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Title: Two-Terminal MoS 2 Memristor and the Homogeneous Integration with a MoS 2 Transistor for Neural Networks
Memristors are promising candidates for constructing neural networks. However, their dissimilar working mechanism to that of the addressing transistors can result in a scaling mismatch, which may hinder efficient integration. Here, we demonstrate two-terminal MoS2 memristors that work with a charge-based mechanism similar to that in transistors, which enables the homogeneous integration with MoS2 transistors to realize one-transistor-one-memristor addressable cells for assembling programmable network. The homogenously integrated cells are implemented in a 2×2 network array to demonstrate the enabled addressability and programmability. The potential for assembling scalable network is evaluated in a simulated neural network using obtained realistic device parameters, which achieves over 91% pattern recognition accuracy. This study also reveals a generic mechanism and strategy that can be applied to other semiconducting devices for the engineering and homogeneous integration of memristive systems.  more » « less
Award ID(s):
2027102 1844904
NSF-PAR ID:
10477370
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
ACS publications
Date Published:
Journal Name:
Nano Letters
Volume:
23
Issue:
13
ISSN:
1530-6984
Page Range / eLocation ID:
5869 to 5876
Subject(s) / Keyword(s):
["memristor","neural network","MoS2","2D materials","transistor"]
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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