posted on 2024-02-21, 17:05authored byGuoliang Wang, Fei Sun, Shiyu Zhou, Yizhi Zhang, Fan Zhang, Haiyan Wang, Jijie Huang, Yue Zheng
Memristors can be used to mimic synaptic behavior in
artificial
neural networks, which makes them a key component in neuromorphic
computing and holds promise for advancing the field. In this study,
a memory artificial synaptic device based on ZnO–BaTiO3 (ZnO–BTO) vertically aligned nanocomposite thin films
was prepared. The vertical interface between the two phases can be
used as a conduit for oxygen vacancy (OV) accumulation and a channel
for OV movement, which greatly optimizes the resistive switching performance
of the device and has the potential for multistage storage. By applying
different pulse sequences to the device, the conductance of the device
is adjusted from multiple angles, and a variety of synaptic functions
are simulated, such as paired-pulse facilitation, spike-timing-dependent
plasticity, short-term plasticity to long-term plasticity (STP–LTP),
and long-term potentiation/depression (LTP/LTD). Finally, we construct
a neural network for image recognition, and the recognition accuracy
can reach 91%. Our study demonstrates the feasibility of using composite
thin-film vertical interface to regulate the resistive performance
of memristors and its great potential in artificial synaptic simulation
and neuromorphic computing.