Volume 9, Issue 7 2401885
Research Article

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

Taehoon Kim

Taehoon Kim

Organic Nanoelectronics Laboratory and KNU Institute for Nanophotonics Applications (KINPA), Department of Chemical Engineering, Kyungpook National University, Daegu, 41566 Republic of Korea

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Woongki Lee

Woongki Lee

Department of Chemistry and Centre for Processable Electronics, Imperial College London, London, W12 0BZ UK

Organic Nanoelectronics Laboratory and KNU Institute for Nanophotonics Applications (KINPA), Department of Chemical Engineering, Kyungpook National University, Daegu, 41566 Republic of Korea

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Youngkyoo Kim

Corresponding Author

Youngkyoo Kim

Organic Nanoelectronics Laboratory and KNU Institute for Nanophotonics Applications (KINPA), Department of Chemical Engineering, Kyungpook National University, Daegu, 41566 Republic of Korea

E-mail: [email protected]

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First published: 15 December 2024

Abstract

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.

Conflict of Interest

The authors declare no conflict of interest.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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