Programmable Multilevel Memtransistors Based on van der Waals Heterostructures

Hyunik Park, Michael A. Mastro, Marko J. Tadjer, Jihyun Kim

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Neuromorphic computing that mimics the energy-efficient cortical neural network in the human brain is attractive because of its possibility to process complex and massive data sets and achieve fast computing capability. Herein, a heterosynaptic and programmable memtransistor architecture with high computing functionality is reported by monolithically integrating a hexagonal boron nitride (h-BN) memristor with a molybdenum disulfide (MoS2) transistor. Memristors consisting of a vertically stacked van der Waals materials (multilayer graphene (MLG) and h-BN) exhibit a stable bipolar resistive switching behavior with a memory window more than three orders of magnitude due to the formation and rupture of the metallic filament within the h-BN layer. By controlling the resistance state of the h-BN memristor, the behaviors of the memtransistor can be programmed with a high switching ratio of ≈104, showing ≈16 pW standby power consumption. A multistate computing window and tunable current on/off ratio can be achieved by controlling the synaptic weight of the memristor, demonstrating that the presented 2D architecture can be exploited as a logic inverter device. The results pave the way toward the development of highly functional neuromorphic systems for the next-generation in-memory computing.

Original languageEnglish
Article number1900333
JournalAdvanced Electronic Materials
Volume5
Issue number10
DOIs
Publication statusPublished - 2019 Oct 1

Keywords

  • 2D materials
  • heterostructures
  • in-memory computing
  • memristors
  • memtransistors
  • neuromorphic

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials

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