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Dual‐Gated MoS 2 Memtransistor Crossbar Array
Author(s) -
Lee HongSub,
Sangwan Vinod K.,
Rojas William A. Gaviria,
Bergeron Hadallia,
Jeong Hye Yun,
Yuan Jiangtan,
Su Katherine,
Hersam Mark C.
Publication year - 2020
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.202003683
Subject(s) - neuromorphic engineering , crossbar switch , memristor , synaptic weight , materials science , transistor , hebbian theory , computer science , gating , electronic circuit , artificial neural network , electronic engineering , optoelectronics , artificial intelligence , voltage , electrical engineering , physiology , telecommunications , engineering , biology
Memristive systems offer biomimetic functions that are being actively explored for energy‐efficient neuromorphic circuits. In addition to providing ultimate geometric scaling limits, 2D semiconductors enable unique gate‐tunable responses including the recent realization of hybrid memristor and transistor devices known as memtransistors. In particular, monolayer MoS 2 memtransistors exhibit nonvolatile memristive switching where the resistance of each state is modulated by a gate terminal. Here, further control over the memtransistor neuromorphic response through the introduction of a second gate terminal is gained. The resulting dual‐gated memtransistors allow tunability over the learning rate for non‐Hebbian training where the long‐term potentiation and depression synaptic behavior is dictated by gate biases during the reading and writing processes. Furthermore, the electrostatic control provided by dual gates provides a compact solution to the sneak current problem in traditional memristor crossbar arrays. In this manner, dual gating facilitates the full utilization and integration of memtransistor functionality in highly scaled crossbar circuits. Furthermore, the tunability of long‐term potentiation yields improved linearity and symmetry of weight update rules that are utilized in simulated artificial neural networks to achieve a 94% recognition rate of hand‐written digits.