Dual‐Mode Neuromorphic Device Based on MoS₂ 2T0C DRAM for Neurons and Synapses

Abstract Artificial neural networks (ANNs) have attracted significant attention for their potential in low‐power computation by mimicking biological neural systems. However, hardware implementation of ANNs remains challenging, especially when integrating synaptic and neuronal functionalities while ensuring high density, power efficiency, and bio‐inspired dynamics. This work proposes wafer‐scale MoS₂ 2T0C devices that enable stable switching between volatile and quasi‐non‐volatile modes. As a synapse, the device exhibits an effective retention time exceeding 40 s, high linearity in weight distribution, fast programming speed of 500 ns, and low write energy consumption of 6 pJ. As a neuron, the device mimics leaky‐integrate‐and‐fire (LIF) neuronal behavior, with a spike energy consumption of 15 pJ. By leveraging an array of these dual‐mode neuromorphic devices, a spiking neural network (SNN) model is constructed that achieved recognition accuracy of 92.88% on the MNIST handwritten digit dataset after 100 training epochs.