Volume 33 Issue 2
Mar.  2024
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Xiaojuan LIAN, Yuelin SHI, Xinyi SHEN, et al., “Design of High Performance MXene/Oxide Structure Memristors for Image Recognition Applications,” Chinese Journal of Electronics, vol. 33, no. 2, pp. 336–345, 2024 doi: 10.23919/cje.2022.00.125
Citation: Xiaojuan LIAN, Yuelin SHI, Xinyi SHEN, et al., “Design of High Performance MXene/Oxide Structure Memristors for Image Recognition Applications,” Chinese Journal of Electronics, vol. 33, no. 2, pp. 336–345, 2024 doi: 10.23919/cje.2022.00.125

Design of High Performance MXene/Oxide Structure Memristors for Image Recognition Applications

doi: 10.23919/cje.2022.00.125
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  • Author Bio:

    Xiaojuan LIAN received the B.S. degree in electronic science and technology and the M.S. degree in physical electronics from Xidian University in 2008 and 2011 respectively. She received the Ph.D. degree in electrical engineering from the Universitat Autònoma de Barcelona, Spain, in 2014. She is currently an Associate Professor at the College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications. Her research interests include memristive devices (RRAM, PCRAM and so on), information storage technology, and neuromorphic computing applications. (Email: xjlian@njupt.edu.cn)

    Yuelin SHI received the B.S. 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 memristive devices. (Email: shiyuelin1@163.com)

    Xinyi SHEN received the B.S. degree in microelectronics science and engineering from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2018. She further got the M.S. degree in microelectronics science and engineering from Nanjing University of Posts and Telecommunications, Nanjing, China, in 2021. (Email: shenxinyi66@qq.com)

    Xiang WAN received the B.S. degree in applied physics and the M.S. degree in materials engineering from University of Science and Technology of China in 2011 and 2014 respectively. He received the Ph.D. degree in electronic science and technology from Nanjing University in 2017. From 2019 to 2021, he held the postdoctoral position at the National Institute for Materials Science. He is currently a Lecturer at the College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications. His current research interests are the design, fabrication and modeling of electronic devices and systems for neuromorphic computation. (Email: wanxiang@njupt.edu.cn)

    Zhikuang CAI received the B.S. degree in information engineering from Nanjing University of Posts and Telecommunications in 2005, and Ph.D. degree from ASIC System Center at the Southeast University in 2014. He is currently a Professor at the College of Integrated Circuit Science and Engineering, Nanjing University of Posts and Telecommunications. His research direction is low power SoC design and test, and Chiplet package and test. (Email: whczk@njupt.edu.cn)

    Lei WANG received the B.S. degree in electrical engineering from the Beijing University of Science and Technology in 2003, the M.S. degree in electronic instrumentation systems from the University of Manchester in 2004, and the Ph.D. degree in 2009 at the University of Exeter. Between 2008 and 2011, he was employed as a Postdoctoral Fellow in the University of Exeter to work on a fellowship funded by European Commission. In 2020, he joined the Nanjing University of Posts and Telecommunications as a Full Professor. His research interests include phase-change memories, neural networks, and other phase-change based optoelectronic devices and their applications. (Email: leiwang1980@njupt.edu.cn)

  • Corresponding author: Email: whczk@njupt.edu.cn; Email: leiwang1980@njupt.edu.cn
  • Received Date: 2022-05-09
  • Accepted Date: 2022-08-29
  • Available Online: 2023-07-08
  • Publish Date: 2024-03-05
  • Recent popularity to realize image recognition by memristor-based neural network hardware systems has been witnessed owing to their similarities to neurons and synapses. However, the stochastic formation of conductive filaments inside the oxide memristor devices inevitably makes them face some drawbacks, represented by relatively higher power consumption and severer resistance switching variability. In this work, we design and fabricate the Ag/MXene (Ti3C2)/SiO2/Pt memristor after considering the stronger interactions between Ti3C2 and Ag ions, which lead to a Ti3C2/SiO2 structure memristor owning to much lower “SET” voltage and smaller resistance switching fluctuation than pure SiO2 memristor. Furthermore, the conductances of the Ag/Ti3C2/SiO2/Pt memristor have been modulated by changing the number of the applied programming pulse, and two typical biological behaviors, i.e., long-term potentiation and long-term depression, have been achieved. Finally, device conductances are introduced into an integrated device-to-algorithm framework as synaptic weights, by which the MNIST hand-written digits are recognized with accuracy up to 77.39%.
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