High-Reliability and Self-Rectifying Alkali Ion Memristor through Bottom Electrode Design and Dopant Incorporation

Byeong Min Lim, Yu Min Lee, Chan Sik Yoo, Minjae Kim, Seung Ju Kim, Sungkyu Kim, J. Joshua Yang, Hong Sub Lee

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Ionic memristor devices are crucial for efficient artificial neural network computations in neuromorphic hardware. They excel in multi-bit implementation but face challenges like device reliability and sneak currents in crossbar array architecture (CAA). Interface-type ionic memristors offer low variation, self-rectification, and no forming process, making them suitable for CAA. However, they suffer from slow weight updates and poor retention and endurance. To address these issues, the study demonstrated an alkali ion self-rectifying memristor with an alkali metal reservoir formed by a bottom electrode design. By adopting Li metal as the adhesion layer of the bottom electrode, an alkali ion reservoir was formed at the bottom of the memristor layer by diffusion occurring during the atomic layer deposition process for the Na:TiO2 memristor layer. In addition, Al dopant was used to improve the retention characteristics by suppressing the diffusion of alkali cations. In the memristor device with optimized Al doping, retention characteristics of more than 20 h at 125 °C, endurance characteristics of more than 5.5 × 105, and high linearity/symmetry of weight update characteristics were achieved. In reliability tests on 100 randomly selected devices from a 32 × 32 CAA device, device-to-device and cycle-to-cycle variations showed low variation values within 81% and 8%, respectively.

Original languageEnglish
Pages (from-to)6373-6386
Number of pages14
JournalACS Nano
Volume18
Issue number8
DOIs
Publication statusPublished - 27 Feb 2024

Bibliographical note

Publisher Copyright:
© 2024 The Authors. Published by American Chemical Society

Keywords

  • adhesion layer
  • alkali cation
  • artificial neural networks
  • reservoir layer
  • synaptic response

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