Regionally controlled ion-doping of organic electrochemical transistors for computing-memory co-integrated neuromorphic systems

Abstract Organic electrochemical transistors (OECTs), endued with processing and memory functionalities, present a remarkable potential for neuromorphic electronics. However, integrating processing and memory cores for neuromorphic systems is complicated by heterogeneous material compatibility and device architecture constraints. Here, we demonstrate a versatile regionally controlled ion-doping strategy for modulating the operational mode of OECTs between high-performance computing and non-volatile memory, without varying the materials or operating conditions. The key to this method is the use of inkjet-printed electrolytes with programmable 3D architectures, which can be precisely deposited onto the OECT channel, achieving a tunable thickness ranging from 100 nm to several tens of micrometers. By engineering the electrolyte’s spatial structure, we demonstrate two complementary OECT configurations: ion-rich OECTs with multilayer electrolytes achieve high stimulus-resolution capability of 1 ms for dynamic computation, and ion-deficient OECTs with single-layer electrolytes establish stable ion-trapping memristive states (300 s retention). Moreover, the integration of ion-rich and ion-deficient OECTs enables a neuromorphic circuit capable of simultaneous encoding and storage of alphanumeric information. This study presents a simple yet effective strategy that overcomes material compatibility constraints and simplifies circuit design, paving the way for highly integrated neuromorphic systems based on OECTs.

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Publication Details

Journal
npj Flexible Electronics
Published
2025-12-17
DOI
https://doi.org/10.1038/s41528-025-00511-7
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Regionally controlled ion-doping of organic electrochemical transistors for computing-memory co-integrated neuromorphic systems

C. Liu, Xiaoci Liang, Songjia Han, Mancheng Li et al.
npj Flexible Electronics
Advanced Memory and Neural Computing
article

Regionally controlled ion-doping of organic electrochemical transistors for computing-memory co-integrated neuromorphic systems

C. Liu, Xiaoci Liang, Songjia Han, Mancheng Li, Wenjing Zhang, Mengye Wang, X. Y. Lv, Chen Chen
article en

Abstract

Abstract Organic electrochemical transistors (OECTs), endued with processing and memory functionalities, present a remarkable potential for neuromorphic electronics. However, integrating processing and memory cores for neuromorphic systems is complicated by heterogeneous material compatibility and device architecture constraints. Here, we demonstrate a versatile regionally controlled ion-doping strategy for modulating the operational mode of OECTs between high-performance computing and non-volatile memory, without varying the materials or operating conditions. The key to this method is the use of inkjet-printed electrolytes with programmable 3D architectures, which can be precisely deposited onto the OECT channel, achieving a tunable thickness ranging from 100 nm to several tens of micrometers. By engineering the electrolyte’s spatial structure, we demonstrate two complementary OECT configurations: ion-rich OECTs with multilayer electrolytes achieve high stimulus-resolution capability of 1 ms for dynamic computation, and ion-deficient OECTs with single-layer electrolytes establish stable ion-trapping memristive states (300 s retention). Moreover, the integration of ion-rich and ion-deficient OECTs enables a neuromorphic circuit capable of simultaneous encoding and storage of alphanumeric information. This study presents a simple yet effective strategy that overcomes material compatibility constraints and simplifies circuit design, paving the way for highly integrated neuromorphic systems based on OECTs.

npj Flexible ElectronicsVol. 10(1)
South China Agricultural University (CN), Sun Yat-sen University (CN), National University of Defense Technology (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hunan Province
Openalex Percentile: Top 59%
Advanced Memory and Neural Computing
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