Mott Memristors for Neuromorphics
Abstract Neuromorphic computing has emerged as a key solution for overcoming the challenge of von Neumann bottleneck, offering a pathway to more efficient and biologically inspired computing systems. A crucial advancement in this field is the utilization of Mott insulators, where the metal‐insulator transition (MIT) elicits substantial alterations in material properties, infusing renewed vigor into the progression of neuromorphic systems. This review begins by explaining the MIT mechanisms and the preparation processes of Mott insulators, followed by an introduction of Mott memristors and memristor arrays, showing different types of multidimensional integration styles. The applications of Mott memristor in neuromorphic computing are then discussed, which include artificial synapse designs and various artificial neuron architectures for sensory recognition and logic calculation. Finally, facing challenges and potential future directions are outlined for utilizing Mott memristors in the advancement of neuromorphic computing. This review aims to provide a thorough understanding of the latest advancements in Mott memristors and their applications, offering a comprehensive reference for further research in related areas, and contributing to bridging the gap between traditional silicon‐based electronics and future brain‐inspired architectures.
Authors
- Shuangmei Xue (ORCID: https://orcid.org/0000-0001-5374-3922)
- Su‐Ting Han (ORCID: https://orcid.org/0000-0003-3392-7569)
- Kui Zhou (ORCID: https://orcid.org/0000-0003-1103-2166)
- Yongbiao Zhai
- Vellaisamy A. L. Roy (ORCID: https://orcid.org/0000-0003-1432-9950)
- Guanglong Ding
- Ziyu Lv (ORCID: https://orcid.org/0000-0002-7062-3647)
- Meng Zhang (ORCID: https://orcid.org/0000-0002-9302-4057)
- Ye Bo Zhou (ORCID: https://orcid.org/0000-0002-0273-007X)
- Yan Yan
- Wanhong Luan
- Zherui Zhao
Institutions
- Hong Kong Polytechnic University (HK)
- Shenzhen University (CN)
- Guangdong Province Special Equipment Testing and Research Institute Zhuhai Testing Institute (CN)
Publication Details
- Journal
- Advanced Physics Research
- Published
- 2024-10-28
- DOI
- https://doi.org/10.1002/apxr.202400129
- Citations
- 9
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 1.88
Funders
- National Natural Science Foundation of China
- National Taipei University of Technology
- Basic and Applied Basic Research Foundation of Guangdong Province