Ferroelectric Memristors Based Hardware of Brain Functions for Future Artificial Intelligence. (September 2020)

• Record Type: Journal Article
• Authors: Shi, Mingmin; Zhang, Miaocheng; Yao, Suhao; Qin, Qi; Wang, Min; Wang, Yu; He, Nan; Zhu, Jiale; Liu, Xiaoyan; Hu, Ertao; Xu, Jianguang; Tong, Yi
• Is Part Of: Journal of physics. Volume 1631(2020)
• Publication Date: 2020-09
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1631/1/012042
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Brain-inspired neuromorphic computing systems have long attracted significant interests to replace the conventional Von-Neumann systems because the development of big data analysis and artificial intelligence has put forward higher requirements for computing speed and energy consumptions. Memristive devices are known as one of the most significant candidates to implement brain-inspired neuromorphic computing systems due to their special properties to emulate biological synapses of human brains. Ferroelectric material is a breakthrough for the resistive-switching layer of memristors due to their continuously tunable resistive switching behaviors. However, the application of ferroelectric memristors is limited by unstable performance and the low switching ratio. In this work, ferroelectric memristors have been fabricated and two-dimensional (2D) material MXene (Ti3 C2 ) has been firstly introduced to the Cu/PZT/Pt devices. The insertion of MXene can enhance the performance of the devices by boosting the formation of conductive filaments of oxygen vacancies. The Cu/MXene/PZT/Pt devices exhibit stable resistive switching phenomena and have a high switching ratio about 10 6 . The resistance of Cu/MXene/PZT/Pt memristors can be regulated under continuous voltage pulses. More interestingly, some synaptic functions have been successfully mimicked using Cu/MXene/PZT/Pt memristors. This work will pave the way for the development of the implementation of brain-inspired computing systems.