Material design strategies for emulating neuromorphic functionalities with resistive switching memories

Research output: Contribution to journalReview articleContributedpeer-review

Contributors

  • Panagiotis Bousoulas - , National Technical University of Athens (Author)
  • Stavros Kitsios - , National Technical University of Athens (Author)
  • Theodoros Panagiotis Chatzinikolaou - , Democritus University of Thrace (Author)
  • Iosif Angelos Fyrigos - , Democritus University of Thrace (Author)
  • Vasileios Ntinas - , Democritus University of Thrace (Author)
  • Michail Antisthenis Tsompanas - , Democritus University of Thrace (Author)
  • Georgios Ch Sirakoulis - , Democritus University of Thrace (Author)
  • Dimitris Tsoukalas - , National Technical University of Athens (Author)

Abstract

Nowadays, the huge power consumption and the inability of the conventional circuits to deal with real-time classification tasks have necessitated the devising of new electronic devices with inherent neuromorphic functionalities. Resistive switching memories arise as an ideal candidate due to their low footprint and small leakage current dissipation, while their intrinsic randomness is smoothly leveraged for implementing neuromorphic functionalities. In this review, valence change memories or conductive bridge memories for emulating neuromorphic characteristics are demonstrated. Moreover, the impact of the device structure and the incorporation of Pt nanoparticles is thoroughly investigated. Interestingly, our devices possess the ability to emulate various artificial synaptic functionalities, including paired-pulsed facilitation and paired-pulse depression, long-term plasticity and four different types of spike-dependent plasticity. Our approach provides valuable insights from a material design point of view towards the development of multifunctional synaptic elements that operate with low power consumption and exhibit biological-like behavior.

Details

Original languageEnglish
Article numberSM0806
Number of pages12
JournalJapanese journal of applied physics
Volume61
Issue numberSM
Publication statusPublished - 1 Oct 2022
Peer-reviewedYes
Externally publishedYes

External IDs

WOS 000842175200001
ORCID /0000-0002-2367-5567/work/168720250

Keywords

Keywords

  • Artificial Synaptic Functionalities, Conductive Bridge Memories (CBRAM), Neuromorphic Computing, Resistive random access memories (RRAMs), Resistive Switching Memories, Spike-Dependent Plasticity, Valence Change Memories (VCM)