Journal
NATURE COMMUNICATIONS
Volume 10, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/s41467-019-11187-9
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Funding
- Samsung Research Funding & Incubation Center of Samsung Electronics [SRFC-MA1701-01]
- Korea Research Fellowship Program through the National Research Foundation of Korea (NRF) - Ministry of Science and ICT [NRF-2017H1D3A1A01013759]
- A3 foresight by JSPS
- Singapore Ministry of Education [MOE2016-T2-2-141]
- Elemental Strategy Initiative by the MEXT, Japan
- National Research Foundation of Korea [2017H1D3A1A01013759] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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 nonvolatile 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.
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