Uniform resistive switching and highly stable synaptic characteristics of HfOx sandwiched TaOx-based memristor for neuromorphic system

Srikant Kumar Mohanty, Debashis Panda, K. Poshan Kumar Reddy, Po Tsung Lee, Chien Hung Wu*, Kow-Ming Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Emerging nanoscale devices, including memristors, have been extensively studied to implement biological synaptic functions such as learning and plasticity, which are the fundamental building blocks of brain-inspired neuromorphic computing. The memristor with analog switching ability exhibits linear tuning of weight during neural network training is a desirable synaptic device behavior. The importance of inserting a HfOx sandwiched layer in a TaOx/HfOx/TaOx memristor is to achieve analog set/reset operation along with improved spatial/temporal switching uniformity. The optimal resistive switching (RS) behavior can be attributed to asymmetric oxygen vacancy distribution in the stacked structure leading to the formation of an hourglass-shaped conductive filament. Furthermore, confining filament formation/rapture in the narrow fixed region displays superior endurance characteristics (dc cycles >2000 and ac cycles > 106) and uniform resistive switching with the set (reset) voltage variation constrained to 1.8 (2.9) %. Paired-pulse facilitation (PPF), a form of short-term synaptic plasticity is stimulated to replicate bio-synapse behavior. The stable long-term potentiation (LTP) and depression (LTD) behavior for more than 1000 epochs (>105 pulses) with excellent symmetry and linearity is achieved with 50 ns voltage pulse stimulation. The pattern recognition accuracy of 93% was achieved for an image of size 10 × 10 pixels after 13 epochs by deploying 100 synapses in Hopfield Neural Network (HNN) simulation. This comprehensive study demonstrates that the HfOx-inserted TaOx memristor has tremendous potential for application in future neuromorphic computing.

Original languageEnglish
Pages (from-to)16909-16917
Number of pages9
JournalCeramics International
Volume49
Issue number11
DOIs
StatePublished - 1 Jun 2023

Keywords

  • Memristor
  • Metal oxide
  • Neuromorphic computing
  • Oxygen vacancy
  • Resistive switching

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