4.8 Article

Three-terminal memtransistors based on two-dimensional layered gallium selenide nanosheets for potential low-power electronics applications

期刊

NANO ENERGY
卷 57, 期 -, 页码 566-573

出版社

ELSEVIER
DOI: 10.1016/j.nanoen.2018.12.057

关键词

2D materials; GaSe; Resistive switching; Memristors; Memtransistors

资金

  1. National Natural Science Foundation of China [61604100, 51772252]
  2. Hong Kong RGC GRF [PolyU 153033/17P]

向作者/读者索取更多资源

A multi-terminal hybrid system named memtransistor has recently been proposed by combining the concepts of both memristor and field effect transistor (FET) with two-dimensional (2D) layered materials as the active semiconductor layer. In the memtransistors, the gate voltages are capable of modulating not only the transport properties of the fabricated FET, but also the resistive switching (RS) behaviors of the memristor. Herein, we employ mechanically exfoliated 2D layered GaSe nanosheets to prepare GaSe based three-terminal memtransistors. By using Ag as the electrodes, the memristor exhibits non-volatile bipolar RS characteristics. More importantly, under exposure to air for one week, the RS behaviors are dramatically enhanced with the ON/OFF ratio reaching up to 5.3 x 10(5) and ultralow threshold electric field of similar to 3.3 x 10(2) V cm(-1). The ultralow threshold electric field of GaSe based memristor could be related to the low migration energy of the intrinsic Ga vacancy in p-type GaSe. Moreover, the GaSe-based memristor shows long-term retention (similar to 10(4) s) and high cycling endurance (similar to 5000 cycles) simultaneously. Hence, the fabricated three-terminal 2D GaSe memtransistors possess high performance with large switching ratios, ultralow threshold electric field, good endurance and long-term retention. Furthermore, the device demonstrates gate tunability in RS characteristics, suggesting the promising applications in multi-terminal electronic devices with low power consumption and complex functionalities, ranging from non-volatile memory, logic device to neuromorphic computing.

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