Surface-Dominated HfO2Nanorod-Based Memristor Exhibiting Highly Linear and Symmetrical Conductance Modulation for High-Precision Neuromorphic Computing

Jae Uk Kwon, Young Geun Song, Ji Eun Kim, Suk Yeop Chun, Gu Hyun Kim, Gichang Noh, Joon Young Kwak, Sunghoon Hur, Chong Yun Kang, Doo Seok Jeong, Soong Ju Oh, Jung Ho Yoon

Research output: Contribution to journalArticlepeer-review

Abstract

The switching characteristics and performance of oxide-based memristors are predominately determined by oxygen-or oxygen-vacancy-mediated redox reactions and the consequent formation of conducting filaments (CFs). Devices using oxide thin films as the switching layer usually require an electroforming process for subsequent switching operations, which induces large device-To-device variations. In addition, the hard-To-control redox reaction during repeated switching causes random fluctuations or degradation of each resistance state, hindering reliable switching operations. In this study, an HfO2nanorod (NR)-based memristor is proposed for simultaneously achieving highly uniform, electroforming-free, fast, and reliable analogue switching properties. The well-controlled redox reaction due to the easy gas exchange with the environment at the surface of the NRs enhances the generation of oxygen or oxygen vacancies during the switching operation, resulting in electroforming-free and reliable switching behavior. In addition, the one-dimensional surface growth of CFs facilitates highly linear conductance modulation with smaller conductance changes compared with the two-dimensional volume growth in thin-film-based memristors, resulting in a high accuracy of >92% in the Modified National Institute of Standards and Technology pattern-recognition test and desirable spike-Timing-dependent plasticity.

Original languageEnglish
Pages (from-to)44550-44560
Number of pages11
JournalACS Applied Materials and Interfaces
Volume14
Issue number39
DOIs
Publication statusPublished - 2022 Oct 5

Keywords

  • Conductance modulation
  • Memristor
  • Nanorod
  • Neuromorphic computing
  • Surface-dominated

ASJC Scopus subject areas

  • Materials Science(all)

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