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Cited 13 time in webofscience Cited 14 time in scopus
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Compatible resistive switching mechanisms in Ni/SiOx/ITO and application to neuromorphic systemsopen access

Authors
Park, MinsuPark, JongminKim, Sungjun
Issue Date
May-2022
Publisher
ELSEVIER
Keywords
Neuromorphic system; Memristor; Synaptic device; Resistive switching
Citation
Journal of Alloys and Compounds, v.903, pp 1 - 8
Pages
8
Indexed
SCIE
SCOPUS
Journal Title
Journal of Alloys and Compounds
Volume
903
Start Page
1
End Page
8
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/3160
DOI
10.1016/j.jallcom.2022.163870
ISSN
0925-8388
1873-4669
Abstract
In this work, we studied the switching mechanisms of Ni/SiOx/ITO devices before and after experiencing a reversible switching. And we also investigated its application for neuromorphic computing systems. First, we checked the composition of the device and progressed the electrical measurements. The device had two operating properties that are affected by an external applied voltage, and thus, we divided it into two distinct I-V curves to experimentally investigate the features. Even if they originated from the same RRAM devices, they exhibited different electrical measurements such as a retention, threshold voltages, and the conductance ratio. The I-V curve with an abrupt switching showed a good retention time of 7000 s and a wide conductance ratio of about similar to 41. On the other hand, the other I-V curve that shows progressive operation displayed a low retention time of 5000 s and a narrow conductance ratio of about similar to 3.43. We discussed the different results that were identified on the same device and concluded that it was caused by a change of switching mechanisms induced by a reversible switching in negative polarity. The overshoot current and large fluctuation in threshold voltage were used as evidence for our discussions. After identifying the electrical features, we progressed the biological processes such as potentiation/depression, paired-pulse facilitation (PPF) and spike-timing-dependent plasticity (STDP) to implement the neural networks. (C) 2022 Elsevier B.V. All rights reserved.
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