Artificial synapse based on Co₃O₄ nanosheets for high-accuracy pattern recognition

Two-dimensional (2D) metal oxides are promising candidates for constructing neuromorphic systems because of their intriguing physical properties, such as atomic thinness and ionic activity. In this work, Co₃O₄ nanosheets were synthesized using a solvothermal method and integrated into artificial synapses. Based on the synaptic plasticity of the Co₃O₄ nanosheet-based memristive device, an artificial neural network (ANN) was designed and tested. A recognition accuracy of approximately 96% was achieved for the Modified National Institute of Standards and Technology (MNIST) handwritten digit classification task using this ANN. These results highlight the potential of Co₃O₄ nanosheet-based artificial synapses and Al/Co₃O₄ nanosheet/ITO memristor devices as excellent material candidates for neuromorphic hardware.