Highly Reproducible Heterosynaptic Plasticity Enabled by MoS2/ZrO2- Heterostructure Memtransistor
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Updated: Aug 22, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Hye Yeon Jang1,2, Ojun Kwon1,2, Jae Hyeon Nam1,2
1Department of Advanced Material Engineering, Chungbuk National University, Chungdae-ro 1, Seowon-Gu, Cheongju, Chungbuk 28644, Republic of Korea.
Abstract:
Electrically tunable resistive switching of a polycrystalline MoS2-based memtransistor has attracted a great deal of attention as an essential synaptic component of neuromorphic circuitry because its switching characteristics from the field-induced migration of sulfur defects in the MoS2 grain boundaries can realize multilevel conductance tunability and heterosynaptic functionality. However, reproducible switching properties in the memtransistor are usually disturbed by the considerable difficulty in controlling the concentration and distribution of the intrinsically existing sulfur defects. Herein, we demonstrate reliable heterosynaptic characteristics using a memtransistor device with a MoS2/ZrO2- heterostructure. Compared to the control device with the MoS2 semiconducting channel, the Schottky barrier height was more effectively modulated by the insertion of the insulating ZrO2- layer below the MoS2, confirmed by an ultraviolet photoelectron spectroscopy analysis and the corresponding energy-band structures. The MoS2/ZrO2- memtransistor accomplishes dual-terminal (drain and gate electrode) stimulated multilevel conductance owing to the tunable resistive switching behavior under varying gate voltages. Furthermore, the memtransistor exhibits long-term potentiation/depression endurance cycling over 7000 pulses and stable pulse cycling behavior by the pulse stimulus from different terminal regions. The promising candidate as an essential synaptic component of the MoS2/ZrO2- memtransistors for neuromorphic systems results from the high recognition accuracy (∼92%) of the deep neural network simulation test, based on the training and inference of handwritten numbers (0-9). The simple memtransistor structure facilitates the implementation of complex neural circuitry.
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