4.8 Article

Synaptic Barristor Based on Phase-Engineered 2D Heterostructures

Journal

ADVANCED MATERIALS
Volume 30, Issue 35, Pages -

Publisher

WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.201801447

Keywords

2D materials; artificial synapse; barristor; heterostructure; memristor; neuromorphic application

Funding

  1. National Research Foundation (NRF) of Korea [2017R1D1A1B03035441, 2016R1C1B2007330, 2017R1A5A1014862]
  2. KU-KIST School Project
  3. Korea University Future Research Grant
  4. TJ Park Science Fellowship of POSCO TJ Park Foundation
  5. Samsung Electronics
  6. NRF of Korea [2009-0081565]
  7. Creative Materials Discovery Program [NRF-2016M3D1A1900035]
  8. National Research Foundation of Korea [2017R1D1A1B03035441, 2018R1A3A3000666, 2016M3D1A1900035, 2017R1A5A1014862, 2016R1C1B2007330] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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The development of energy-efficient artificial synapses capable of manifoldly tuning synaptic activities can provide a significant breakthrough toward novel neuromorphic computing technology. Here, a new class of artificial synaptic architecture, a three-terminal device consisting of a vertically integrated monolithic tungsten oxide memristor, and a variable-barrier tungsten selenide/graphene Schottky diode, termed as a 'synaptic barrister,' are reported. The device can implement essential synaptic characteristics, such as short-term plasticity, long-term plasticity, and paired-pulse facilitation. Owing to the electrostatically controlled barrier height in the ultrathin van der Waals heterostructure, the device exhibits gate-controlled memristive switching characteristics with tunable programming voltages of 0.2-0.5 V. Notably, by electrostatic tuning with a gate terminal, it can additionally regulate the degree and tuning rate of the synaptic weight independent of the programming impulses from source and drain terminals. Such gate tunability cannot be accomplished by previously reported synaptic devices such as memristors and synaptic transistors only mimicking the two-neuronal-based synapse. These capabilities eventually enable the accelerated consolidation and conversion of synaptic plasticity, functionally analogous to the synapse with an additional neuromodulator in biological neural networks.

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