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Reconfigurable Resistive Switching in VO2/La0.7Sr0.3MnO3/Al2O3 (0001) Memristive Devices for Neuromorphic Computing

  • Sundar Kunwar
  • , Nicholas Cucciniello
  • , Alessandro R. Mazza
  • , Di Zhang
  • , Luis Santillan
  • , Ben Freiman
  • , Pinku Roy
  • , Quanxi Jia
  • , Judith L. MacManus-Driscoll
  • , Haiyan Wang
  • , Wanyi Nie
  • , Aiping Chen
  • United States Department of Energy
  • SUNY Buffalo
  • Los Alamos National Laboratory Materials Science and Technology Division
  • University of Cambridge
  • Purdue University

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

The coexistence of nonvolatile and volatile switching modes in a single memristive device provides flexibility to emulate both neuronal and synaptic functions in the brain. Furthermore, such a device structure may eliminate the need for additional circuit elements such as transistor-based selectors, enabling low-power consumption and high-density device integration in fully memristive spiking neural networks. In this work, we report dual resistive switching (RS) modes in VO2/La0.7Sr0.3MnO3 (LSMO) bilayer memristive devices. Specifically, the nonvolatile RS is driven by the movement of oxygen vacancies (Vo) at the VO2/LSMO interface and requires a higher biasing voltage, whereas the volatile RS is controlled by the metal-insulator transition (MIT) of VO2 under a lower biasing voltage. The simple device structure is electrically driven between the two RS modes and thus can operate as a one selector-one resistor (1S1R) cell, which is a desirable feature in memristive crossbar arrays to avoid the sneak-path current issue. The RS modes are found to be stable and repeatable and can be reconfigured by exploiting the interfacial and phase transition properties, and thus, they hold great promise for applications in memristive neural networks and neuromorphic computing.

Original languageEnglish
Pages (from-to)19103-19111
Number of pages9
JournalACS Applied Materials and Interfaces
Volume16
Issue number15
DOIs
StatePublished - Apr 17 2024

Keywords

  • VO/LSMO heterostructures
  • dual resistive switching
  • metal-insulator phase transition
  • neuromorphic computing
  • reconfigurable memristor

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