Directional Formation of Conductive Filaments for a Reliable Organic-Based Artificial Synapse by Doping Molybdenum Disulfide Quantum Dots into a Polymer Matrix
- Authors
- Li, Mingjun; An, Haoqun; Kim, Youngjin; An, Jun Seop; Li, Ming; Kim, 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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