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

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. An artificial optoelectronic synapse based on non-volatile phase transition VO2/graphene heterojunction regulation is reported. Under light and electrical stimulation, VO2 undergoes a phase transition and exchanges electrons with graphene to achieve a non-volatile phase transition. Such synapses offer a promising way to realize biomimetic perception, learning, and memory for neuromorphic vision.image