The investigation of La2Ti2O7 based ferroelectric memristors for brain-inspired computing

Miaocheng Zhang; Xingyu Chen; Yixin Wei; Miao Tan; Weijing Shao; Ye-Rong Zhang; Yi Tong

doi:10.1117/12.3013259

29 November 2023 The investigation of La₂Ti₂O₇ based ferroelectric memristors for brain-inspired computing

Miaocheng Zhang, Xingyu Chen, Yixin Wei, Miao Tan, Weijing Shao, Ye-Rong Zhang, Yi Tong

Proceedings Volume 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023); 129370V (2023) https://doi.org/10.1117/12.3013259
Event: International Conference on Internet of Things and Machine Learning (IoTML 2023), 2023, Singapore, Singapore

Abstract

Ferroelectric memristors have become a promising contender for the realization of neuromorphic computing, due to their advantages of good stability, ultra-fast switching behavior, long endurance, and low power consumption. In this work, novel lanthanum titanate (La₂Ti₂O₇) based ferroelectric memristors (Ag/La2Ti2O7/Pt) have been fabricated. Physical morphology of ferroelectric devices has been investigated by atomic force microscope, piezo-response force microscopy, and optical microscope. The Ag/La₂Ti₂O₇/Pt devices exhibit stable resistive switching phenomena. More interestingly, the conductance of devices can be modulated under continuous voltage pulse signal. Based on the results, the synaptic behaviors have been successfully mimicked including paired pulse facilitation, short-term plasticity, and long-term plasticity. In addition, the convolutional neural network based on the synaptic behaviors of the La₂Ti₂O₇ based ferroelectric crossbar arrays has been built for the application potential in image recognition. The recognition accuracy after learning is up to 96 %.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Miaocheng Zhang, Xingyu Chen, Yixin Wei, Miao Tan, Weijing Shao, Ye-Rong Zhang, and Yi Tong "The investigation of La₂Ti₂O₇ based ferroelectric memristors for brain-inspired computing", Proc. SPIE 12937, International Conference on Internet of Things and Machine Learning (IoTML 2023), 129370V (29 November 2023); https://doi.org/10.1117/12.3013259

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