Abstract
In the field of neuromorphic computing, time-series prediction poses a significant challenge to recurrent neural network architectures, often requiring task-specific customization that limits the development of general-purpose computing platforms. In this work, we implement a physical echo-state network (ESN) using ambipolar organic–inorganic heterostructure transistors to form its reservoir layer. Leveraging the ambipolar nature of the transistor, its variable-resistance region enables sparse matrix operations, while the saturation region provides tanh-like nonlinearity, making it well-suited for implementing both synaptic weighting and neuronal activation in an ESN. Additionally, its dynamic response naturally introduces temporal attributes. Thus, it can serve as a neuromorphic computing model for time-series tasks. Without the involvement of dynamic mechanisms, it is capable of performing image recognition, time-series prediction, and multimodal recognition tasks. When dynamic mechanisms are incorporated, the model achieves an accuracy of 96.98% on the MNIST handwritten digit dataset and 86.67% on the Fashion-MNIST dataset. This work offers a neuromorphic computing architecture, providing insights for tasks such as nonlinear mapping and time-series prediction.
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Data availability
The physical data generated in this study have been deposited in the Zenodo database [https://doi.org/10.5281/zenodo.18475506].
Code availability
The code is available at Zenodo [https://doi.org/10.5281/zenodo.18459291].
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Acknowledgements
We acknowledge grants from RSC Sustainable Laboratories Grant (L24-8215098370, to Y.Z.), Guangdong Basic and Applied Basic Research Foundation (2024B1515040002, to S.T.H.), Guangdong Science and Technology Department (Grant No. MS202500156, to Y.Z.), Department of Education of Guangdong Province (Grant No. 2025ZDZX3024, to Y.Z.), Science, Technology and Innovation Commission of Shenzhen Municipality (Grant No. JCYJ20250604181254072, to Y.Z.), State Key Laboratory of Radio Frequency Heterogeneous Integration (Independent Scientific Research Program No. 2024010, to Y.Z.), the Hong Kong Research Grants Council, Young Collaborative Research Grant (C5001-24, to S.T.H.), Research Institute for Smart Energy (U-CDC9, to S.T.H.) and NTUT-SZU Joint Research Program (Grant No. 2026002, to Y.Z.).
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W.-M. Zhong: Conceptualization, Methodology, Writing - Original Draft. W. Zhang: Investigation. Y.-X. Zeng: Data Curation. J.-Y. Zhao: Investigation. Z.-Q. Jia: Investigation. G. Ding: Data Curation. S.-T. Han: Supervision, Data Curation. V.A.L. Roy: Writing - Review & Editing. Y. Zhou: Conceptualization, Project administration, Funding acquisition, Writing - Review & Editing.
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Zhong, WM., Zhang, W., Zeng, YX. et al. Physical echo state network based on the nonlinearity and dynamic response of ambipolar heterostructure transistors. Nat Commun (2026). https://doi.org/10.1038/s41467-026-70171-2
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DOI: https://doi.org/10.1038/s41467-026-70171-2
