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Self-selective van der Waals heterostructures for large scale memory array

  • Linfeng Sun
  • , Yishu Zhang
  • , Gyeongtak Han
  • , Geunwoo Hwang
  • , Jinbao Jiang
  • , Bomin Joo
  • , Kenji Watanabe
  • , Takashi Taniguchi
  • , Young Min Kim
  • , Woo Jong Yu
  • , Bai Sun Kong
  • , Rong Zhao
  • , Heejun Yang
  • Sungkyunkwan University
  • Singapore University of Technology and Design
  • National Institute for Materials Science Tsukuba

Research output: Contribution to journalArticlepeer-review

Abstract

The large-scale crossbar array is a promising architecture for hardware-amenable energy efficient three-dimensional memory and neuromorphic computing systems. While accessing a memory cell with negligible sneak currents remains a fundamental issue in the crossbar array architecture, up-to-date memory cells for large-scale crossbar arrays suffer from process and device integration (one selector one resistor) or destructive read operation (complementary resistive switching). Here, we introduce a self-selective memory cell based on hexagonal boron nitride and graphene in a vertical heterostructure. Combining non-volatile and volatile memory operations in the two hexagonal boron nitride layers, we demonstrate a self-selectivity of 1010 with an on/off resistance ratio larger than 103. The graphene layer efficiently blocks the diffusion of volatile silver filaments to integrate the volatile and non-volatile kinetics in a novel way. Our self-selective memory minimizes sneak currents on large-scale memory operation, thereby achieving a practical readout margin for terabit-scale and energy-efficient memory integration.

Original languageEnglish
Article number3161
JournalNature Communications
Volume10
Issue number1
DOIs
StatePublished - 1 Dec 2019

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