Nociceptor-Enhanced Spike-Timing-Dependent Plasticity in Memristor with Coexistence of Filamentary and Non-Filamentary Switching

Dongyeol Ju, Jungwoo Lee, Sungjun Kim

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In the era of big data, traditional computing architectures face limitations in handling vast amounts of data owing to the separate processing and memory units, thus causing bottlenecks and high-energy consumption. Inspired by the human brain's information exchange mechanism, neuromorphic computing offers a promising solution. Resistive random access memory devices, particularly those with bilayer structures like Pt/TaOx/TiOx/TiN, show potential for neuromorphic computing owing to their simple design, low-power consumption, and compatibility with existing technology. This study investigates the synaptic applications of Pt/TaOx/TiOx/TiN devices for neuromorphic computing. The unique coexistence of nonfilamentary and filamentary switching in the Pt/TaOx/TiOx/TiN device enables the realization of reservoir computing and the functions of artificial nociceptors and synapses. Additionally, the linkage between artificial nociceptors and synapses is examined based on injury-enhanced spike-time-dependent plasticity paradigms. This study underscores the Pt/TaOx/TiOx/TiN device's potential in neuromorphic computing, providing a framework for simulating nociceptors, synapses, and learning principles.

Original languageEnglish
Article number2400440
JournalAdvanced Materials Technologies
Volume9
Issue number19
DOIs
StatePublished - 7 Oct 2024

Keywords

  • artificial synapse
  • memristor
  • nervous system
  • nociceptor
  • reservoir computing

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