Decoupling the Retention–Energy Trade‐Off Through Structural Reorganization of Synaptic Polyelectrolytes for Nonvolatile Neuromorphic Devices

ABSTRACT Electrolyte‐gated synaptic transistors (EGSTs) are promising ion‐mediated artificial synapses, but their performance is constrained by a retention–energy trade‐off. Enhancing long‐term memory (LTM) retention often requires enhanced ion accessibility, which can induce excessive ion accumulation and increase energy consumption. Herein, we resolve this physical dilemma by rationally engineering the spatial architecture of a poly(maleic acid)‐poly(styrenesulfonate) (PMA‐PSS) copolymer electrolyte. The density of the bulky, hydrophilic PSS blocks is increased to impose steric hindrance and thermodynamic mismatch against TFSI − ions, thereby limiting excessive ion influx while preserving the injected ions through a confined ion–polymer coupling pathway. Such spatial confinement triggers a localized, persistent doping‐induced lattice expansion. This structural reorganization establishes a structural basis for suppressed TFSI − back‐diffusion by creating a sterically constrained ion–polymer environment that stabilizes the doped state. Consequently, the minimized excessive ion accumulation and suppressed post‐pulse ion back‐diffusion enable the high‐PSS‐content EGSTs to achieve exceptional LTM retention, low energy consumption, and endurance over 8,000 programming cycles. System‐level simulations reveal an image recognition accuracy of 87%, comparable to that of an ideal weight‐update model, confirming that our polyanion‐induced structural reorganization provides a promising blueprint for next‐generation, high‐fidelity artificial intelligence hardware.

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Journal
Small
Published
2026-09-12
DOI
https://doi.org/10.1002/smll.75766
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Decoupling the Retention–Energy Trade‐Off Through Structural Reorganization of Synaptic Polyelectrolytes for Nonvolatile Neuromorphic Devices

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Small
Advanced Memory and Neural Computing
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Decoupling the Retention–Energy Trade‐Off Through Structural Reorganization of Synaptic Polyelectrolytes for Nonvolatile Neuromorphic Devices

Dong Ha Lee, Goomin Kwon, Seong‐Min Bak, Hyoik Jang, Eunho Lee, Donghwa Lee, Sein Chung, Jinbo Kim, Jeonghun Kim, Jisoo Park, Junho Sung, Myeongjin An, Eunsung Hwang
article en

Abstract

ABSTRACT Electrolyte‐gated synaptic transistors (EGSTs) are promising ion‐mediated artificial synapses, but their performance is constrained by a retention–energy trade‐off. Enhancing long‐term memory (LTM) retention often requires enhanced ion accessibility, which can induce excessive ion accumulation and increase energy consumption. Herein, we resolve this physical dilemma by rationally engineering the spatial architecture of a poly(maleic acid)‐poly(styrenesulfonate) (PMA‐PSS) copolymer electrolyte. The density of the bulky, hydrophilic PSS blocks is increased to impose steric hindrance and thermodynamic mismatch against TFSI − ions, thereby limiting excessive ion influx while preserving the injected ions through a confined ion–polymer coupling pathway. Such spatial confinement triggers a localized, persistent doping‐induced lattice expansion. This structural reorganization establishes a structural basis for suppressed TFSI − back‐diffusion by creating a sterically constrained ion–polymer environment that stabilizes the doped state. Consequently, the minimized excessive ion accumulation and suppressed post‐pulse ion back‐diffusion enable the high‐PSS‐content EGSTs to achieve exceptional LTM retention, low energy consumption, and endurance over 8,000 programming cycles. System‐level simulations reveal an image recognition accuracy of 87%, comparable to that of an ideal weight‐update model, confirming that our polyanion‐induced structural reorganization provides a promising blueprint for next‐generation, high‐fidelity artificial intelligence hardware.

Small
Seoul National University of Science and Technology (KR), Pohang University of Science and Technology (KR), Yonsei University (KR)
National Research Foundation, National Research Foundation of Korea, Ministry of Science and ICT, South Korea
Affordable and clean energy
Openalex Percentile: Top 20%
Advanced Memory and Neural Computing
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