Thermal Engineering of NbO2-Based Memristor for Low-Power and High-Capacity Oscillatory Neural Networks

Negative differential resistance (NDR) devices based on transition metal oxides, such as NbO2 memristors, inherently exhibit multiple nonlinear dynamics that have garnered considerable interest in emulating neuronal functions. However, the challenge of simultaneously reducing switching voltages and currents while maintaining a stable hysteresis window limits the energy efficiency and computational functionality of NbO2-based oscillatory systems. Here, a thermal engineering strategy is proposed to break this dilemma, in which a SnSe layer with low thermal conductivity and high electrical conductivity is inserted between the NbO2 layer and the bottom electrode. This SnSe barrier effectively suppresses thermal dissipation, enabling lower switching voltages and currents in SnSe/NbO2 devices without compromising their hysteresis window. By using such a thermally optimized device to construct oscillator circuits, a 45% reduction in energy consumption per spike is achieved compared to the NbOy/NbO2 control sample. Furthermore, the preserved hysteresis window of SnSe/NbO2 devices enables the construction of oscillatory neural networks (ONNs) with higher oscillator capacity and computational capability than those based on NbOy/NbO2 devices. These findings shed light on thermal engineering for the development of low-power NbO2-based NDR devices, paving the way for energy-efficient neuromorphic systems and high-capacity ONNs.