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Bioinspired Polydopamine‐Based Resistive‐Switching Memory on Cotton Fabric for Wearable Neuromorphic Device Applications
Author(s) -
Bae Hagyoul,
Kim Daewon,
Seo Myungsoo,
Jin Ik Kyeong,
Jeon SeungBae,
Lee Hye Moon,
Jung SooHo,
Jang Byung Chul,
Son Gyeongho,
Yu Kyoungsik,
Choi SungYool,
Choi YangKyu
Publication year - 2019
Publication title -
advanced materials technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.184
H-Index - 42
ISSN - 2365-709X
DOI - 10.1002/admt.201900151
Subject(s) - neuromorphic engineering , yarn , resistive random access memory , materials science , fabrication , resistive touchscreen , wearable computer , wearable technology , electrode , nanotechnology , computer science , electrical engineering , engineering , composite material , embedded system , artificial intelligence , artificial neural network , medicine , chemistry , alternative medicine , pathology
Fabric‐based electronic textiles (e‐textiles) have been investigated for the fabrication of high‐performance wearable electronic devices with good durability. Current e‐textile technology is limited by not only the delicate characteristics of the materials used but also by the fabric substrates, which impose constraints on the fabrication process. A polydopamine (PDA)‐intercalated fabric memory (PiFAM) with a resistive random access memory (RRAM) architecture is reported for fabric‐based wearable devices, as a step towards promising neuromorphic devices beyond the most simple. It is composed of interwoven cotton yarns. A solution‐based dip‐coating method is used to create a functional core–shell yarn. The outer shell is coated with PDA and the inner shell is coated with aluminum (Al) surrounding the core yarn, which serves as a backbone. The Al shell serves as the RRAM electrode and the PDA is a resistive‐switching layer. These functional yarns are then interwoven to create the RRAM in a lattice point. Untreated yarn is intercalated between adjacent functional yarns to avoid cell‐to‐cell interference. The PiFAM is applied to implement a synapse, and the feasibility of a neuromorphic device with pattern recognition accuracy of ≈81% and the potential for application in wearable and flexible electronic platforms is demonstrated.