Recently, several types of lead halide perovskites have been demonstrated as active layers in resistive switching memory or artificial synaptic devices for neuromorphic computing applications. However, the thermal instability and toxicity of lead halide perovskites severely restricted their further practical applications. Herein, the environmentally friendly and uniform Cs3Cu2I5 perovskite films are introduced to act as the active layer in the Ag/Cs3Cu2I5/ITO memristor. Generally, the Ag ions could react with iodide ions and form AgI x compounds easily, so the Ag/PMMA/Cs3Cu2I5/ITO memristor was designed by employing the ultrathin polymethylmethacrylate (PMMA) layer to avoid the direct contact between the top Ag electrode and Cs3Cu2I5 perovskite films. After optimization, the obtained memristor demonstrated bipolar resistive switching with low operating voltage (< ±1 V), large on/off ratio (102), stable endurance (100 cycles), and long retention (>104 s). Additionally, biological synaptic behaviors including long-term potentiation and long-term depression have been investigated. By using the MNIST handwritten recognition data set, the handwritten recognition rate based on experimental data could reach 94%. In conclusion, our work provides the opportunity of exploring the novel application for the development of next-generation neuromorphic computing based on lead-free halide perovskites.

Herein, we employed lead-free Cs3Cu2I5 perovskite films as the functional layers to construct Al/Cs3Cu2I5/ITO memory devices and systematically investigated the impact on the corresponding resistive switching (RS) performance via adding different amounts of hydroiodic acid (HI) in Cs3Cu2I5 precursor solution. The results demonstrated that the crystallinity and morphology of the Cs3Cu2I5 films can be improved and the resistive switching performance can be modulated by adding an appropriate amount of HI. The obtained Cs3Cu2I5 films by adding 5 μL HI exhibit the fewest lattice defects and flattest surface (RMS = 13.3 nm). Besides, the memory device, utilizing the optimized films, has a low electroforming voltage (1.44 V), a large on/off ratio (∼65), and a long retention time (104 s). The RS performance impacted by adding HI, providing a scientific strategy for improving the RS performance of iodine halide perovskite-based memories.

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Brain-inspired computing is believed to have a better performance compared with the conventional von Neumann computing. The synaptic electronic device is the most important component of a neuromorphic circuit. In this study, we present an aluminum nitride (AlN) based memristor as the synaptic weight element in a functional neural network for handwritten digit recognition. Reliable and stable resistive switching behaviors were successfully demonstrated in the AlN based memristor. Moreover, it also possesses excellent features for neuromorphic applications such as long retention (>104 s), and multi-level storage. Continuous and smooth gradual set and reset switching transition can be modulated by applying appropriate compliance current limits and reset stop voltages. We particularly examined long-term potentiation and long-term depression and improved the linearity by optimizing pulse response conditions. Finally, the symmetric and linear synaptic behaviors which can be utilized in a neural network simulation are obtained. Simulations using the MNIST handwritten recognition data set prove that the AlN based memristor can operate with an online learning accuracy of 95%. Our work suggests AlN based memristor has potential for using as an electronic synapse in future neuromorphic systems.