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 () and Heejun Yang ()
Additional contact information
Linfeng Sun: Sungkyunkwan University
Yishu Zhang: Singapore University of Technology & Design
Gyeongtak Han: Sungkyunkwan University
Geunwoo Hwang: Sungkyunkwan University
Jinbao Jiang: Sungkyunkwan University
Bomin Joo: Sungkyunkwan University
Kenji Watanabe: National Institute for Materials Science
Takashi Taniguchi: National Institute for Materials Science
Young-Min Kim: Sungkyunkwan University
Woo Jong Yu: Sungkyunkwan University
Bai-Sun Kong: Sungkyunkwan University
Rong Zhao: Singapore University of Technology & Design
Heejun Yang: Sungkyunkwan University

Nature Communications, 2019, vol. 10, issue 1, 1-7

Abstract: 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.

Date: 2019
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DOI: 10.1038/s41467-019-11187-9

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