Trivalent Ionic Molecular Bridges as Efficient Charge-Trapping Method for All-Solid-State Organic Synaptic Transistors toward Neuromorphic Signal Processing Applications

被引:0
作者
Kim, Taehoon [1 ,2 ]
Lee, Woongki [1 ,2 ,3 ,4 ]
Kim, Youngkyoo [1 ,2 ]
机构
[1] Kyungpook Natl Univ, Organ Nanoelect Lab, Dept Chem Engn, Daegu 41566, South Korea
[2] Kyungpook Natl Univ, KNU Inst Nanophoton Applicat KINPA, Daegu 41566, South Korea
[3] Imperial Coll London, Dept Chem, London W12 0BZ, England
[4] Imperial Coll London, Ctr Processable Elect, London W12 0BZ, England
基金
新加坡国家研究基金会;
关键词
all-solid-state; charge-trapping; neuromorphic signal processing; organic synaptic transistors; trivalent ionic molecular bridges; NETWORK;
D O I
10.1002/smtd.202401885
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
Achieving high retention of memory state is crucial in artificial synapse devices for neuromorphic computing systems. Of various memorizing methods, a charge-trapping method provides fast response times when it comes to the smallest size of electrons. Here, for the first time, it is demonstrated that trivalent molecular bridges with three ionic bond sites in the polymeric films can efficiently trap electrons in the organic synaptic transistors (OSTRs). A water-soluble polymer with sulfonic acid groups, poly(2-acrylamido-2-methyl-1-propanesulfonic acid) (PAMPSA), is reacted with melamine (ML) to make trivalent molecular bridges with three ionic bond sites for the application of charge-trapping and gate-insulating layer in all-solid-state OSTRs. The OSTRs with the PAMPSA:ML layers are operated at low voltages (<= 5 V) with pronounced hysteresis and high memory retention characteristics (ML = 25 mol%) and delivered excellent potentiation/depression performances under modulation of gate pulse frequency. The optimized OSTRs could successfully process analog (Morse/Braile) signals to synaptic current datasets for recognition/prediction logics with an accuracy of >95%, supporting strong potential as all-solid-state synaptic devices for neuromorphic systems in artificial intelligence applications.
引用
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页数:10
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