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

• Published in: Nature communications Vol. 10; no. 1; pp. 3161 - 7
• Main Authors: Sun, Linfeng, Zhang, Yishu, Han, Gyeongtak, Hwang, Geunwoo, Jiang, Jinbao, Joo, Bomin, Watanabe, Kenji, Taniguchi, Takashi, Kim, Young-Min, Yu, Woo Jong, Kong, Bai-Sun, Zhao, Rong, Yang, Heejun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.07.2019; Nature Publishing Group; Nature Portfolio
• Subjects: 142/126; 639/301/1005/1007; 639/301/357/1018; Arrays; Boron; Boron nitride; Computer architecture; Computer memory; Diffusion barriers; Diffusion layers; Energy efficiency; Filaments; Graphene; Heterostructures; Humanities and Social Sciences; Integration; Kinetics; Memory cells; multidisciplinary; Random access memory; Science; Science (multidisciplinary); Selectivity
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-11187-9

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 10 10 with an on/off resistance ratio larger than 10 3 . 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. Designing large-scale crossbar arrays for energy efficient neuromorphic computing systems remains a challenge. Here, the authors propose Van der Waals (h-BN/graphene/h-BN) self-selective memory design able to combine, in the same cell, non-volatile and volatile behaviors with negligible sneak current.