Journal of Alloys and Compounds2022,Vol.9117.DOI:10.1016/j.jallcom.2022.164870

Synaptic plasticity features and neuromorphic system simulation in AlN-based memristor devices

Kwon O. Lee Y. Kim S. Kang M.
Journal of Alloys and Compounds2022,Vol.9117.DOI:10.1016/j.jallcom.2022.164870

Synaptic plasticity features and neuromorphic system simulation in AlN-based memristor devices

Kwon O. 1Lee Y. 1Kim S. 1Kang M.2
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作者信息

  • 1. Division of Electronics and Electrical Engineering Dongguk University
  • 2. Department of Electronics Engineering Korea National University of Transportation
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Abstract

? 2022 Elsevier B.V.In this paper, we show various memory characteristics of the Ag/AlN/TiN devices for neuromorphic systems. We verified the thickness and the components of the device stack by transmission electron microscopy (TEM) and energy-dispersive X-ray spectroscopy (EDS). We investigated the long-term memory (LTM) characteristics, and short-term memory (STM) characteristics can be determined by compliance current (CC). It shows LTM characteristics when CC is high and STM characteristics when CC is low. I-V curves for each characteristic were investigated, and potentiation and depression for LTM characteristics. The switching and conduction mechanisms of Ni/Ag/AlN/TiN devices are studied using the schematic drawing of the conducting filament and the energy band diagram, including the work function, electron affinity, and bandgap energy of each layer. The linearity of potentiation and depression was compared for an identical pulse and an incremental pulse. Finally, we investigated Modified National Institute of Standards and Technology (MNIST) pattern accuracy depending on the linearity of potentiation and depression.

Key words

AlN/Memristor/MNIST/Neuromorphic system

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出版年

2022
Journal of Alloys and Compounds

Journal of Alloys and Compounds

EISCI
ISSN:0925-8388
被引量8
参考文献量39
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