A Large-Dynamic-Range Violet Phosphorus Heterostructure Optoelectronic Synapse for High-Complexity Neuromorphic Computing

Neuromorphic computing can efficiently handle data-intensive tasks and address the redundant data interaction required by traditional von Neumann architectures. Synaptic devices are essential components for neuromorphic computation. For high computational accuracy, synaptic devices need to retain good conductance linearity, but this leads to a limited dynamic range (10 ~ 100) and weight states, which impedes their processing of high-complexity tasks and restricts further advances in accuracy. Two-dimensional materials, such as transition metal disulfides and phosphorene, hold promise for the construction of synaptic devices with large dynamic ranges due to their strong light-matter interactions, while the stability of phosphorene remains an issue. Here, for the first time, we use the most stable violet phosphorene for device applications. The combination of violet phosphorene and molybdenum disulfide demonstrates an optoelectronic synapse with a record dynamic range of over 10 6 , benefiting from a significant threshold shift due to charge transfer and trapping in the heterostructure. Remarkable synaptic properties are demonstrated, including 128 distinguishable conductance states, electro-optical dependent plasticity, short-term paired-pulse facilitation, and long-term potentiation/depression. High-precision image classification with accuracies of 95.23% and 79.65% is achieved for MNIST and high-complexity Fashion-MNIST datasets, which is close to the ideal device (95.47%, 79.95%), indicating the potential of dynamic range and multi-states for optimizing accuracy. This work fills the device application gap of violet phosphorene and provides a strategy for building synaptic devices with large dynamic range to facilitate neuromorphic computing.