Tunable Linear Weight Modulation via Ion Dynamics in Electrolyte‐Gated Synaptic Transistors: Molecular Dynamics and Experimental Insights into Ion Concentration Effects
Abstract Electrolyte‐gated transistors (EGTs) are promising candidates for low‐power and energy‐efficient artificial synapses utilizing rapid ion‐mediated electrostatic and electrochemical reactions. However, the correlation between sub‐nanosecond ion transport and linear conductance modulation remains elusive when analyzed solely by macroscopic electrical characterization. Here, molecular dynamics (MD) simulations are integrated with experiments using Li‐EGTs at different ion concentrations. MD simulations quantify Li⁺ displacement, coordination environments, and ionic conductivity in poly(ethylene oxide) electrolytes, revealing suppressed Li⁺ diffusivity and reorganization of the ethylene oxide (EO) solvation structure at an [EO]:[Li⁺] ratio of 8:1. EGTs are fabricated with different EO ratios to investigate the transition regime. Electrochemical impedance spectroscopy, optical microscopy, and X‐ray diffraction confirm the MD‑predicted ion dynamic transition at 8:1 EO ratio and identify phase crystallization at the ultrahigh 2:1 ratio. Consequently, Li‐EGTs fabricated with the optimized 2:1 EO ratio successfully achieve an accuracy of 89.51% in neural network training. This study bridges atomic‑scale insights and device performance, offering a systematic, precision‑engineered framework for complex spatiotemporal ion dynamics in artificial synaptic EGTs.
Authors
- Yeonseo Kim
- Haeyeon Lee (ORCID: https://orcid.org/0009-0004-6860-7869)
- Tengfei Luo (ORCID: https://orcid.org/0000-0003-3940-8786)
- Sobin Alosious (ORCID: https://orcid.org/0000-0003-3579-4747)
- Minho Jin (ORCID: https://orcid.org/0000-0001-8074-1391)
Institutions
- University of Notre Dame (US)
- Seoul National University of Science and Technology (KR)
- Seoul National University (KR)
Publication Details
- Journal
- Advanced Functional Materials
- Published
- 2025-11-12
- DOI
- https://doi.org/10.1002/adfm.202517100
- Primary Topic
- Advanced Memory and Neural Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00