the stability test, and data can be maintained for 105 s despite a small read voltage of 0.5 V. Typical synaptic functions, such as nonlinear transmission characteristics, spike-timing-dependent plasticity, and learning-experience behaviors, are achieved simultaneously with this device. Based on the hydrodynamic transport mechanism of water molecules in porous sponges and the principle of water storage, the synaptic behavior of the device is discussed. The SLDLP oxide memristor is very promising due to its excellent synaptic performance and potential in neuromorphic computing.

Highlights

1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; Introduction The development of deep learning is closely associated with the advancement of artificial intelligence, which is inseparable from brain science and neurobiology
porous LiCoO2 (PLiCoO2) is incorporated into the porous SiO2 (PSiO2) structure by radio frequency (RF) magnetron sputtering to provide conduction channels for ion transport[53,54]
A porous interface is fabricated between the PSiO2 layer and PLiCoO2 layer to provide conduction channels to enhance the electrochemical reactions

Summary

Introduction

The development of deep learning is closely associated with the advancement of artificial intelligence, which is inseparable from brain science and neurobiology. The porous structure in the LiCoO2 layer and SiO2 layer can relieve the volume expansion of LiCoO2 and SiO2 during cycling, and the pores in the LiCoO2 layer provide channels for ion transport and increase the concentration of lithium ions under the action of the same electric field.

Results

Conclusion