Material design strategies for emulating neuromorphic functionalities with resistive switching memories

Publikation: Beitrag in FachzeitschriftÜbersichtsartikel (Review)BeigetragenBegutachtung

Beitragende

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

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

OriginalspracheEnglisch
AufsatznummerSM0806
Seitenumfang12
FachzeitschriftJapanese journal of applied physics
Jahrgang61
AusgabenummerSM
PublikationsstatusVeröffentlicht - 1 Okt. 2022
Peer-Review-StatusJa
Extern publiziertJa

Externe IDs

WOS 000842175200001

Schlagworte

Schlagwörter

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

Bibliotheksschlagworte