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Cited 18 time in webofscience Cited 19 time in scopus
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Synaptic plasticity features and neuromorphic system simulation in AlN-based memristor devicesopen access

Authors
Kwon, OsungLee, YewonKang, MyounggonKim, Sungjun
Issue Date
Aug-2022
Publisher
Elsevier BV
Keywords
Neuromorphic system; Memristor; AlN; MNIST
Citation
Journal of Alloys and Compounds, v.911, pp 1 - 7
Pages
7
Indexed
SCIE
SCOPUS
Journal Title
Journal of Alloys and Compounds
Volume
911
Start Page
1
End Page
7
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/2689
DOI
10.1016/j.jallcom.2022.164870
ISSN
0925-8388
1873-4669
Abstract
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.(c) 2022 Elsevier B.V. All rights reserved.
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