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通过激光剥离工艺制备的仿生超灵活人工范德华 2D-MoS 通道/LiSiO 固态电解质突触阵列,用于可穿戴自适应神经形态计算。

A Bioinspired Ultra Flexible Artificial van der Waals 2D-MoS Channel/LiSiO Solid Electrolyte Synapse Arrays via Laser-Lift Off Process for Wearable Adaptive Neuromorphic Computing.

机构信息

Department of Energy and Electronic Materials, Surface Materials Division, Korea Institute of Materials Science (KIMS), 797 Changwondaero, Sungsan-gu, Changwon, Gyeongnam, 51508, Republic of Korea.

School of Materials Science and Engineering, Pusan National University, 2 Busandaehak-ro 63-beon-gil, Geumjeong-gu, Busan, 46241, Republic of Korea.

出版信息

Small Methods. 2023 Jul;7(7):e2201719. doi: 10.1002/smtd.202201719. Epub 2023 Mar 24.

DOI:10.1002/smtd.202201719
PMID:36960927
Abstract

Wearable electronic devices with next-generation biocompatible, mechanical, ultraflexible, and portable sensors are a fast-growing technology. Hardware systems enabling artificial neural networks while consuming low power and processing massive in situ personal data are essential for adaptive wearable neuromorphic edging computing. Herein, the development of an ultraflexible artificial-synaptic array device with concrete-mechanical cyclic endurance consisting of a novel heterostructure with an all-solid-state 2D MoS channel and LiSiO (lithium silicate) is demonstrated. Enabled by the sequential fabrication process of all layers, by excluding the transfer process, artificial van der Waals devices combined with the 2D-MoS channel and LiSiO solid electrolyte exhibit excellent neuromorphic synaptic characteristics with a nonlinearity of 0.55 and asymmetry ratio of 0.22. Based on the excellent flexibility of colorless polyimide substrates and thin-layered structures, the fabricated flexible neuromorphic synaptic devices exhibit superior long-term potentiation and long-term depression cyclic endurance performance, even when bent over 700 times or on curved surfaces with a diameter of 10 mm. Thus, a high classification accuracy of 95% is achieved without any noticeable performance degradation in the Modified National Institute of Standards and Technology. These results are promising for the development of personalized wearable artificial neural systems in the future.

摘要

具有下一代生物相容性、机械、超柔韧和可移植传感器的可穿戴电子设备是一项快速发展的技术。硬件系统能够在消耗低功率和处理大量原位个人数据的同时实现人工神经网络,这对于自适应可穿戴神经形态边缘计算至关重要。在此,展示了一种由具有全固态 2D MoS 通道和 LiSiO(硅酸锂)的新型异质结构组成的具有具体机械循环耐久性的超柔韧人工突触阵列器件的开发。通过各层的顺序制造工艺,通过排除转移工艺,人工范德华器件与 2D-MoS 通道和 LiSiO 固体电解质相结合,表现出优异的神经形态突触特性,具有 0.55 的非线性和 0.22 的非对称比。基于无色聚酰亚胺基底和薄层结构的优异柔韧性,所制造的柔性神经形态突触器件即使在弯曲 700 次以上或弯曲直径为 10mm 的曲面上也表现出优异的长时程增强和长时程压抑循环耐久性性能。因此,在修改后的国家标准与技术研究所中实现了 95%的高分类精度,而没有任何明显的性能下降。这些结果为未来个性化可穿戴人工神经网络系统的发展提供了希望。

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