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. 2025 Sep 17.
doi: 10.1038/s41565-025-02013-z. Online ahead of print.

Molecular crystal memristors

Affiliations

Molecular crystal memristors

Lanhao Qin et al. Nat Nanotechnol. .

Abstract

Memristors have emerged as a promising hardware platform for in-memory computing, but many current devices suffer from channel material degradation during repeated resistive switching. This leads to high energy consumption and limited endurance. Here we introduce a molecular crystal memristor, of which the representative channel material, Sb2O3, possesses a molecular crystal structure where molecular cages are interconnected via van der Waals forces. This unique configuration allows ions to migrate through intermolecular spaces with relatively low energy input, preserving the integrity of the crystal structure even after extensive switching cycles. Our molecular crystal memristor thus exhibits low energy consumption of 26 zJ per operation, with prominent endurance surpassing 109 switching cycles. The device delivers both reconfigurable non-volatile and volatile resistive switching behaviours over a broad range of device scales, from micrometres down to nanometres. Furthermore, we establish the scalability of this technology by fabricating large crossbar arrays on an 8 inch wafer. This enables the successful implementation of reservoir computing on a single CMOS-integrated chip using these memristors, achieving 100% accuracy in dynamic vision recognition.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

References

    1. Lanza, M. et al. Memristive technologies for data storage, computation, encryption, and radio-frequency communication. Science 376, eabj9979 (2022). - PubMed - DOI
    1. Wang, C. et al. Parallel in-memory wireless computing. Nat. Electron. 6, 381–389 (2023). - DOI
    1. Langenegger, J. et al. In-memory factorization of holographic perceptual representations. Nat. Nanotechnol. 18, 479–485 (2023). - PubMed - DOI
    1. Yi, S. I., Kendall, J. D., Williams, R. S. & Kumar, S. Activity-difference training of deep neural networks using memristor crossbars. Nat. Electron. 6, 45–51 (2023).
    1. Zidan, M. A., Strachan, J. P. & Lu, W. D. The future of electronics based on memristive systems. Nat. Electron. 1, 22–29 (2018). - DOI

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