神经形态工程学
铁电性
MNIST数据库
材料科学
人工神经网络
计算机科学
光电子学
二极管
电介质
卷积神经网络
神经科学
人工智能
心理学
作者
Fan Ye,Xin‐Gui Tang,Jiaying Chen,Wen-Min Zhong,Li Zhang,Yanping Jiang,Qiu‐Xiang Liu
摘要
Brain-like neuromorphic computing networks based on the human brain information processing model are gradually breaking down the memory barriers caused by traditional computing frameworks. The brain-like neural system consists of electronic synapses and neurons. The multiple ferroelectric polarization switching modulated by the external electric field is well suited to simulate artificial neural synaptic weights. Therefore, ferroelectric diodes' (FDs) synapses have great advantages in building highly reliable and energy-efficient artificial neural networks. In this paper, we demonstrate the FDs synapse, which is based on rare-earth metal-doped BaTiO3 ferroelectric dielectric layer materials. This performs short-term and long-term synaptic plasticity behaviors by modulating synaptic weights using pulsed stimuli to polarize or flip ferroelectric films. In addition, convolutional neural networks were constructed on the MNIST dataset and the Fashion-MNIST dataset to check the feasibility of the device in simulating bio-visual recognition. The results expand the application of FDs' devices in the intersection of artificial intelligence and bioscience.
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