神经形态工程学
计算机科学
人工神经网络
材料科学
人工智能
突触
多层感知器
感知器
光电子学
神经科学
生物
作者
Jing Wang,Yue Wang,Siqin Li,Dunan Hu,Qiujiang Chen,Fei Zhuge,Zhizhen Ye,Xiaodong Pi,Jianguo Lü
标识
DOI:10.1002/adfm.202312444
摘要
Abstract Artificial synapse devices are dedicated to overcoming the von Neumann bottleneck. Adopting light signals in visual information processing and computing is vital for developing next‐generation artificial neuromorphic systems. A strategy to construct all‐optically controlled artificial synaptic devices based on full oxides with amorphous ZnAlSnO/SnO heterojunction in a two‐terminal planar configuration is proposed. All synaptic behaviors are operated in the visible optical pathway, with excitatory synapse under red (635 nm) light and inhibitory synapse under green (532 nm) and blue (405 nm) lights. Based on the different inhibitory effects, two modes of long‐term depression (LTD) and RESET processes can be implemented through green and blue lights, respectively. The energy consumption of an event can be as low as 0.75 pJ. A three‐layer perceptron model is designed to classify 28 × 28‐pixel handwritten digital images and performed supervised learning using a backpropagation algorithm, demonstrating the bio‐visually inspired neuromorphic computing with a training accuracy of 92.74%. The all‐optically controlled artificial synapses with write/erasure behaviors in visible RGB region and rational microelectronic process, as presented in this work, are essential in developing future artificial neuromorphic systems and highlight the huge potential of next‐generation computer systems.
科研通智能强力驱动
Strongly Powered by AbleSci AI