Heterogeneous Memristive Devices Enabled by Magnetic Tunnel Junction Nanopillars Surrounded by Resistive Silicon Switches

TL;DR

This paper introduces a novel heterogeneous memristive device combining magnetic tunnel junctions with resistive silicon switches, achieving high ON/OFF ratios and multilevel resistance for advanced memory and computing applications.

Contribution

The study presents a new Re-MTJ device that integrates magnetic and resistive switching, offering enhanced performance and functionalities over conventional NVMs.

Findings

High ON/OFF ratio of >1000% achieved.

Multilevel resistance behavior demonstrated.

Microscopic evidence of silicon nanocrystals supports the mechanism.

Abstract

Emerging non-volatile memories (NVMs) have currently attracted great interest for their potential applications in advanced low-power information storage and processing technologies. Conventional NVMs, such as magnetic random access memory (MRAM) and resistive random access memory (RRAM) suffer from limitations of low tunnel magnetoresistance (TMR), low access speed or finite endurance. NVMs with synergetic advantages are still highly desired for future computer architectures. Here, we report a heterogeneous memristive device composed of a magnetic tunnel junction (MTJ) nanopillar surrounded by resistive silicon switches, named resistively enhanced MTJ (Re-MTJ), that may be utilized for novel memristive memories, enabling new functionalities that are inaccessible for conventional NVMs. The Re-MTJ device features a high ON/OFF ratio of >1000% and multilevel resistance behaviour by combining magnetic switching together with resistive switching mechanisms. The magnetic switching originates from the MTJ, while the resistive switching is induced by a point-switching filament process that is related to the mobile oxygen ions. Microscopic evidence of silicon aggregated as nanocrystals along the edges of the nanopillars verifies the synergetic mechanism of the heterogeneous memristive device. This device may provide new possibilities for advanced memristive memory and computing architectures, e.g., in-memory computing and neuromorphics.