Spintronic devices for high-density memory and neuromorphic computing – a review
Spintronics is a growing research field that focuses on exploring materials and devices that take advantage of the electron’s “spin” to go beyond charge based devices. The most impactful spintronic device to date is a highly sensitive magnetic field sensor, the spin-valve, that allowed for a 10,00...
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Physics Spintronics Neuromorphic computing Multistate device |
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Physics Spintronics Neuromorphic computing Multistate device Chen, Bingjin Zeng, Minggang Khoo, Khoong Hong Das, Debasis Fong, Xuanyao Fukami, Shunsuke Li, Sai Zhao, Weisheng Parkin, Stuart S. P. Piramanayagam, S. N. Lim, Sze Ter Spintronic devices for high-density memory and neuromorphic computing – a review |
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Spintronics is a growing research field that focuses on exploring materials and devices that take
advantage of the electron’s “spin” to go beyond charge based devices. The most impactful spintronic
device to date is a highly sensitive magnetic field sensor, the spin-valve, that allowed for a 10,000-fold
increase in the storage capacity of hard disk drives since it was first introduced in a magnetic recording
read head in 1997. In about 2007, the original spin-valve that was based on spin-dependent scattering
in metallic magnetic/non-magnetic interfaces evolved to a closely related device in which the essential
physics changed to that of spin-dependent tunneling across ultra-thin insulating layers placed between
magnetic electrodes, but the basic spin-engineered structure remained largely unchanged. These latter
structures were proposed in 1995 as potential memory elements for a magnetic random-access memory
(MRAM) and the first demonstration of this possibility was made in 1999. It was only recently (about
2019) that MRAM became a mainstream foundry technology.
Compared with most conventional charge based electronic devices, spintronic devices have the
advantage of non-volatility, low-power consumption, and scalability to smaller dimensions. For these
reasons, spintronic devices are highly attractive for next-generation information memory-storage and
are promising for advanced applications such as in-memory computing. Furthermore, spintronics
allows for a unique high capacity, non-volatile, solid-state memory-storage device that relies on devices
that can store multiple digital bits in the form of a series of chiral domain walls that are moved at highspeed
using nanosecond long current pulses along magnetic nanowires. These devices also enable synaptic functionalities in neuromorphic computing and are therefore, potential hardware candidates
for artificial intelligence.
In this review article, recent advances in multi-state spintronic devices are discussed. The review
starts with an introduction followed by a discussion on using domain-walls for achieving multiple
states for memory and neuromorphic computing. In the next section, achieving multiple levels based
on domain nucleation are discussed. Subsequent discussions review the use of magnetic pillars, and
other schemes for achieving high-density memory. The prospects of spintronic devices in neuromorphic
computing for artificial intelligence (AI) are also presented. The outlook and directions for new
research are provided at the end. |
author2 |
School of Physical and Mathematical Sciences |
author_facet |
School of Physical and Mathematical Sciences Chen, Bingjin Zeng, Minggang Khoo, Khoong Hong Das, Debasis Fong, Xuanyao Fukami, Shunsuke Li, Sai Zhao, Weisheng Parkin, Stuart S. P. Piramanayagam, S. N. Lim, Sze Ter |
format |
Article |
author |
Chen, Bingjin Zeng, Minggang Khoo, Khoong Hong Das, Debasis Fong, Xuanyao Fukami, Shunsuke Li, Sai Zhao, Weisheng Parkin, Stuart S. P. Piramanayagam, S. N. Lim, Sze Ter |
author_sort |
Chen, Bingjin |
title |
Spintronic devices for high-density memory and neuromorphic computing – a review |
title_short |
Spintronic devices for high-density memory and neuromorphic computing – a review |
title_full |
Spintronic devices for high-density memory and neuromorphic computing – a review |
title_fullStr |
Spintronic devices for high-density memory and neuromorphic computing – a review |
title_full_unstemmed |
Spintronic devices for high-density memory and neuromorphic computing – a review |
title_sort |
