Influence of Lithium Doping on Volcanic-like Perovskite Memristors and Artificial Synaptic Simulation for Neurocomputing

Perovskite-based memristors have attracted much attention in synaptic simulation due to their outstanding electrical properties and promising potential in neuromorphic computing (NC). In this work, inorganic lead-free perovskite-based memristors composed of Ag/Cs₃Bi_2-xLi_xI_9-2x (CBL_xI)/ITO (x = 0, 0.2, 0.4, 0.6) are fabricated, and the electrical properties, such as endurance, on/off ratio, and retention time, are determined. It is found that the device with x = 0.4 shows good characteristics, such as a set voltage of −0.1 V and a retention time of 10⁴ s. The multilevel storage performance is investigated, and multiple synaptic characteristics, such as paired-pulse facilitation (PPF), spike-voltage-dependent plasticity (SVDP), spike-width-dependent plasticity (SWDP), spike-timing-dependent plasticity (STDP), and learning-forgetting, are simulated. The conductive mechanism of the device is analyzed and discussed with an analogy to natural volcanic rocks, which also have a large surface area, high adsorption, and high chemical inertness. An artificial neural network (ANN) based on the potentiation/depression characteristics is designed and analyzed theoretically, and a pattern recognition rate of 94.25% is accomplished. The strategy and results described in this paper provide insights into the development of nonvolatile memory devices boding well for the adoption of neuromorphic computing for image recognition.