Programmable Multilevel Memtransistors Based on van der Waals Heterostructures
- Authors
- Park, Hyunik; Mastro, Michael A.; Tadjer, Marko J.; Kim, Jihyun
- Issue Date
- 10월-2019
- Publisher
- WILEY
- Keywords
- 2D materials; heterostructures; in-memory computing; memristors; memtransistors; neuromorphic
- Citation
- ADVANCED ELECTRONIC MATERIALS, v.5, no.10
- Indexed
- SCIE
SCOPUS
- Journal Title
- ADVANCED ELECTRONIC MATERIALS
- Volume
- 5
- Number
- 10
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/62661
- DOI
- 10.1002/aelm.201900333
- ISSN
- 2199-160X
- Abstract
- Neuromorphic computing that mimics the energy-efficient cortical neural network in the human brain is attractive because of its possibility to process complex and massive data sets and achieve fast computing capability. Herein, a heterosynaptic and programmable memtransistor architecture with high computing functionality is reported by monolithically integrating a hexagonal boron nitride (h-BN) memristor with a molybdenum disulfide (MoS2) transistor. Memristors consisting of a vertically stacked van der Waals materials (multilayer graphene (MLG) and h-BN) exhibit a stable bipolar resistive switching behavior with a memory window more than three orders of magnitude due to the formation and rupture of the metallic filament within the h-BN layer. By controlling the resistance state of the h-BN memristor, the behaviors of the memtransistor can be programmed with a high switching ratio of approximate to 10(4), showing approximate to 16 pW standby power consumption. A multistate computing window and tunable current on/off ratio can be achieved by controlling the synaptic weight of the memristor, demonstrating that the presented 2D architecture can be exploited as a logic inverter device. The results pave the way toward the development of highly functional neuromorphic systems for the next-generation in-memory computing.
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Collections - College of Engineering > Department of Chemical and Biological Engineering > 1. Journal Articles
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