The Interplay Between Molecular Weight and Crystal Orientation for Enhanced Ion Retention in Organic Electrochemical Synapses

Organic electrochemical synaptic transistors (OESTs) have emerged as a pivotal architecture for neuromorphic computing, enabling efficient artificial synaptic operations through ion-based synaptic weight updates. While significant strides have been made in diversifying organic materials for these devices, the fundamental interplay between molecular weight, crystal orientation, and ion retention has remained largely elusive, despite the profound influence of molecular weight on polymer microstructure. In this study, we systematically investigate how molecular weight dictates the nonvolatile memory characteristics of OESTs by establishing a direct correlation between polymer chain dynamics and ion-trapping mechanisms. Through comprehensive structural and electrochemical analyses, including grazing-incidence wide-angle X-ray scattering (GIWAXS), we demonstrate that low-molecular-weight polymers favor an edge-on orientation that facilitates diffusion-dominated doping and enhances long-term synaptic weight stability. The fabricated devices successfully emulated biological synaptic properties such as paired-pulse facilitation (PPF) and long-term potentiation/depression (LTP/D). Based on these characteristics, we achieved an accuracy comparable to that of an ideal device in electrocardiogram (ECG) pattern recognition simulations. These results highlight that molecular weight control is a key factor in establishing the characteristics of artificial synapses and provide design strategies for organic-based devices.

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Small
Published
2026-09-03
DOI
https://doi.org/10.1002/smll.75609
Primary Topic
Advanced Memory and Neural Computing
Type
article
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The Interplay Between Molecular Weight and Crystal Orientation for Enhanced Ion Retention in Organic Electrochemical Synapses

Eunho Lee, Donghwa Lee, Sein Chung, Du Yeol Ryu et al.
Small
Advanced Memory and Neural Computing
article

The Interplay Between Molecular Weight and Crystal Orientation for Enhanced Ion Retention in Organic Electrochemical Synapses

Eunho Lee, Donghwa Lee, Sein Chung, Du Yeol Ryu, Seulki Song, Wonho Lee, Junho Sung, Soyoung Kim, Myeongjin An, Young Un Jeon, Hyeonse Kim, Minguen Park
article en

Abstract

Organic electrochemical synaptic transistors (OESTs) have emerged as a pivotal architecture for neuromorphic computing, enabling efficient artificial synaptic operations through ion-based synaptic weight updates. While significant strides have been made in diversifying organic materials for these devices, the fundamental interplay between molecular weight, crystal orientation, and ion retention has remained largely elusive, despite the profound influence of molecular weight on polymer microstructure. In this study, we systematically investigate how molecular weight dictates the nonvolatile memory characteristics of OESTs by establishing a direct correlation between polymer chain dynamics and ion-trapping mechanisms. Through comprehensive structural and electrochemical analyses, including grazing-incidence wide-angle X-ray scattering (GIWAXS), we demonstrate that low-molecular-weight polymers favor an edge-on orientation that facilitates diffusion-dominated doping and enhances long-term synaptic weight stability. The fabricated devices successfully emulated biological synaptic properties such as paired-pulse facilitation (PPF) and long-term potentiation/depression (LTP/D). Based on these characteristics, we achieved an accuracy comparable to that of an ideal device in electrocardiogram (ECG) pattern recognition simulations. These results highlight that molecular weight control is a key factor in establishing the characteristics of artificial synapses and provide design strategies for organic-based devices.

Small
Kumoh National Institute of Technology (KR), Seoul National University of Science and Technology (KR), Pohang University of Science and Technology (KR), Yonsei University (KR), Chungnam National University (KR)
Openalex Percentile: Top 19%
Advanced Memory and Neural Computing
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