Please use this identifier to cite or link to this item: https://doi.org/10.1038/s41467-022-30519-w
Title: Wafer-scale solution-processed 2D material analog resistive memory array for memory-based computing
Authors: Tang, Baoshan 
Veluri, Hasita
Li, Yida 
Yu, Zhi Gen
Waqar, Moaz
Leong, Jin Feng 
Sivan, Maheswari 
Zamburg, Evgeny 
Zhang, Yong-Wei 
Wang, John 
Thean, Aaron V-Y 
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
MOS2
INTEGRATION
NANOSHEETS
SHEETS
Issue Date: 1-Jun-2022
Publisher: NATURE PORTFOLIO
Citation: Tang, Baoshan, Veluri, Hasita, Li, Yida, Yu, Zhi Gen, Waqar, Moaz, Leong, Jin Feng, Sivan, Maheswari, Zamburg, Evgeny, Zhang, Yong-Wei, Wang, John, Thean, Aaron V-Y (2022-06-01). Wafer-scale solution-processed 2D material analog resistive memory array for memory-based computing. NATURE COMMUNICATIONS 13 (1). ScholarBank@NUS Repository. https://doi.org/10.1038/s41467-022-30519-w
Abstract: Realization of high-density and reliable resistive random access memories based on two-dimensional semiconductors is crucial toward their development in next-generation information storage and neuromorphic computing. Here, wafer-scale integration of solution-processed two-dimensional MoS2 memristor arrays are reported. The MoS2 memristors achieve excellent endurance, long memory retention, low device variations, and high analog on/off ratio with linear conductance update characteristics. The two-dimensional nanosheets appear to enable a unique way to modulate switching characteristics through the inter-flake sulfur vacancies diffusion, which can be controlled by the flake size distribution. Furthermore, the MNIST handwritten digits recognition shows that the MoS2 memristors can operate with a high accuracy of >98.02%, which demonstrates its feasibility for future analog memory applications. Finally, a monolithic three-dimensional memory cube has been demonstrated by stacking the two-dimensional MoS2 layers, paving the way for the implementation of two memristor into high-density neuromorphic computing system.
Source Title: NATURE COMMUNICATIONS
URI: https://scholarbank.nus.edu.sg/handle/10635/238260
ISSN: 2041-1723
DOI: 10.1038/s41467-022-30519-w
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