Literature DB >> 30192507

MoS2 Memristors Exhibiting Variable Switching Characteristics toward Biorealistic Synaptic Emulation.

Da Li, Bin Wu, Xiaojian Zhu, Juntong Wang, Byunghoon Ryu, Wei D Lu, Wei Lu, Xiaogan Liang.   

Abstract

Memristors based on 2D layered materials could provide biorealistic ionic interactions and potentially enable construction of energy-efficient artificial neural networks capable of faithfully emulating neuronal interconnections in human brains. To build reliable 2D-material-based memristors suitable for constructing working neural networks, the memristive switching mechanisms in such memristors need to be systematically analyzed. Here, we present a study on the switching characteristics of the few-layer MoS2 memristors made by mechanical printing. First, two types of dc-programmed switching characteristics, termed rectification-mediated and conductance-mediated behaviors, are observed among different MoS2 memristors, which are attributed to the modulation of MoS2/metal Schottky barriers and redistribution of vacancies, respectively. We also found that an as-fabricated MoS2 memristor initially exhibits an analog pulse-programmed switching behavior, but it can be converted to a quasi-binary memristor with an abrupt switching behavior through an electrical stress process. Such a transition of switching characteristics is attributed to field-induced agglomeration of vacancies at MoS2/metal interfaces. The additional Kelvin probe force microscopy, Auger electron spectroscopy analysis, and electronic characterization results support this hypothesis. Finally, we fabricated a testing device consisting of two adjacent MoS2 memristors and demonstrated that these two memristors can be ionically coupled to each other. This device interconnection scheme could be exploited to build neural networks for emulating ionic interactions among neurons. This work advances the device physics for understanding the memristive properties of 2D-material-based memristors and serves as a critical foundation for building biorealistic neuromorphic computing systems based on such memristors.

Entities:  

Keywords:  2D materials; MoS2; defects; memories; memristors; nanoelectronics

Mesh:

Substances:

Year:  2018        PMID: 30192507     DOI: 10.1021/acsnano.8b03977

Source DB:  PubMed          Journal:  ACS Nano        ISSN: 1936-0851            Impact factor:   15.881


  6 in total

1.  Analog-digital hybrid computing with SnS2 memtransistor for low-powered sensor fusion.

Authors:  Shania Rehman; Muhammad Farooq Khan; Hee-Dong Kim; Sungho Kim
Journal:  Nat Commun       Date:  2022-05-19       Impact factor: 17.694

Review 2.  Decade of 2D-materials-based RRAM devices: a review.

Authors:  Muhammad Muqeet Rehman; Hafiz Mohammad Mutee Ur Rehman; Jahan Zeb Gul; Woo Young Kim; Khasan S Karimov; Nisar Ahmed
Journal:  Sci Technol Adv Mater       Date:  2020-03-18       Impact factor: 8.090

3.  A Photoelectric-Stimulated MoS2 Transistor for Neuromorphic Engineering.

Authors:  Shuiyuan Wang; Xiang Hou; Lan Liu; Jingyu Li; Yuwei Shan; Shiwei Wu; David Wei Zhang; Peng Zhou
Journal:  Research (Wash D C)       Date:  2019-11-11

4.  Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing.

Authors:  Shengliang Cheng; Zhen Fan; Jingjing Rao; Lanqing Hong; Qicheng Huang; Ruiqiang Tao; Zhipeng Hou; Minghui Qin; Min Zeng; Xubing Lu; Guofu Zhou; Guoliang Yuan; Xingsen Gao; Jun-Ming Liu
Journal:  iScience       Date:  2020-11-30

5.  In-sensor reservoir computing for language learning via two-dimensional memristors.

Authors:  Linfeng Sun; Zhongrui Wang; Jinbao Jiang; Yeji Kim; Bomin Joo; Shoujun Zheng; Seungyeon Lee; Woo Jong Yu; Bai-Sun Kong; Heejun Yang
Journal:  Sci Adv       Date:  2021-05-14       Impact factor: 14.136

Review 6.  Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing.

Authors:  Ki Chang Kwon; Ji Hyun Baek; Kootak Hong; Soo Young Kim; Ho Won Jang
Journal:  Nanomicro Lett       Date:  2022-02-05
  6 in total

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