A Bioinspired Ultra Flexible Artificial van der Waals 2D‐MoS2 Channel/LiSiOx Solid Electrolyte Synapse Arrays via Laser‐Lift Off Process for Wearable Adaptive Neuromorphic Computing

Author:

Hwang Yunjeong1,Park Byeongjin12,Hwang Seungkwon12,Choi Soo‐Won12,Kim Han Seul3,Kim Ah Ra1,Choi Jin Woo4,Yoon Jongwon1,Kwon Jung‐Dae1,Kim Yonghun1ORCID

Affiliation:

1. 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

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

3. Department of Advanced Materials Engineering Chungbuk National University 1 Chungdae‐ro, Seowon‐gu Cheongju 28644 Republic of Korea

4. Department of Data Information and Physics Kongju National University 56 Gongjudaehak‐ro Gongju Chungcheongnam‐do 32588 Republic ofKorea

Abstract

AbstractWearable 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.

Funder

National Research Foundation of Korea

National Research Council of Science and Technology

Publisher

Wiley

Subject

General Materials Science,General Chemistry

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