Graphene‐Assisting Nonvolatile Vanadium Dioxide Phase Transition for Neuromorphic Machine Vision

Abstract As a photoinduced and electro‐induced phase change material, VO₂ undergoes a transition from an insulating phase to a metallic phase under photoelectric stimulation, accompanied by strong lattice distortion and band changes. This characteristic of simultaneously responding to optical and electrical signals provides a basis for simulating biological vision systems, but the volatility of phase transition challenges the Long‐term memory of neural networks. Here, a phase transition‐regulated artificial photoelectric synapse based on VO₂/graphene heterostructure is proposed. Graphene serves as an electron exchange center, amplifying weak signals generated by VO₂ phase transitions and achieving nonvolatile properties. Using the energy band change of VO₂ before and after the photoinduced phase transition and the electron exchange with graphene, synaptic devices can be regulated by optical signals. The modulation of gate voltage on the Fermi level of graphene leads to the phase transition of VO₂, thereby achieving the regulation of synapses by electrical signals. Extract the electrical conductivity difference between two equivalent synapses as synaptic weight values to train a three‐layer neural network. The trained neural network achieves high recognition accuracy and noise resistance for handwritten digits, which is of great significance for the application of artificial optoelectronic synapses in neural morphology calculation.