Journal
ADVANCED MATERIALS TECHNOLOGIES
Volume 4, Issue 8, Pages -Publisher
WILEY
DOI: 10.1002/admt.201900151
Keywords
artificial synapses; cotton fabric; neuromorphic devices; polydopamine; resistive random access memory (RRAM)
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Funding
- Center for Integrated Smart Sensors - Ministry of Science, ICT & Future Planning as Global Frontier Project [CISS-2011-0031848]
- National Research Foundation (NRF) of Korea - Ministry of Education [2018R1A6A3A03013435, 2018R1A6A1A03025708]
- IDEC (EDA Tool, MPW)
- NRF of Korea - Korea government (MSIT) [2018R1A2A3075302, 2018R1C1B5045747]
- National Research Foundation of Korea [2018R1A6A3A03013435, 2018R1C1B5045747, 2018R1A6A1A03025708] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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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 approximate to 81% and the potential for application in wearable and flexible electronic platforms is demonstrated.
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