All‐Polymer Organic Electrochemical Synaptic Transistor With Controlled Ionic Dynamics for High‐Performance Wearable and Sustainable Reservoir Computing

Abstract Wearable near/in‐sensor neuromorphic computing is driving next‐generation human‐artificial intelligence (AI) interface, the Internet of Things, and intelligent robots, with reservoir computing (RC) playing a pivotal role in advancing AI hardware, yet its potential remains underexplored. Herein, an all‐polymer accumulation‐mode organic electrochemical synaptic transistor (OEST) is demonstrated with controlled ionic dynamics that can facilitate high‐performance wearable RC while allowing entire recyclability. A microporous glycolated conjugated polymer channel (P3gCPDT‐1gT2) affords current output above mA level at <1 V and enables both volatile and non‐volatile modes in combination with soft poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)/sorbitol electrodes and electrolytes (gelatin/glycerol). Particularly, modulation of the volatile OESTs as nonlinear dynamic reservoirs are elucidated by tuning ionic dynamics with applied voltages and gel compositions. Moreover, such an all‐polymer device exhibits synaptic performance preservation over >3000 bending cycles and allows convenient recyclability using eco‐friendly solvents. A wearable and sustainable RC system can be thus established by configuring the volatile units for data processing and the nonvolatile units as the weight storage in a single‐layer perceptron readout. Such a simple platform achieves up to 90% accuracy in voice recognition tasks under bending. Thus, this work facilitates the widespread integration of multifunctional organic electronic hardware for implementing intelligent information processing with low‐cost, body‐conformable, and eco‐benign features.

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Publication Details

Journal
Advanced Functional Materials
Published
2024-10-18
DOI
https://doi.org/10.1002/adfm.202415595
Citations
45
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
9.39

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article

All‐Polymer Organic Electrochemical Synaptic Transistor With Controlled Ionic Dynamics for High‐Performance Wearable and Sustainable Reservoir Computing

Zhuang Xie, Liuyuan Lan, Zhaoxian Li, Wan Yue et al.
45 citations
Advanced Functional Materials
Advanced Memory and Neural Computing
9.39
article

All‐Polymer Organic Electrochemical Synaptic Transistor With Controlled Ionic Dynamics for High‐Performance Wearable and Sustainable Reservoir Computing

Zhuang Xie, Liuyuan Lan, Zhaoxian Li, Wan Yue, Limei Zhang, Zhiyang Li, Yifei He, Riping Liu, Zhaolin Ge
article en
45 citations

Abstract

Abstract Wearable near/in‐sensor neuromorphic computing is driving next‐generation human‐artificial intelligence (AI) interface, the Internet of Things, and intelligent robots, with reservoir computing (RC) playing a pivotal role in advancing AI hardware, yet its potential remains underexplored. Herein, an all‐polymer accumulation‐mode organic electrochemical synaptic transistor (OEST) is demonstrated with controlled ionic dynamics that can facilitate high‐performance wearable RC while allowing entire recyclability. A microporous glycolated conjugated polymer channel (P3gCPDT‐1gT2) affords current output above mA level at <1 V and enables both volatile and non‐volatile modes in combination with soft poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)/sorbitol electrodes and electrolytes (gelatin/glycerol). Particularly, modulation of the volatile OESTs as nonlinear dynamic reservoirs are elucidated by tuning ionic dynamics with applied voltages and gel compositions. Moreover, such an all‐polymer device exhibits synaptic performance preservation over >3000 bending cycles and allows convenient recyclability using eco‐friendly solvents. A wearable and sustainable RC system can be thus established by configuring the volatile units for data processing and the nonvolatile units as the weight storage in a single‐layer perceptron readout. Such a simple platform achieves up to 90% accuracy in voice recognition tasks under bending. Thus, this work facilitates the widespread integration of multifunctional organic electronic hardware for implementing intelligent information processing with low‐cost, body‐conformable, and eco‐benign features.

Advanced Functional MaterialsVol. 35(8)
Sun Yat-sen University (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), State Key Laboratory of Optoelectronic Materials and Technology
National Natural Science Foundation of China, Sun Yat-sen University, Natural Science Foundation of Guangdong Province, Fundamental Research Funds for the Central Universities
Responsible consumption and production
Openalex Percentile: Top 1%
Advanced Memory and Neural Computing
9.39
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