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- Title
A Bioinspired Ultra Flexible Artificial van der Waals 2D‐MoS<sub>2</sub> Channel/LiSiO<sub>x</sub> Solid Electrolyte Synapse Arrays via Laser‐Lift Off Process for Wearable Adaptive Neuromorphic Computing.
- Authors
Hwang, Yunjeong; Park, Byeongjin; Hwang, Seungkwon; Choi, Soo‐Won; Kim, Han Seul; Kim, Ah Ra; Choi, Jin Woo; Yoon, Jongwon; Kwon, Jung‐Dae; Kim, Yonghun
- 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 MoS2 channel and LiSiOx (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‐MoS2 channel and LiSiOx 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.
- Subjects
ADAPTIVE computing systems; SOLID electrolytes; WEARABLE technology; ARTIFICIAL neural networks; LONG-term potentiation; SOLID state batteries; ELECTRONIC equipment
- Publication
Small Methods, 2023, Vol 7, Issue 7, p1
- ISSN
2366-9608
- Publication type
Article
- DOI
10.1002/smtd.202201719