Grain-Boundary Engineering of Monolayer MoS2 for Energy-Efficient Lateral Synaptic Devices
© 2021 Wiley-VCH GmbH.
| Veröffentlicht in: | Advanced materials (Deerfield Beach, Fla.). - 1998. - 33(2021), 32 vom: 30. Aug., Seite e2102435 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , , , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | Advanced materials (Deerfield Beach, Fla.) |
| Schlagworte: | Journal Article artificial synapses grain-boundary engineering low energy consumption monolayer molybdenum disulfide switching mechanisms Disulfides Molybdenum 81AH48963U molybdenum disulfide |
| Zusammenfassung: | © 2021 Wiley-VCH GmbH. Synaptic devices based on 2D-layered materials have emerged as high-efficiency electronic synapses and neurons for neuromorphic computing. Lateral 2D synaptic devices have the advantages of multiple functionalities by responding to diverse stimuli, but they consume large amounts of energy, far more than the human brain. Moreover, current lateral devices employ several mechanisms based on conductive filaments and grain boundaries (GBs), but their formation is random and difficult to control, also hindering their practical applications. Here, four-terminal, lateral synaptic devices with artificially engineered GBs are reported, which are made from monolayer MoS2 . With lithography-free, direct-laser-writing-controlled MoS2 /MoS2- x Oδ GBs, such synaptic devices exhibit short-term and long-term plasticity characteristics that are responsive to electric and light stimulation simultaneously. This enables detailed simulations of biological learning and cognitive processes as well as image perception and processing. In particular, the device exhibits low energy consumption, similar to that of the human brain and much lower than those of other lateral 2D synaptic devices. This work provides an effective way to fabricate lateral synaptic devices for practical application development and sheds light on controllable electrical state switching for neuromorphic computing |
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| Beschreibung: | Date Completed 19.01.2022 Date Revised 19.01.2022 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1521-4095 |
| DOI: | 10.1002/adma.202102435 |