基于Ga-Sn-O薄膜的神经网络多层交叉点突触

2018 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK) Pub Date : 2018-06-01 DOI:10.1109/IMFEDK.2018.8581900

Jumpei Shimura, Keisuke Ikushima, M. Kimura, T. Matsuda

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引用次数: 0

摘要

我们利用Ga-Sn-O (GTO)薄膜开发了用于神经网络的多层交叉点突触。20条中间层金属线和10条下层和上层金属线在玻璃基板上形成400个交叉点突触。通过对GTO薄膜连续施加恒定电压，电流值发生变化。结果，我们得到了可以应用于改进的Hebb学习规则的退化。

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Multilayer Cross-Point Synapses Using Ga-Sn-O Thin Films for Neural Network

We have developed multilayer cross-point synapses using Ga-Sn-O (GTO) thin films for neural networks. Twenty intermediate layer metal lines and ten lower and upper layer metal lines make 400 cross-point synapses integrated on a glass substrate. By continuously applying a constant voltage to the GTO thin films, the current value is changed. As a result, we obtained degradation that can be applied to the modified Hebb's learning rule.

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来源期刊

2018 IEEE International Meeting for Future of Electron Devices, Kansai (IMFEDK)

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