Reliable Memristor Crossbar Array Based on 2D Layered Nickel Phosphorus Trisulfide for Energy-Efficient Neuromorphic...

Designing reliable and energy-efficient memristors for artificial synaptic arrays in neuromorphic computing beyond von Neumann architecture remains a challenge. Here, memristors based on emerging layered nickel phosphorus trisulfide (NiPS₃ ) are reported that exhibit several favorable characteristics, including uniform bipolar nonvolatile switching with small operating voltage (<1 V), fast switching speed (< 20 ns), high On/Off ratio (>10² ), and the ability to achieve programmable multilevel resistance states. Through direct experimental evidence using transmission electron microscopy and energy dispersive X-ray spectroscopy, it is revealed that the resistive switching mechanism in the Ti/NiPS₃ /Au device is related to the formation and dissolution of Ti conductive filaments. Intriguingly, further investigation into the microstructural and chemical properties of NiPS₃ suggests that the penetration of Ti ions is accompanied by the drift of phosphorus-sulfur ions, leading to induced P/S vacancies that facilitate the formation of conductive filaments. Furthermore, it is demonstrated that the memristor, when operating in quasi-reset mode, effectively emulates long-term synaptic weight plasticity. By utilizing a crossbar array, multipattern memorization and multiply-and-accumulate (MAC) operations are successfully implemented. Moreover, owing to the highly linear and symmetric multiple conductance states, a high pattern recognition accuracy of ≈96.4% is demonstrated in artificial neural network simulation for neuromorphic systems.