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Matveyev Y, Kirtaev R, Fetisova A, et al. Crossbar Nanoscale HfO2-Based Electronic Synapses. Nanoscale Res Lett. 2016;11(1):147doi: 10.1186/s11671-016-1360-6.
Matveyev, Y., Kirtaev, R., Fetisova, A., Zakharchenko, S., Negrov, D., & Zenkevich, A. (2016). Crossbar Nanoscale HfO2-Based Electronic Synapses. Nanoscale research letters, 11(1), 147. https://doi.org/10.1186/s11671-016-1360-6
Matveyev, Yury, et al. "Crossbar Nanoscale HfO2-Based Electronic Synapses." Nanoscale research letters vol. 11,1 (2016): 147. doi: https://doi.org/10.1186/s11671-016-1360-6
Matveyev Y, Kirtaev R, Fetisova A, Zakharchenko S, Negrov D, Zenkevich A. Crossbar Nanoscale HfO2-Based Electronic Synapses. Nanoscale Res Lett. 2016 Dec;11(1):147. doi: 10.1186/s11671-016-1360-6. Epub 2016 Mar 15. PMID: 26979725; PMCID: PMC4792835.
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Nanoscale Res Lett. 2016 Dec;11(1):147. doi: 10.1186/s11671-016-1360-6. Epub 2016 Mar 15.

Crossbar Nanoscale HfO2-Based Electronic Synapses.

Nanoscale research letters

Yury Matveyev, Roman Kirtaev, Alena Fetisova, Sergey Zakharchenko, Dmitry Negrov, Andrey Zenkevich

Affiliations

  1. Moscow Institute of Physics and Technology, Dolgoprudny, 141700, Russia. [email protected].
  2. Moscow Institute of Physics and Technology, Dolgoprudny, 141700, Russia.

PMID: 26979725 PMCID: PMC4792835 DOI: 10.1186/s11671-016-1360-6

Abstract

Crossbar resistive switching devices down to 40 × 40 nm(2) in size comprising 3-nm-thick HfO2 layers are forming-free and exhibit up to 10(5) switching cycles. Four-nanometer-thick devices display the ability of gradual switching in both directions, thus emulating long-term potentiation/depression properties akin to biological synapses. Both forming-free and gradual switching properties are modeled in terms of oxygen vacancy generation in an ultrathin HfO2 layer. By applying the voltage pulses to the opposite electrodes of nanodevices with the shape emulating spikes in biological neurons, spike-timing-dependent plasticity functionality is demonstrated. Thus, the fabricated memristors in crossbar geometry are promising candidates for hardware implementation of hybrid CMOS-neuron/memristor-synapse neural networks.

Keywords: Crossbar; Electronic synapse; HfO2; Memristor; Resistive switching; STDP

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