Nanolayered NbO₂-Based Dynamic Memristor for Leaky Integrate and Fire Neuron

Dynamic memristors with short-term memory have been widely researched for their high biomimetic properties to mimic the leaky and integration functions of biological neurons. However, the limited reliability and poor switching window still hinder the implementation of dynamic memristors in a neuromorphic system. Here, we propose a highly reliable dynamic memristor with nanolayered NbO 2 material based on the transition effect between two-electron emission processes. The introduced additional oxygen defects effectively enhance the electron emission effect and therefore enlarge the switching window over 10 7, accompanied by a low variation below 2%. Meanwhile, the designed conduction transition avoids the structural damage and the direct connection between two electrodes, benefiting the high endurance (>10 9 ) and low operating energy (19 fJ). Subsequently, we confirm the conduction transition mechanism during the dynamic behavior by data analysis, device surface expansion detection, and oxygen-controlled fabrication. The proposed mechanism is mathematically verified by a constructed compact model, which can be used not only to instruct the device fabrication but also to simulate neuron design. Based on the developed neuron with dynamic memristors, we construct a spiking neural network. The interaction between different aspects of the memristor in the network is discussed, which helps to achieve a reasonable compromise among the network accuracy (94.63%), energy consumption, and peripheral circuit requirements.