Flexible ZnO Nanosheet‐Based Artificial Synapses Prepared by Low‐Temperature Process for High Recognition Accuracy Neuromorphic Computing

In neuromorphic computing networks, a flexible synaptic memristor with high recognition accuracy is highly desired. In this study, ZnO nanosheets (ZnO NS) embedded within a polymethyl methacrylate host material are used as the intermediate layer to prepare flexible synaptic memristor at a low‐temperature of 80 °C. The device shows excellent switching characteristics with low SET/RESET voltages (−0.4 V/0.4 V) and stable retention characteristic (104 s). By modulating the conductance continuously, the flexible synaptic memristor simulates typical synaptic plasticities, including excitation post‐synaptic current, paired‐pulse facilitation, and spike‐timing dependent plasticity. Especially, the neuromorphic system built from flexible ZnO NS‐based memristors achieves a high recognition accuracy up to 97.7% for handwriting digit. Under the influence of 5% Uniform noise and 5% Gaussian noise, recognition accuracies are maintained at 94.6% and 93.7%, respectively. These properties are well maintained even when bending 1000 times at a radius of 5 mm. The flexible ZnO NS‐based memristor shows great prospects in wearable devices and neural morphology calculation. The ZnO NS‐based artificial synapses are prepared at low temperature by a simple spin coating process. Combining various characterization techniques and data analysis, the weight transition process in artificial synapses is recognized, In addition, the typical synaptic plasticity and high recognition accuracy are realized and kept stable under a series of bending operations.