A self-rectifying TaOy/nanoporous TaOx memristor synaptic array for learning and energy-efficient neuromorphic systems

Sanghyeon Choi, Seonghoon Jang, Jung Hwan Moon, Jong Chan Kim, Hu Young Jeong, Peonghwa Jang, Kyoung Jin Lee, Gunuk Wang

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


The human brain intrinsically operates with a large number of synapses, more than 1015. Therefore, one of the most critical requirements for constructing artificial neural networks (ANNs) is to achieve extremely dense synaptic array devices, for which the crossbar architecture containing an artificial synaptic node at each cross is indispensable. However, crossbar arrays suffer from the undesired leakage of signals through neighboring cells, which is a major challenge for implementing ANNs. In this work, we show that this challenge can be overcome by using Pt/TaOy/nanoporous (NP) TaOx/Ta memristor synapses because of their self-rectifying behavior, which is capable of suppressing unwanted leakage pathways. Moreover, our synaptic device exhibits high non-linearity (up to 104), low synapse coupling (S.C, up to 4.00 × 10−5), acceptable endurance (5000 cycles at 85 °C), sweeping (1000 sweeps), retention stability and acceptable cell uniformity. We also demonstrated essential synaptic functions, such as long-term potentiation (LTP), long-term depression (LTD), and spiking-timing-dependent plasticity (STDP), and simulated the recognition accuracy depending on the S.C for MNIST handwritten digit images. Based on the average S.C (1.60 × 10−4) in the fabricated crossbar array, we confirmed that our memristive synapse was able to achieve an 89.08% recognition accuracy after only 15 training epochs.

Original languageEnglish
Pages (from-to)1097-1106
Number of pages10
JournalNPG Asia Materials
Issue number12
Publication statusPublished - 2018 Dec 1

ASJC Scopus subject areas

  • Modelling and Simulation
  • Materials Science(all)
  • Condensed Matter Physics


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