Flexible three-dimensional artificial synapse networks with correlated learning and trainable memory capability

• Published in: Nature communications Vol. 8; no. 1; pp. 752 - 9
• Main Authors: Wu, Chaoxing, Kim, Tae Whan, Choi, Hwan Young, Strukov, Dmitri B., Yang, J. Joshua
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29-09-2017; Nature Publishing Group; Nature Portfolio
• Subjects: 631/378/2591; 639/301/1005/1007; 639/925/927/1007; Algorithms; Complexity; Electronic devices; Electronic equipment; Electronics; Fabrication; Fault tolerance; Feasibility studies; Firing pattern; Humanities and Social Sciences; Humans; Information storage; Learning; Learning algorithms; Long-Term Potentiation; Machine learning; Memory; Metal oxides; Models, Neurological; multidisciplinary; Neural networks; Neural Networks (Computer); Neuronal Plasticity; Paired-pulse facilitation; Power consumption; Science; Science (multidisciplinary); Stacking; Synapses; Synapses - physiology; Synaptic depression; Tolerances
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-017-00803-1

If a three-dimensional physical electronic system emulating synapse networks could be built, that would be a significant step toward neuromorphic computing. However, the fabrication complexity of complementary metal-oxide-semiconductor architectures impedes the achievement of three-dimensional interconnectivity, high-device density, or flexibility. Here we report flexible three-dimensional artificial chemical synapse networks, in which two-terminal memristive devices, namely, electronic synapses (e-synapses), are connected by vertically stacking crossbar electrodes. The e-synapses resemble the key features of biological synapses: unilateral connection, long-term potentiation/depression, a spike-timing-dependent plasticity learning rule, paired-pulse facilitation, and ultralow-power consumption. The three-dimensional artificial synapse networks enable a direct emulation of correlated learning and trainable memory capability with strong tolerances to input faults and variations, which shows the feasibility of using them in futuristic electronic devices and can provide a physical platform for the realization of smart memories and machine learning and for operation of the complex algorithms involving hierarchical neural networks.
High-density information storage calls for the development of modern electronics with multiple stacking architectures that increase the complexity of three-dimensional interconnectivity. Here, Wu et al. build a stacked yet flexible artificial synapse network using layer-by-layer solution processing.