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Improved switching and synapse characteristics using PEALD SiO2 thin film in Cu/SiO2/ZrO2/Pt device

Authors
Lee, DoheeSokolov, Andrey S.Ku, BoncheolJeon, Yu-RimKim, Ho DuKim, Tae HyeonKim, Hwan GunChoi, Chang hwan
Issue Date
May-2021
Publisher
Elsevier B.V.
Keywords
Plasma enhanced atomic layer deposition; Silicon dioxide; Diffusion limiting layer; Artificial synapse; Neuromorphic engineering
Citation
Applied Surface Science, v.547, pp.1 - 10
Indexed
SCIE
SCOPUS
Journal Title
Applied Surface Science
Volume
547
Start Page
1
End Page
10
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/1476
DOI
10.1016/j.apsusc.2021.149140
ISSN
0169-4332
Abstract
As the demand for data increases rapidly, neuromorphic computing, which can process parallel data and consume low power, is attracting to mimic the human nervous system. Resistive switching memory is considered as a viable element to realize neuromorphic computing due to high power and area efficiency. Although many previous studies have emphasized the linearity of analog switching, which is essential for realizing an ideal artificial synapse, it is still limited to control the diffusion of oxygen vacancies and suppress abrupt switching. Switching mechanism depends on the growth and dissolution of oxygen-deficient filaments, which could cause random variation. In this study, to further improve linear conductance change characteristics, we investigate the role of inserting a diffusion limiting layer in the synapse device operated by the migration of metal cations. Inserting thin SiO2 was prepared by plasma enhanced atomic layer deposition at the Cu/ZrO2 interface. This effectively mitigates the random diffusion of Cu ions and can perform multi-level RESET switching characteristics, which significantly improve the linearity of conductance with the number of pulses. In addition, various synaptic characteristics of potentiation and depression, short-term memory to long-term memory transition, pulse-paired facilitation, and post-tetanic potentiation, spike timing dependent plasticity were successfully emulated.
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