Non-Destructive Readout Method for 3D Magnetic Memory

Recently 3D magnetic memory using artificial ferromagnets has been proposed as a high performance memory solution. However, the existing method for reading information from this device is destructive. Here, we propose a novel nondestructive readout method for 3D magnetic memory, leveraging the magnetization dynamics induced by the spin transfer torque. We show that 16 different identifications in a 4-bit memory are possible by simply reading out the magnetization dynamics of the topmost bit layer of 3D magnetic memory with a magnetic tunnel junction. This advancement paves the way for more reliable 3D magnetic memory technologies.

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Publication Details

Journal
Journal of the Magnetics Society of Japan
Published
2024-11-27
DOI
https://doi.org/10.3379/msjmag.2501r002
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
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article

Non-Destructive Readout Method for 3D Magnetic Memory

Takao Ono, Ryusuke Hisatomi, Yoichi Shiota, Shutaro Karube et al.
1 links
Journal of the Magnetics Society of Japan
Neural Networks and Applications
article

Non-Destructive Readout Method for 3D Magnetic Memory

Takao Ono, Ryusuke Hisatomi, Yoichi Shiota, Shutaro Karube, Hyuk‐Jae Jang, Fei Ye
article en

Abstract

Recently 3D magnetic memory using artificial ferromagnets has been proposed as a high performance memory solution. However, the existing method for reading information from this device is destructive. Here, we propose a novel nondestructive readout method for 3D magnetic memory, leveraging the magnetization dynamics induced by the spin transfer torque. We show that 16 different identifications in a 4-bit memory are possible by simply reading out the magnetization dynamics of the topmost bit layer of 3D magnetic memory with a magnetic tunnel junction. This advancement paves the way for more reliable 3D magnetic memory technologies.

Journal of the Magnetics Society of JapanVol. 49(1)
Kyoto University (JP), Spintronics Research Network of Japan (JP), Instituto de Investigaciones Químicas (ES), Kyoto University Institute for Chemical Research
Openalex Percentile: Top 81%
Neural Networks and Applications
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