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

https://mdr.nims.go.jp/datasets/2a5aa841-32d7-46e0-b3fb-fb98584843a1

## File

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## Id

2a5aa841-32d7-46e0-b3fb-fb98584843a1

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-02-20T02:28:08.921030Z

## Updated at

2025-02-23T13:49:49.215687Z

## Published at

2025-02-23T13:49:49.271948Z

## Doi



## First published url

https://doi.org/10.1038/s41467-019-11187-9

## Date published

2019-07-18

## Recorded date published



## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Self-selective van der Waals heterostructures for large scale memory array
  title_type: original
  lang: en

## Description

- description: The massively integrated crossbar array is a highly promising architecture
    for next-generation three-dimensional (3D) memory and neuromorphic computing systems.
    However, accessing a specific memory cell with negligible influence on innumerable
    unselected memory cells, generally known as sneak current, remains a fundamental
    issue. Here, we report a self-selective memory cell based on two-dimensional (2D)
    hexagonal boron nitride (h-BN) and graphene in a vertical heterostructure of h-BN/graphene/h-BN.
    The self-selectivity, defined as the resistance ratio of selected and unselected
    memory cells, exceeds 10^10 over a wide voltage window in our memory cell, which
    originates from volatile silver (Ag) filaments and non-volatile boron vacancies
    inside two separated h-BN layers with impermeable graphene located between these
    two h-BN layers as a filament-blocking layer. Moreover, the chemically inert interface
    between the h-BN and graphene layers creates rapid filament-forming dynamics with
    a time constant of tens of nanoseconds. The record-high selectivity over a wide
    voltage window greatly minimizes the effects of sneak current on integrated memory
    operation, thereby achieving a practical readout margin for terabit-scale and
    energy-efficient memory integration. The self-selective van der Waals heterostructure
    not only provides an ideal memory compatible with flexible substrates for wearable
    electronic applications but also is greatly instrumental in constructing 3D stackable
    memory and neuromorphic systems.
  description_type: abstract
  lang: und

## Creator

- name: Linfeng Sun
  role: author
- name: Yishu Zhang
  role: author
- name: Gyeongtak Han
  role: author
- name: Geunwoo Hwang
  role: author
- name: Jinbao Jiang
  role: author
- name: Bomin Joo
  role: author
- name: Kenji Watanabe
  role: author
  orcid: https://orcid.org/0000-0003-3701-8119
  organization: National Institute for Materials Science
- name: Takashi Taniguchi
  role: author
  orcid: https://orcid.org/0000-0002-1467-3105
  organization: National Institute for Materials Science
- name: Young-Min Kim
  role: author
- name: Woo Jong Yu
  role: author
- name: Bai-Sun Kong
  role: author
- name: Rong Zhao
  role: author
- name: Heejun Yang
  role: author

## Contact agent



## Publisher

organization: Springer Science and Business Media LLC

## Managing organization



## Keyword

- subject: Crossbar array
  schema: not_defined
- subject: memory cell
  schema: not_defined
- subject: hexagonal boron nitride
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Nature Communications
  issn: '20411723'
  volume: '10'
  issue: '1'

## Conference



## Related item



## Funding

- identifier: SRFC-MA1701-01
  funder_name: Samsung

## Instrument



## Instrument operator



## Instrument managing organization



## Measurement method



## Specimen



## Chemical composition



## Structure for specimen



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## Computational method



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## Fileset

- id: 0e3b9e78-4024-4a6e-8c93-d3577002360c
  filename: s41467-019-11187-9.pdf
  content_type: application/pdf
  size: 1890777
  md5: f2ef0b25109d707d6a55c35a438fbc04

## Thumbnail

fileset_id: 0e3b9e78-4024-4a6e-8c93-d3577002360c
filename: s41467-019-11187-9.pdf