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.
|Journal||Applied Surface Science|
|Publication status||Published - 2021 May 1|
Bibliographical noteFunding Information:
This research was supported by the Nano Material Technology Development Programs and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of science, ICT & Future Planning (NRF-2019R1F1A1057243, NRF-2020M3F3A2A02082449), respectively, as well as the Future Semiconductor Device Technology Development Program (10080689, 20003808, 20004399) funded by MOTIE (Ministry of Trade, Industry & Energy) and KSRC (Korean Semiconductor Research Consortium).
© 2021 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Condensed Matter Physics
- Physics and Astronomy(all)
- Surfaces and Interfaces
- Surfaces, Coatings and Films