Artificial Synaptic Performance with Learning Behavior for Memristor Fabricated with Stacked Solution-Processed Switching Layers

Zongjie Shen, Chun Zhao*, Tianshi Zhao, Wangying Xu, Yina Liu, Yanfei Qi, Ivona Z. Mitrovic, Li Yang, Ce Zhou Zhao

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Citations (Scopus)

Abstract

As one of the promising next-generation electronics, brain-inspired synaptic resistive random access memory (RRAM) devices with stacked solution-processed (SP) spin-coated resistive switching (RS) layers were fabricated in this work. Compared with the RRAM device with a single SP-RS layer (Ag/SP-AlOx/ITO), the device with stacked SP-RS layers (Ag/SP-GaOx/SP-AlOx/ITO) is induced by the metal conductive filament performed with lower power consumption (∼±0.6 V operation voltage), larger read and write capability (∼2 × 104 ON/OFF ratio), and enhanced stability (>2 × 104 s retention time and >1000 endurance cycles). Multiple conductance states with long-term potentiation and depression (200 pulses) were obtained on Ag/SP-GaOx/SP-AlOx/ITO RRAM devices, which resulted in the human brain-like behavior (learning-forgetting-relearning) of a matrix comprising of RRAM devices with SP-GaOx/SP-AlOx layers. Based on the synaptic performance of Ag/SP-GaOx/SP-AlOx/ITO RRAM devices, an image recognition process based on a neuron network was conducted and the average recognition accuracy was close to 90%.

Original languageEnglish
Pages (from-to)1288-1300
Number of pages13
JournalACS Applied Electronic Materials
Volume3
Issue number3
DOIs
Publication statusPublished - 23 Mar 2021

Keywords

  • image recognition
  • resistive switching
  • solution-processed RRAM
  • stacked layers
  • synaptic behavior

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