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90% yield production of polymer nano-memristor for in-memory computing

Author

Listed:
  • Bin Zhang

    (East China University of Science and Technology)

  • Weilin Chen

    (Shanghai Jiao Tong University)

  • Jianmin Zeng

    (Hefei University of Technology)

  • Fei Fan

    (East China University of Science and Technology
    Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Junwei Gu

    (Northwestern Polytechnical University)

  • Xinhui Chen

    (Shanghai Jiao Tong University)

  • Lin Yan

    (Hefei University of Technology)

  • Guangjun Xie

    (Hefei University of Technology)

  • Shuzhi Liu

    (Shanghai Jiao Tong University)

  • Qing Yan

    (East China University of Science and Technology)

  • Seung Jae Baik

    (Hankyong National University)

  • Zhi-Guo Zhang

    (Zhengzhou University)

  • Weihua Chen

    (Zhengzhou University)

  • Jie Hou

    (East China University of Science and Technology)

  • Mohamed E. El-Khouly

    (Egypt-Japan University of Science and Technology (E-JUST))

  • Zhang Zhang

    (Hefei University of Technology)

  • Gang Liu

    (Shanghai Jiao Tong University)

  • Yu Chen

    (East China University of Science and Technology)

Abstract

Polymer memristors with light weight and mechanical flexibility are preeminent candidates for low-power edge computing paradigms. However, the structural inhomogeneity of most polymers usually leads to random resistive switching characteristics, which lowers the production yield and reliability of nanoscale devices. In this contribution, we report that by adopting the two-dimensional conjugation strategy, a record high 90% production yield of polymer memristors has been achieved with miniaturization and low power potentials. By constructing coplanar macromolecules with 2D conjugated thiophene derivatives to enhance the π–π stacking and crystallinity of the thin film, homogeneous switching takes place across the entire polymer layer, with fast responses in 32 ns, D2D variation down to 3.16% ~ 8.29%, production yield approaching 90%, and scalability into 100 nm scale with tiny power consumption of ~ 10−15 J/bit. The polymer memristor array is capable of acting as both the arithmetic-logic element and multiply-accumulate accelerator for neuromorphic computing tasks.

Suggested Citation

  • Bin Zhang & Weilin Chen & Jianmin Zeng & Fei Fan & Junwei Gu & Xinhui Chen & Lin Yan & Guangjun Xie & Shuzhi Liu & Qing Yan & Seung Jae Baik & Zhi-Guo Zhang & Weihua Chen & Jie Hou & Mohamed E. El-Kho, 2021. "90% yield production of polymer nano-memristor for in-memory computing," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22243-8
    DOI: 10.1038/s41467-021-22243-8
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