spintronic devices for high-density memory and neuromorphic computing – a review |
publishDate |
2024 |
url |
https://hdl.handle.net/10356/176741 |
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sg-ntu-dr.10356-1767412024-05-20T15:35:36Z Spintronic devices for high-density memory and neuromorphic computing – a review Chen, Bingjin Zeng, Minggang Khoo, Khoong Hong Das, Debasis Fong, Xuanyao Fukami, Shunsuke Li, Sai Zhao, Weisheng Parkin, Stuart S. P. Piramanayagam, S. N. Lim, Sze Ter School of Physical and Mathematical Sciences Physics and Applied Physics Institute of Material Research and Engineering, A*STAR Physics Spintronics Neuromorphic computing Multistate device Spintronics is a growing research field that focuses on exploring materials and devices that take advantage of the electron’s “spin” to go beyond charge based devices. The most impactful spintronic device to date is a highly sensitive magnetic field sensor, the spin-valve, that allowed for a 10,000-fold increase in the storage capacity of hard disk drives since it was first introduced in a magnetic recording read head in 1997. In about 2007, the original spin-valve that was based on spin-dependent scattering in metallic magnetic/non-magnetic interfaces evolved to a closely related device in which the essential physics changed to that of spin-dependent tunneling across ultra-thin insulating layers placed between magnetic electrodes, but the basic spin-engineered structure remained largely unchanged. These latter structures were proposed in 1995 as potential memory elements for a magnetic random-access memory (MRAM) and the first demonstration of this possibility was made in 1999. It was only recently (about 2019) that MRAM became a mainstream foundry technology. Compared with most conventional charge based electronic devices, spintronic devices have the advantage of non-volatility, low-power consumption, and scalability to smaller dimensions. For these reasons, spintronic devices are highly attractive for next-generation information memory-storage and are promising for advanced applications such as in-memory computing. Furthermore, spintronics allows for a unique high capacity, non-volatile, solid-state memory-storage device that relies on devices that can store multiple digital bits in the form of a series of chiral domain walls that are moved at highspeed using nanosecond long current pulses along magnetic nanowires. These devices also enable synaptic functionalities in neuromorphic computing and are therefore, potential hardware candidates for artificial intelligence. In this review article, recent advances in multi-state spintronic devices are discussed. The review starts with an introduction followed by a discussion on using domain-walls for achieving multiple states for memory and neuromorphic computing. In the next section, achieving multiple levels based on domain nucleation are discussed. Subsequent discussions review the use of magnetic pillars, and other schemes for achieving high-density memory. The prospects of spintronic devices in neuromorphic computing for artificial intelligence (AI) are also presented. The outlook and directions for new research are provided at the end. National Research Foundation (NRF) Submitted/Accepted version This work is supported by Agency for Science, Technology and Research (A*STAR) under RIE2020 AME Programmatic Grant (Grant No. A18A6b0057), and Career Development Fund (Project No. C210812054). SNP gratefully acknowledges the funding from the National Research Foundation (NRF), Singapore for the CRP21 grant (NRF-CRP21-2018-0003) and the Ministry of Education, Singapore for the tier 2 grant (MOET2EP50122-0023.). 2024-05-20T03:01:33Z 2024-05-20T03:01:33Z 2023 Journal Article Chen, B., Zeng, M., Khoo, K. H., Das, D., Fong, X., Fukami, S., Li, S., Zhao, W., Parkin, S. S. P., Piramanayagam, S. N. & Lim, S. T. (2023). Spintronic devices for high-density memory and neuromorphic computing – a review. Materials Today, 70, 193-217. https://dx.doi.org/10.1016/j.mattod.2023.10.004 1369-7021 https://hdl.handle.net/10356/176741 10.1016/j.mattod.2023.10.004 70 193 217 en NRF-CRP21-2018-0003 A18A6b0057 MOET2EP50122-0023 C210812054 Materials Today © 2024 Elsevier Ltd. All rights reserved. This article may be downloaded for personal use only. Any other use requires prior permission of the copyright holder. The Version of Record is available online at http://doi.org/10.1016/j.mattod.2023.10.004. application/pdf |