Designing Asymmetric Memristive Behavior in Proton Mixed Conductors for Neuromorphic Applications

Abstract Solid‐state devices incorporating mobile ionic species, such as protons, are promising platforms for next‐generation neuromorphic computing. Unlike conventional electronic devices, whose behavior can be predicted using well‐established band diagrams, the design in the mixed conduction regime of ions and electrons remains challenging due to the complex interplay between electronic and ionic transport mechanisms. In this work, a fundamental design principle is elucidated by leveraging the proton–electron coupling at the interface of proton‐conducting mixed ionic‐electronic conductors. This approach enables the realization of unconventional asymmetric memristive behavior, exhibiting a pronounced temporal asymmetry with a gradual conductance increase by voltage and a rapid relaxation to the initial state without voltage, like a stretched rubber band suddenly let go. Such rubber‐like memristive behaviors are advantageous for read‐write operation with only two voltage levels simplifying circuit integration of neuromorphic information processing. Moreover, this study demonstrates that the device's operational state can be reversibly modulated by ambient humidity, enabling ON/OFF switching through external chemical cues. This behavior mimics hormone‐like modulation observed in biological neural systems, highlighting the potential for environmentally responsive neuromorphic architectures. These findings offer a robust framework for the rational design of bioinspired ionic devices, opening new pathways for multifunctional platforms based on protonic materials.

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Publication Details

Journal
Advanced Functional Materials
Published
2025-11-23
DOI
https://doi.org/10.1002/adfm.202525988
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Designing Asymmetric Memristive Behavior in Proton Mixed Conductors for Neuromorphic Applications

Takeaki Yajima, Nada H. A. Besisa, Satya Prakash Pati, Yifan Geng et al.
Advanced Functional Materials
Advanced Memory and Neural Computing
article

Designing Asymmetric Memristive Behavior in Proton Mixed Conductors for Neuromorphic Applications

Takeaki Yajima, Nada H. A. Besisa, Satya Prakash Pati, Yifan Geng, Satoshi Hamasuna, Masaki Tsuji, Yoshihiro Furue
article en

Abstract

Abstract Solid‐state devices incorporating mobile ionic species, such as protons, are promising platforms for next‐generation neuromorphic computing. Unlike conventional electronic devices, whose behavior can be predicted using well‐established band diagrams, the design in the mixed conduction regime of ions and electrons remains challenging due to the complex interplay between electronic and ionic transport mechanisms. In this work, a fundamental design principle is elucidated by leveraging the proton–electron coupling at the interface of proton‐conducting mixed ionic‐electronic conductors. This approach enables the realization of unconventional asymmetric memristive behavior, exhibiting a pronounced temporal asymmetry with a gradual conductance increase by voltage and a rapid relaxation to the initial state without voltage, like a stretched rubber band suddenly let go. Such rubber‐like memristive behaviors are advantageous for read‐write operation with only two voltage levels simplifying circuit integration of neuromorphic information processing. Moreover, this study demonstrates that the device's operational state can be reversibly modulated by ambient humidity, enabling ON/OFF switching through external chemical cues. This behavior mimics hormone‐like modulation observed in biological neural systems, highlighting the potential for environmentally responsive neuromorphic architectures. These findings offer a robust framework for the rational design of bioinspired ionic devices, opening new pathways for multifunctional platforms based on protonic materials.

Advanced Functional Materials
Kyushu University (JP), North Eastern Hill University (IN)
National Institute of Advanced Industrial Science and Technology, University of Tokyo, Japan Society for the Promotion of Science
Openalex Percentile: Top 64%
Advanced Memory and Neural Computing
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