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

Abstract Negative differential resistance (NDR) devices based on transition metal oxides, such as NbO 2 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 NbO 2 ‐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 NbO 2 layer and the bottom electrode. This SnSe barrier effectively suppresses thermal dissipation, enabling lower switching voltages and currents in SnSe/NbO 2 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 NbO y /NbO 2 control sample. Furthermore, the preserved hysteresis window of SnSe/NbO 2 devices enables the construction of oscillatory neural networks (ONNs) with higher oscillator capacity and computational capability than those based on NbO y /NbO 2 devices. These findings shed light on thermal engineering for the development of low‐power NbO 2 ‐based NDR devices, paving the way for energy‐efficient neuromorphic systems and high‐capacity ONNs.