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Efficient data processing using tunable entropy-stabilized oxide memristors

Nature Electronics (2024)Cite this article

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Abstract

Memristive devices are of potential use in a range of computing applications. However, many of these devices are based on amorphous materials, where systematic control of the switching dynamics is challenging. Here we report tunable and stable memristors based on an entropy-stabilized oxide. We use single-crystalline (Mg,Co,Ni,Cu,Zn)O films grown on an epitaxial bottom electrode. By adjusting the magnesium composition (XMg = 0.11–0.27) of the entropy-stabilized oxide films, a range of internal time constants (159–278 ns) for the switching process can be obtained. We use the memristors to create a reservoir computing network that classifies time-series input data and show that the reservoir computing system, which has tunable reservoirs, offers better classification accuracy and energy efficiency than previous reservoir system implementations.

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Fig. 1: Tunable composition and structural disorder in single-crystalline ESO thin films on epitaxial YBCO electrodes.
Fig. 2: Tunable defect-mediated hopping conductivity in single-crystalline thin-film ESOs.
Fig. 3: Tunable RS characteristics.
Fig. 4: Experimentally implemented RC network with ESO memristors.

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Data availability

Source data are provided with this paper. Additional data related to this work are available from the corresponding authors upon request.

Code availability

Computational simulation code of the RC system for the speech-recognition task is available from the corresponding authors upon reasonable request.

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Acknowledgements

This work was supported by the National Science Foundation (NSF) through awards CCF-1900675, ECCS-1915550, NSF CAREER grant no. DMR-1847847 and NSF MRSEC grant no. DMR-2011839, S.C. acknowledges support from Rackham Predoctoral Fellowship. We gratefully acknowledge the Michigan Center for Materials Characterization. We also acknowledge technical support from Lurie Nanofabrication Facility.

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Author notes

  1. These authors contributed equally: Sangmin Yoo, Sieun Chae.

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA

    Sangmin Yoo, Yongmo Park & Wei D. Lu

  2. Department of Materials Science and Engineering, University of Michigan, Ann Arbor, MI, USA

    Sieun Chae, Tony Chiang, Matthew Webb, Logan Williams, Emmanouil Kioupakis & John T. Heron

  3. Michigan Center for Materials Characterization, University of Michigan, Ann Arbor, MI, USA

    Tao Ma

  4. School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, USA

    Hanjong Paik

  5. School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

    Kazuki Nomoto & Huili G. Xing

  6. Department of Materials Science and Engineering, Cornell University, Ithaca, NY, USA

    Huili G. Xing

  7. Department of Materials Science and Engineering and Materials Research Institute, The Pennsylvania State University, University Park, PA, USA

    Susan Trolier-McKinstry

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Contributions

S.Y. and S.C. conceived the idea and designed the experiments. S.C. synthesized the materials and fabricated the devices under J.T.H.’s supervision together with T.C. S.C. performed the TEM, EDS and material characterizations with T.M., H.P., K.N. and H.G.X. S.C. performed the DFT calculation with L.W. under E.K.’s supervision. S.Y. performed the pulse measurements and analysis with Y.P. and demonstrated the RC systems under W.D.L.’s supervision. S.Y. and S.C. wrote the paper under the supervision of W.L. and J.T.H. All authors contributed to the discussion and checked the paper.

Corresponding authors

Correspondence to John T. Heron or Wei D. Lu.

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Nature Electronics thanks Shriram Ramanathan, Cristina Rost and Ilia Valov for their contribution to the peer review of this work.

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Supplementary Figs. 1–15.

Source data

Source Data Fig. 1

Unprocessed X-ray diffraction data and simulated structure data.

Source Data Fig. 2

Statistical source data in Fig. 2 and analysis data.

Source Data Fig. 3

Statistical source data and experimental data.

Source Data Fig. 4

Experimental data used in a computational simulation.

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Yoo, S., Chae, S., Chiang, T. et al. Efficient data processing using tunable entropy-stabilized oxide memristors. Nat Electron (2024). https://doi.org/10.1038/s41928-024-01169-1

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