Bismuth-based ferroelectric memristive device induced by interface barrier for neuromorphic computing

Abstract

The development of the Internet of Things (IoT) not only facilitates our lives but also dramatically grows data. Artificial synaptic devices appear to represent an emerging physical solution compared to the existing computational architectures. Memristive devices are of great interest with their high integration and low power consumption in the field of synaptic devices. In this work, we demonstrate short-term plasticity (STP) and long-term plasticity (LTP) by regulating synaptic weights with different stimulus pulses. Moreover, the application in neuromorphic computing is further exhibited by image recognition with an accuracy of 95.2 % under the modified National Institute of Standards and Technology database. Therefore, Nd-doped Bi₄Ti₃O₁₂ memristors, as emerging artificial synaptic devices, are expected to achieve breakthroughs in artificial intelligence and promote the development of neuromorphic computing and intelligent systems.