Volume 2 Issue 3
August  2023
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Yuxuan Yao, Houyi Cheng, Boyu Zhang, Jialiang Yin, Daoqian Zhu, Wenlong Cai, Sai Li, Weisheng Zhao. Tunneling magnetoresistance materials and devices for neuromorphic computing[J]. Materials Futures, 2023, 2(3): 032302. doi: 10.1088/2752-5724/ace3af
Citation: Yuxuan Yao, Houyi Cheng, Boyu Zhang, Jialiang Yin, Daoqian Zhu, Wenlong Cai, Sai Li, Weisheng Zhao. Tunneling magnetoresistance materials and devices for neuromorphic computing[J]. Materials Futures, 2023, 2(3): 032302. doi: 10.1088/2752-5724/ace3af
shu
Topical Review •
OPEN ACCESS

Tunneling magnetoresistance materials and devices for neuromorphic computing

© 2023 The Author(s). Published by IOP Publishing Ltd on behalf of the Songshan Lake Materials Laboratory
Materials Futures, Volume 2, Number 3
  • Received Date: 2023-04-15
  • Accepted Date: 2023-06-28
  • Rev Recd Date: 2023-06-17
  • Publish Date: 2023-08-04
doi: 10.1088/2752-5724/ace3af
  • 1.

    Fert Beijing Institute, MIIT Key Laboratory of Spintronics, School of Integrated Circuit Science and Engineering, Beihang University, Beijing 100191, People’s Republic of China

  • 2.

    Hefei Innovation Research Institute, Anhui High Reliability Chips Engineering Laboratory, Beihang University, Hefei 230013, People’s Republic of China

  • 3.

    Truth Equipment Corporation, TREC, Hefei 230013, People’s Republic of China

  • 4.

    Beihang-Geortek Joint Microelectronics Institute, Qingdao Research Institute, Beihang University, Qingdao 266061, People’s Republic of China

  • These authors contributed equally to this work.

More Information
    Abstract
  • Artificial intelligence has become indispensable in modern life, but its energy consumption has become a significant concern due to its huge storage and computational demands. Artificial intelligence algorithms are mainly based on deep learning algorithms, relying on the backpropagation of convolutional neural networks or binary neural networks. While these algorithms aim to simulate the learning process of the human brain, their low bio-fidelity and the separation of storage and computing units lead to significant energy consumption. The human brain is a remarkable computing machine with extraordinary capabilities for recognizing and processing complex information while consuming very low power. Tunneling magnetoresistance (TMR)-based devices, namely magnetic tunnel junctions (MTJs), have great advantages in simulating the behavior of biological synapses and neurons. This is not only because MTJs can simulate biological behavior such as spike-timing dependence plasticity and leaky integrate-fire, but also because MTJs have intrinsic stochastic and oscillatory properties. These characteristics improve MTJs’ bio-fidelity and reduce their power consumption. MTJs also possess advantages such as ultrafast dynamics and non-volatile properties, making them widely utilized in the field of neuromorphic computing in recent years. We conducted a comprehensive review of the development history and underlying principles of TMR, including a detailed introduction to the material and magnetic properties of MTJs and their temperature dependence. We also explored various writing methods of MTJs and their potential applications. Furthermore, we provided a thorough analysis of the characteristics and potential applications of different types of MTJs for neuromorphic computing. TMR-based devices have demonstrated promising potential for broad application in neuromorphic computing, particularly in the development of spiking neural networks. Their ability to perform on-chip learning with ultra-low power consumption makes them an exciting prospect for future advances in the era of the internet of things.
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    • Conflict of interests

    The authors have no conflicts to disclose.

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