Co-existence of digital and analog resistive switching in 2D layered BiOI nanosheets for synaptic applications

• Published in: Materials & design 2023-10, Vol.234, p.112367, Article 112367
• Main Authors: Xie, Wanxuan, Zhong, Yang, Wang, Dehui, Zhong, Lun, Han, Lu, Yang, Qiongfen, Jie, Wenjing
• Format: Article
• Language: English
• Subjects: Bismuth oxyiodide; Memristors; Resistive switching; Synapses; Two-dimensional materials
• ISSN: 0264-1275; 1873-4197
• DOI: 10.1016/j.matdes.2023.112367

[Display omitted] •The co-existence of digital and analog resistive switching behaviors can be observed in BiOI nanosheets.•The synaptic memristors can be used to simulate learning-forgetting experience and Pavlov’s dog experiment.•The artificial neural network based on the memristors can be emulated for image recognition with 91.15% accuracy. The emulation of the biological synapses is essential for brain-inspired computing which is expected to overcome the traditional von Neumann bottleneck. Thus, synaptic memristor with analog resistive switching (RS) is highly desirable in non-volatile memristors for future neuromorphic computing. Herein, the co-existence of digital and analog RS can be observed in two-dimensional (2D) layered BiOI nanosheets sandwiched by the top and bottom Pt electrodes. The vertical Pt/BiOI/Pt memristors demonstrate typical bipolar RS behaviors with a large ON/OFF ratio of 1.0 × 103 and long retention time up to 1.6 × 104 s under a relatively large operation voltage. When the operation voltages are reduced to 1 V, analog RS behaviors with a series of tunable resistance states can be observed. The adjustable resistance states can be utilized to emulate “learning-forgetting” experience of human brain. Repeatable long-term potentiation (LTP) and long-term depression (LTD) cycles can be implemented based on the synaptic memristors, which can be used for simulation of artificial neural network for image recognition with accuracy up to 91.15 %. Moreover, Pavlov’s dog experiment is successfully emulated based on the synaptic memristors. This study suggests good prospects of the synaptic memristors based on BiOI nanosheets for future neuromorphic computing.