Spintronic heterostructures for artificial intelligence: a materials perspective
With the advent of the Big Data era, neuromorphic computing (NC) (also known as brain-inspired computing) has gained a lot of research interest. Spintronic devices are the emerging candidates for implementing the NC due to their intrinsic nonvolatility, extremely high endurance, low-power consumptio...
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sg-ntu-dr.10356-1658992023-05-15T15:37:19Z Spintronic heterostructures for artificial intelligence: a materials perspective Maddu, Ramu Kumar, Durgesh Bhatti, Sabpreet Piramanayagam, S. N. School of Physical and Mathematical Sciences Science::Physics::Atomic physics::Solid state physics Science::Physics::Electricity and magnetism Spintronic Devices Materials Engineering With the advent of the Big Data era, neuromorphic computing (NC) (also known as brain-inspired computing) has gained a lot of research interest. Spintronic devices are the emerging candidates for implementing the NC due to their intrinsic nonvolatility, extremely high endurance, low-power consumption, and complementary metal-oxide compatibility. Many research groups have proposed various NC architectures based on spintronic devices. Herein, a collective survey of different spintronic-based approaches is given for NC. The reviewed approaches include the progress of stochastic magnetic tunnel junction (MTJ)devices, spin-torque nano-oscillator, spin-Hall nano-oscillator, domain walls, and skyrmion devices. In all of these approaches, spin–orbit torque (SOT)-based magnetization control, which is achieved via spintronics heterostructures, plays a significant role. Various heterostructures of heavy metal and ferromagnetic layers that have been proposed are reviewed for generating SOT. In addition, the phenomena and materials involved in the generation of orbital torque are summarized due to the orbital Hall effect (OHE), which has recently gained researchers' attention. Finally, an outlook on the opportunities and challenges for spintronic-based NC hardware is provided, shedding light on its great potential for artificial intelligence (AI) applications. Ministry of Education (MOE) National Research Foundation (NRF) Submitted/Accepted version The authors gratefully acknowledge the funding from the National Research Foundation (NRF), Singapore, for the CRP21 grant (NRF-CRP21-2018-0003). This research is also partially supported by the Ministry of Education, Singapore under its Tier 2 grant MOE-T2EP50122-0023. 2023-04-14T08:05:20Z 2023-04-14T08:05:20Z 2023 Journal Article Maddu, R., Kumar, D., Bhatti, S. & Piramanayagam, S. N. (2023). Spintronic heterostructures for artificial intelligence: a materials perspective. Physica Status Solidi - Rapid Research Letters. https://dx.doi.org/10.1002/pssr.202200493 1862-6254 https://hdl.handle.net/10356/165899 10.1002/pssr.202200493 en NRF-CRP21-2018-0003 MOE-T2EP50122-0023 Physica Status Solidi - Rapid Research Letters © 2023 Wiley-VCH GmbH. All rights reserved. This is the peer reviewed version of the following article: Maddu, R., Kumar, D., Bhatti, S. & Piramanayagam, S. N. (2023). Spintronic heterostructures for artificial intelligence: a materials perspective. Physica Status Solidi - Rapid Research Letters, which has been published in final form at https://doi.org/10.1002/pssr.202200493. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. application/pdf |
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Science::Physics::Atomic physics::Solid state physics Science::Physics::Electricity and magnetism Spintronic Devices Materials Engineering Maddu, Ramu Kumar, Durgesh Bhatti, Sabpreet Piramanayagam, S. N. Spintronic heterostructures for artificial intelligence: a materials perspective |
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With the advent of the Big Data era, neuromorphic computing (NC) (also known as brain-inspired computing) has gained a lot of research interest. Spintronic devices are the emerging candidates for implementing the NC due to their intrinsic nonvolatility, extremely high endurance, low-power consumption, and complementary metal-oxide compatibility. Many research groups have proposed various NC architectures based on spintronic devices. Herein, a collective survey of different spintronic-based approaches is given for NC. The reviewed approaches include the progress of stochastic magnetic tunnel junction (MTJ)devices, spin-torque nano-oscillator, spin-Hall nano-oscillator, domain walls, and skyrmion devices. In all of these approaches, spin–orbit torque (SOT)-based magnetization control, which is achieved via spintronics heterostructures, plays a significant role. Various heterostructures of heavy metal and ferromagnetic layers that have been proposed are reviewed for generating SOT. In addition, the phenomena and materials involved in the generation of orbital torque are summarized due to the orbital Hall effect (OHE), which has recently gained researchers' attention. Finally, an outlook on the opportunities and challenges for spintronic-based NC hardware is provided, shedding light on its great potential for artificial intelligence (AI) applications. |
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School of Physical and Mathematical Sciences |
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School of Physical and Mathematical Sciences Maddu, Ramu Kumar, Durgesh Bhatti, Sabpreet Piramanayagam, S. N. |
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Article |
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Maddu, Ramu Kumar, Durgesh Bhatti, Sabpreet Piramanayagam, S. N. |
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Maddu, Ramu |
title |
Spintronic heterostructures for artificial intelligence: a materials perspective |
title_short |
Spintronic heterostructures for artificial intelligence: a materials perspective |
title_full |
Spintronic heterostructures for artificial intelligence: a materials perspective |
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Spintronic heterostructures for artificial intelligence: a materials perspective |
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Spintronic heterostructures for artificial intelligence: a materials perspective |
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spintronic heterostructures for artificial intelligence: a materials perspective |
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2023 |
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https://hdl.handle.net/10356/165899 |
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