Compatible resistive switching mechanisms in Ni/SiOx/ITO and application to neuromorphic systemsopen access
- Authors
- Park, Minsu; Park, Jongmin; Kim, 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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Collections - College of Engineering > Department of Electronics and Electrical Engineering > 1. Journal Articles

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