Enhanced Switching Properties in TaOx Memristors Using Diffusion Limiting Layer for Synaptic Learning

Pei Yu Jung, Debashis Panda, Sridhar Chandrasekaran, Sailesh Rajasekaran, Tseung Yuen Tseng*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Scopus citations


To move towards a new generation powerful computing system, brain-inspired neuromorphic computing is expected to transform the architecture of the conventional computer, where memristors are considered to be potential solutions for synapses part. We propose and demonstrate a novel approach to achieve remarkable improvement of analog switching linearity in TaN/Ta/TaOx/Al2O3/Pt/Si memristors by varying Al2O3 layer thickness. Presence of the Al2O3 layer is confirmed from the Auger Electron Spectroscopy study. Good analog switching ratio of about 100× and superior switching uniformity are observed for the 1 nm Al2O3 based device. Multilevel capability of the memristive devices is also explored for prospective use as a synapse. More than 104 and 4×104 cycles nondegradable dc and ac endurances, respectively, alongwith 104 second retention are achieved for the optimized device. Improved linearities of 2.41 and -2.77 for potentiation and depression, respectively are obtained for such 1 nm Al2O3-based devices. The property of gradual resistance changed by pulse amplitudes confirms that the TaOx memristors can be potentially used as an electronic synapse.

Original languageEnglish
Article number8960312
Pages (from-to)110-115
Number of pages6
JournalIEEE Journal of the Electron Devices Society
Issue number1
StatePublished - 15 Jan 2020


  • Memristors
  • neuromorphic computing
  • synapse


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