Highly Stable Artificial Synapse Consisting of Low-Surface Defect van der Waals and Self-Assembled Materials
- Authors
- Oh Seyong; Jung Sooyoung; Ali Muhammad Hasnain; Kim Jeong-Hoon; Kim Hyeongjun; Park Jin-Hong
- Issue Date
- Aug-2020
- Publisher
- American Chemical Society
- Keywords
- APTES; artificial synapses; neuromorphic computing; pattern recognition; vdW materials
- Citation
- ACS Applied Materials & Interfaces, v.12, no.34, pp 38299 - 38305
- Pages
- 7
- Indexed
- SCIE
SCOPUS
- Journal Title
- ACS Applied Materials & Interfaces
- Volume
- 12
- Number
- 34
- Start Page
- 38299
- End Page
- 38305
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/113739
- DOI
- 10.1021/acsami.0c07394
- ISSN
- 1944-8244
1944-8252
- Abstract
- The long-term plasticity of biological synapses was successfully emulated in an artificial synapse fabricated by combining low-surface defect van der Waals (vdW) and self-assembled (SA) materials. The synaptic operation could be achieved by facilitating hole trapping and releasing only via the amine (NH2) functional groups in 3-aminopropyltriethoxysilane, which consequently induced a gradual conductance change in the WSe2 channel. The vdW-SA synaptic device exhibited extremely stable long-term potentiation/depression (LTP/LTD) characteristics; its dynamic range and nonlinearity reproduced near 100 and 3.13/-6.53 (for LTP/LTD) with relative standard deviations (RSDs) below 2%. Furthermore, after conducting training and recognition tasks for the Modified National Institute of Standard and Technology (MNIST) digit patterns, we verified that the maximum recognition rate was 78.3%, and especially, its RSD was as low as 0.32% over several training/recognition cycles. This study provides a background for future research on advanced artificial synapses based on vdW and organic materials.
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