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Neuromorphic computing based on halide perovskites

Nature Electronics volume 6, pages 949–962 (2023)Cite this article

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Abstract

Neuromorphic computing requires electronic systems that can perform massively parallel computational tasks with low energy consumption. Such systems have traditionally been based on complementary metal–oxide–semiconductor circuits, but further advances in computational performance will probably require devices that can offer high-order complexity combined with area and energy efficiency. Halide perovskites can handle both ions and electronic charges, and could be used to create adaptive computing systems based on intrinsic device dynamics. The materials also offer exotic switching phenomena, providing opportunities for multimodal systems. Here we explore the development of neuromorphic hardware systems based on halide perovskites. We examine how devices based on these materials can serve as synapses and neurons, and can be used in neuromorphic computing networks. We also consider the challenges involved in developing practical perovskite neuromorphic systems, and highlight how these systems could augment and complement digital circuits.

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Fig. 1: Two-terminal perovskite memristors.
Fig. 2: Ferroelectric gate field-effect synaptic transistor.
Fig. 3: Perovskite ANNs.
Fig. 4: Engineering future perovskite neural networks.

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The data that support the findings of this study are available from the corresponding authors.

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Acknowledgements

This study was supported by the Ministry of Science and ICT through the National Research Foundation, funded by the Korean Government (NRF-2021R1A2C3005401). The research work presented in this article was partially supported by the European Commission under the Horizon Europe Programme, through funding of the OASEES project (101092702).

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Authors and Affiliations

  1. Institute of Nanoscience and Nanotechnology, National Centre for Scientific Research Demokritos, Attica, Greece

    Maria Vasilopoulou

  2. Department of Physics, Faculty of Science, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Abd Rashid bin Mohd Yusoff

  3. Department of Applied Physics, The Hong Kong Polytechnic University, Hong Kong, China

    Yang Chai

  4. The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, China

    Yang Chai

  5. Institute of Informatics and Telecommunications, National Centre for Scientific Research Demokritos, Athens, Greece

    Michael-Alexandros Kourtis

  6. International Institute for Carbon-Neutral Energy Research, Kyushu University, Fukuoka, Japan

    Toshinori Matsushima

  7. Department of Chemistry and Centre for Processable Electronics, Imperial College London, London, UK

    Nicola Gasparini

  8. Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Rose Du

  9. Department of Physics, Chemistry and Biology (IFM), Linköping University, Linköping, Sweden

    Feng Gao

  10. Institute of Chemical Sciences and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), Sion, Switzerland

    Mohammad Khaja Nazeeruddin

  11. Physical Science and Engineering Division, KAUST Solar Center (KSC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia

    Thomas D. Anthopoulos

  12. Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea

    Yong-Young Noh

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Contributions

All authors conceived this work and contributed to the discussion of content. M.V. and A.R.b.M.Y. researched the data and wrote the first draft. M.K.N., R.D., F.G., T.D.A. and Y.-Y.N. revised the manuscript before submission.

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Correspondence to Maria Vasilopoulou, Abd Rashid bin Mohd Yusoff, Thomas D. Anthopoulos or Yong-Young Noh.

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Vasilopoulou, M., Mohd Yusoff, A.R.b., Chai, Y. et al. Neuromorphic computing based on halide perovskites. Nat Electron 6, 949–962 (2023). https://doi.org/10.1038/s41928-023-01082-z

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