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Directional Formation of Conductive Filaments for a Reliable Organic-Based Artificial Synapse by Doping Molybdenum Disulfide Quantum Dots into a Polymer Matrix

Authors
Li, MingjunAn, HaoqunKim, YoungjinAn, Jun SeopLi, MingKim, Tae Whan
Issue Date
Oct-2022
Publisher
AMER CHEMICAL SOC
Keywords
artificial synaptic devices; silver cluster-type filament; molybdenum disulfide quantum dots; neuromorphic computing; low energy consumption
Citation
ACS APPLIED MATERIALS & INTERFACES, v.14, no.39, pp.44724 - 44734
Indexed
SCIE
SCOPUS
Journal Title
ACS APPLIED MATERIALS & INTERFACES
Volume
14
Number
39
Start Page
44724
End Page
44734
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185813
DOI
10.1021/acsami.2c08337
ISSN
1944-8244
Abstract
The conductive filament (CF) model, as an important means to realize synaptic functions, has received extensive attention and has been the subject of intense research. However, the random and uncontrollable growth of CFs seriously affects the performances of such devices. In this work, we prepared a neural synaptic device based on polyvinyl pyrrolidone-molybdenum disulfide quantum dot (MoS2 QD) nanocomposites. The doping with MoS2 QDs was found to control the growth mode of Ag CFs by providing active centers for the formation of Ag clusters, thus reducing the uncertainty surrounding the growth of Ag CFs. As a result, the device, with a low power consumption of 32.8 pJ/event, could be used to emulate a variety of synaptic functions, including long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation, post-tetanic potentiation, short-term memory to long-term memory conversion, and "learning experience" behavior. After having undergone consecutive stimulation with different numbers of pulses, the device stably realized a "multi-level LTP to LTD conversion" function. Moreover, the synaptic characteristics of the device experienced no degradation due to mechanical stress. Finally, the simulation result based on the synaptic characteristics of our devices achieved a high recognition accuracy of 91.77% in learning and inference tests and showed clear digital classification results.
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