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Versatile memristor for memory and neuromorphic computing

  • Tao Guo
  • , Kangqiang Pan
  • , Yixuan Jiao
  • , Bai Sun
  • , Cheng Du
  • , Joel P. Mills
  • , Zuolong Chen
  • , Xiaoye Zhao
  • , Lan Wei
  • , Y. Norman Zhou
  • , Yimin A. Wu
  • University of Waterloo
  • Southwest Jiaotong University
  • Harbin Institute of Technology

Research output: Contribution to journalArticlepeer-review

107 Scopus citations

Abstract

The memristor is a promising candidate to implement high-density memory and neuromorphic computing. Based on the characteristic retention time, memristors are classified into volatile and non-volatile types. However, a single memristor generally provides a specific function based on electronic performances, which poses roadblocks for further developing novel circuits. Versatile memristors exhibiting both volatile and non-volatile properties can provide multiple functions covering non-volatile memory and neuromorphic computing. In this work, a versatile memristor with volatile/non-volatile bifunctional properties was developed. Non-volatile functionality with a storage window of 4.0 × 105 was obtained. Meanwhile, the device can provide threshold volatile functionalities with a storage window of 7.0 × 104 and a rectification ratio of 4.0 × 104. The leaky integrate-and-fire (LIF) neuron model and artificial synapse based on the device have been studied. Such a versatile memristor enables non-volatile memory, selectors, artificial neurons, and artificial synapses, which will provide advantages regarding circuit simplification, fabrication processes, and manufacturing costs.

Original languageEnglish
Pages (from-to)299-310
Number of pages12
JournalNanoscale Horizons
Volume7
Issue number3
DOIs
StatePublished - Mar 2022
Externally publishedYes

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