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Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor

Author

Listed:
  • Xudong Ji

    (The University of Hong Kong
    Northwestern University
    Northwestern University)

  • Bryan D. Paulsen

    (Northwestern University)

  • Gary K. K. Chik

    (The University of Hong Kong
    Advanced Biomedical Instrumentation Centre, Hong Kong Science Park)

  • Ruiheng Wu

    (Northwestern University)

  • Yuyang Yin

    (The University of Hong Kong)

  • Paddy K. L. Chan

    (The University of Hong Kong
    Advanced Biomedical Instrumentation Centre, Hong Kong Science Park)

  • Jonathan Rivnay

    (Northwestern University
    Northwestern University)

Abstract

Associative learning, a critical learning principle to improve an individual’s adaptability, has been emulated by few organic electrochemical devices. However, complicated bias schemes, high write voltages, as well as process irreversibility hinder the further development of associative learning circuits. Here, by adopting a poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran composite as the active channel, we present a non-volatile organic electrochemical transistor that shows a write bias less than 0.8 V and retention time longer than 200 min without decoupling the write and read operations. By incorporating a pressure sensor and a photoresistor, a neuromorphic circuit is demonstrated with the ability to associate two physical inputs (light and pressure) instead of normally demonstrated electrical inputs in other associative learning circuits. To unravel the non-volatility of this material, ultraviolet-visible-near-infrared spectroscopy, X-ray photoelectron spectroscopy and grazing-incidence wide-angle X-ray scattering are used to characterize the oxidation level variation, compositional change, and the structural modulation of the poly(3,4-ethylenedioxythiophene):tosylate/Polytetrahydrofuran films in various conductance states. The implementation of the associative learning circuit as well as the understanding of the non-volatile material represent critical advances for organic electrochemical devices in neuromorphic applications.

Suggested Citation

  • Xudong Ji & Bryan D. Paulsen & Gary K. K. Chik & Ruiheng Wu & Yuyang Yin & Paddy K. L. Chan & Jonathan Rivnay, 2021. "Mimicking associative learning using an ion-trapping non-volatile synaptic organic electrochemical transistor," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22680-5
    DOI: 10.1038/s41467-021-22680-5
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    Cited by:

    1. Xiaosong Wu & Shaocong Wang & Wei Huang & Yu Dong & Zhongrui Wang & Weiguo Huang, 2023. "Wearable in-sensor reservoir computing using optoelectronic polymers with through-space charge-transport characteristics for multi-task learning," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. Matteo Cucchi & Anton Weissbach & Lukas M. Bongartz & Richard Kantelberg & Hsin Tseng & Hans Kleemann & Karl Leo, 2022. "Thermodynamics of organic electrochemical transistors," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    3. Chengpeng Jiang & Jiaqi Liu & Yao Ni & Shangda Qu & Lu Liu & Yue Li & Lu Yang & Wentao Xu, 2023. "Mammalian-brain-inspired neuromorphic motion-cognition nerve achieves cross-modal perceptual enhancement," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    4. Pengzhan Li & Mingzhen Zhang & Qingli Zhou & Qinghua Zhang & Donggang Xie & Ge Li & Zhuohui Liu & Zheng Wang & Erjia Guo & Meng He & Can Wang & Lin Gu & Guozhen Yang & Kuijuan Jin & Chen Ge, 2024. "Reconfigurable optoelectronic transistors for multimodal recognition," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    5. Padinhare Cholakkal Harikesh & Chi-Yuan Yang & Deyu Tu & Jennifer Y. Gerasimov & Abdul Manan Dar & Adam Armada-Moreira & Matteo Massetti & Renee Kroon & David Bliman & Roger Olsson & Eleni Stavrinidou, 2022. "Organic electrochemical neurons and synapses with ion mediated spiking," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    6. Yang Gao & Yuchen Zhou & Xudong Ji & Austin J. Graham & Christopher M. Dundas & Ismar E. Miniel Mahfoud & Bailey M. Tibbett & Benjamin Tan & Gina Partipilo & Ananth Dodabalapur & Jonathan Rivnay & Ben, 2024. "A hybrid transistor with transcriptionally controlled computation and plasticity," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    7. Xudong Ji & Xuanyi Lin & Jonathan Rivnay, 2023. "Organic electrochemical transistors as on-site signal amplifiers for electrochemical aptamer-based sensing," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    8. Pei-Yu Huang & Bi-Yi Jiang & Hong-Ji Chen & Jia-Yi Xu & Kang Wang & Cheng-Yi Zhu & Xin-Yan Hu & Dong Li & Liang Zhen & Fei-Chi Zhou & Jing-Kai Qin & Cheng-Yan Xu, 2023. "Neuro-inspired optical sensor array for high-accuracy static image recognition and dynamic trace extraction," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    9. Ji Hwan Kim & Roman Halaksa & Il-Young Jo & Hyungju Ahn & Peter A. Gilhooly-Finn & Inho Lee & Sungjun Park & Christian B. Nielsen & Myung-Han Yoon, 2023. "Peculiar transient behaviors of organic electrochemical transistors governed by ion injection directionality," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    10. Peiyun Li & Junwei Shi & Yuqiu Lei & Zhen Huang & Ting Lei, 2022. "Switching p-type to high-performance n-type organic electrochemical transistors via doped state engineering," Nature Communications, Nature, vol. 13(1), pages 1-8, December.

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