Applications of MXene-based memristors in neuromorphic intelligence applications

Abstract

MXenes are materials with a few thick layers of transition metal carbides, nitrides, and carbonitrides and have received considerable attention because of their widespread application in energy storage in photonic diodes. In addition, nanoscale devices that include either an MXene layer only or a combination of MXene and other functional layers were found to exhibit multiple non-volatile resistance states when subjected to an electrical stimulus. Therefore, the MXene layer has most recently shown a strong liaison with the concept of the well-known memristor, whereby a variety of MXene-based memristors have been developed for emerging neuromorphic applications. Despite the current prosperity, the physics behind which MXene-based devices enable memristive behaviour remains vague, and the advantages and disadvantages of these reported MXene-based memristors in association with their performance comparisons are missing. To address these issues, we first presented different types of MXene-based memristors according to the constitutions of their active layers, and the possible physical mechanisms that govern the memristive behaviours of these memristors were analysed. The promising applications of the reported MXene-based memristors, particularly in the field of neuromorphic intelligence, are subsequently discussed. Finally, the advantages and disadvantages of MXene-based memristors and their practical prospects are envisaged.

KEYWORDS:

• MXene material
• memristor
• resistance switching
• neuromorphic intelligence

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under grant 61964012 and grant 61974073, in part by the National Key Research and Development Program of China under grant 2018YFB2202005, in part by the Natural Science Foundation of Jiangsu Province under grant BK20211273 and grant BZ2021031, in part by the open research fund of the National and Local Joint Engineering Laboratory of RF Integration and Micro-Assembly Technology under grant KFJJ20200102, in part by the Nanjing University of Posts and Telecommunications under grant NY220112, and in part by the Foundation of Jiangxi Science and Technology Department under grant 20202ACBL21200.

Notes on contributors

Xiaojuan Lian

Xiaojuan Lian received the B. S. degree in electronic science and technology and the M. S. degree in physical electronics from Xidian University, Xi’an, China, in 2008 and 2011 respectively. She received her Ph.D. degree in electrical engineering from the Universitat Autònoma de Barcelona, Spain, in 2014. She is currently an associate professor at the Department of Electronic and Optical Engineering, Nanjing University of Posts and Telecommunications, China. Her research interests include memristive devices (RRAM, PCRAM and so on), 2D material-based devices, information storage, and artificial intelligence.

Yuelin Shi

Yuelin Shi received the B. Eng. degree in information engineering from Ludong University, Shandong, China, in 2021. She is currently pursuing the M.S. degree with Nanjing University of Posts and Telecommunications, engaged in the research of neuromorphic computing applications based on memristor.

Shiyu Li

Shiyu Li received the B. Eng. degree in Microelectronics Science and Engineering from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2022. He is currently pursuing the M. S. degree with Nanjing University of Posts and Telecommunications, engaged in the design and analysis of memristor devices.

Bingxin Ding

Bingxin Ding received the B. Eng. degree in new energy science and engineering from Yancheng Teachers University, Yancheng, China, in 2021. She is currently pursuing the M. S. degree with Nanjing University of Posts and Telecommunications, engaged in the research of logic gates research based on memristor.

Chenfei Hua

Chenfei Hua received the B. Eng. degree in Science and Engineering of Microelectronics from Nanjing University of Posts and Telecommunications, JiangSu, China, in 2022. He is currently pursuing the M. S. degree with Nanjing University of Posts and Telecommunications, engaged in the research of Memoristor test.

Lei Wang

Lei Wang received the B. Eng. degree in electrical engineering from the Beijing University of Science and Technology, Beijing, China, in 2003, the M. Sc. degree in electronic instrumentation systems from the University of Manchester, Manchester, U.K., in 2004, and the Ph.D degree in “Tbit/sq.in. scanning probe phase-change memory” from the University of Exeter, Exeter, U.K., in 2009. Between 2008 and 2011, he was employed as a Postdoctoral Research Fellow in the University of Exeter to work on a fellowship funded by European Commision. These works included the study of phase-change probe memory and phase-change memristor. Since 2020, he joined the Nanjing University of Posts and Telecommunications, Nanjing, P. R. China as a Professor, where he is engaged in the phase-change memories, phase-change neural networks, and other phase-change based optoelectronic devices and their potential applications.