Structure–Transport–Ion Retention Coupling for Enhanced Nonvolatile Artificial Synapses

Abstract Organic electrochemical synaptic transistors (OESTs) are emerging as promising candidates for neuromorphic devices capable of exhibiting high‐performance synaptic characteristics. While previous studies primarily focus on enhancing synaptic properties through controlling morphology at the electrolyte/semiconductor interface, efforts to correlate molecular‐level structural design with synaptic performance remain limited. In particular, from a molecular design perspective, the influence of the conjugated backbone, serving as the primary pathway for charge transport, on synaptic function remains poorly understood. Here, an effective molecular design strategy is proposed that facilitates efficient charge transfer while enhancing synaptic retention characteristics. This approach incorporates nitrogen into the benzothiadiazole unit of the acceptor moiety within the donor–acceptor structure of the polymer. N‐incorporated OESTs exhibit excellent synaptic retention properties enabled by deep ion penetration. This improvement in synaptic characteristics is attributed to the enhanced charge transport resulting from nitrogen incorporation, as confirmed by various ion doping and de‐doping analyses. Furthermore, improved long‐term potentiation/depression (LTP/D) characteristics are achieved through precise control of ion diffusion dynamics. These results demonstrate that regulating charge transfer within the electronic structure at the conjugated backbone level has a decisive impact on synaptic performance, suggesting a new molecular design direction for future neuromorphic applications.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-12-02
DOI
https://doi.org/10.1002/adfm.202521650
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Structure–Transport–Ion Retention Coupling for Enhanced Nonvolatile Artificial Synapses

Eunho Lee, Sein Chung, C. Han, Donghwa Lee et al.
Advanced Functional Materials
Advanced Memory and Neural Computing
article

Structure–Transport–Ion Retention Coupling for Enhanced Nonvolatile Artificial Synapses

Eunho Lee, Sein Chung, C. Han, Donghwa Lee, Jin‐Sung Kim, Junho Sung
article en

Abstract

Abstract Organic electrochemical synaptic transistors (OESTs) are emerging as promising candidates for neuromorphic devices capable of exhibiting high‐performance synaptic characteristics. While previous studies primarily focus on enhancing synaptic properties through controlling morphology at the electrolyte/semiconductor interface, efforts to correlate molecular‐level structural design with synaptic performance remain limited. In particular, from a molecular design perspective, the influence of the conjugated backbone, serving as the primary pathway for charge transport, on synaptic function remains poorly understood. Here, an effective molecular design strategy is proposed that facilitates efficient charge transfer while enhancing synaptic retention characteristics. This approach incorporates nitrogen into the benzothiadiazole unit of the acceptor moiety within the donor–acceptor structure of the polymer. N‐incorporated OESTs exhibit excellent synaptic retention properties enabled by deep ion penetration. This improvement in synaptic characteristics is attributed to the enhanced charge transport resulting from nitrogen incorporation, as confirmed by various ion doping and de‐doping analyses. Furthermore, improved long‐term potentiation/depression (LTP/D) characteristics are achieved through precise control of ion diffusion dynamics. These results demonstrate that regulating charge transfer within the electronic structure at the conjugated backbone level has a decisive impact on synaptic performance, suggesting a new molecular design direction for future neuromorphic applications.

Advanced Functional Materials
Seoul National University of Science and Technology (KR), Pohang University of Science and Technology (KR), Massachusetts Institute of Technology (US)
Openalex Percentile: Top 62%
Advanced Memory and Neural Computing
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