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一维有机人工多突触实现用于可穿戴神经形态应用的电子纺织神经网络。

One-dimensional organic artificial multi-synapses enabling electronic textile neural network for wearable neuromorphic applications.

作者信息

Ham Seonggil, Kang Minji, Jang Seonghoon, Jang Jingon, Choi Sanghyeon, Kim Tae-Wook, Wang Gunuk

机构信息

KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea.

Institute of Advanced Composite Materials, Korea Institute of Science and Technology, 92 Chudong-ro, Bongdong-eup, Wanju-gun, Jeollabuk-do 55324, Republic of Korea.

出版信息

Sci Adv. 2020 Jul 10;6(28). doi: 10.1126/sciadv.aba1178. Print 2020 Jul.

Abstract

One-dimensional (1D) devices are becoming the most desirable format for wearable electronic technology because they can be easily woven into electronic (e-) textile(s) with versatile functional units while maintaining their inherent features under mechanical stress. In this study, we designed 1D fiber-shaped multi-synapses comprising ferroelectric organic transistors fabricated on a 100-μm Ag wire and used them as multisynaptic channels in an e-textile neural network for wearable neuromorphic applications. The device mimics diverse synaptic functions with excellent reliability even under 6000 repeated input stimuli and mechanical bending stress. Various NOR-type textile arrays are formed simply by cross-pointing 1D synapses with Ag wires, where each output from individual synapse can be integrated and propagated without undesired leakage. Notably, the 1D multi-synapses achieved up to ~90 and ~70% recognition accuracy for MNIST and electrocardiogram patterns, respectively, even in a single-layer neural network, and almost maintained regardless of the bending conditions.

摘要

一维(1D)器件正成为可穿戴电子技术最理想的形式,因为它们可以轻松地与具有多功能单元的电子(e-)织物编织在一起,同时在机械应力下保持其固有特性。在本研究中,我们设计了由在100μm银线上制造的铁电有机晶体管组成的一维纤维状多突触,并将它们用作可穿戴神经形态应用的电子织物神经网络中的多突触通道。该器件即使在6000次重复输入刺激和机械弯曲应力下也能模拟各种突触功能,具有出色的可靠性。通过将一维突触与银线交叉点简单地形成各种或非型织物阵列,其中来自各个突触的每个输出都可以集成和传播,而不会出现不期望的泄漏。值得注意的是,即使在单层神经网络中,一维多突触对MNIST和心电图模式的识别准确率分别高达约90%和70%,并且几乎不受弯曲条件的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3098/10662591/f2b43eb42674/aba1178-F1.jpg

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