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Three-dimensional crossbar arrays of self-rectifying Si/SiO2/Si memristors

Author

Listed:
  • Can Li

    (University of Massachusetts)

  • Lili Han

    (University of Massachusetts
    Center for Functional Nanomaterials, Brookhaven National Laboratory
    Present address: Center for Electron Microscopy, TUT-FEI Joint Laboratory, Institute for New Energy Materials & Low-Carbon Technologies, School of Materials Science and Engineering, Tianjin University of Technology, Tianjin 300384, China)

  • Hao Jiang

    (University of Massachusetts)

  • Moon-Hyung Jang

    (University of Massachusetts)

  • Peng Lin

    (University of Massachusetts)

  • Qing Wu

    (Air Force Research Laboratory, Information Directorate)

  • Mark Barnell

    (Air Force Research Laboratory, Information Directorate)

  • J. Joshua Yang

    (University of Massachusetts)

  • Huolin L. Xin

    (Center for Functional Nanomaterials, Brookhaven National Laboratory)

  • Qiangfei Xia

    (University of Massachusetts)

Abstract

Memristors are promising building blocks for the next-generation memory and neuromorphic computing systems. Most memristors use materials that are incompatible with the silicon dominant complementary metal-oxide-semiconductor technology, and require external selectors in order for large memristor arrays to function properly. Here we demonstrate a fully foundry-compatible, all-silicon-based and self-rectifying memristor that negates the need for external selectors in large arrays. With a p-Si/SiO2/n-Si structure, our memristor exhibits repeatable unipolar resistance switching behaviour (105 rectifying ratio, 104 ON/OFF) and excellent retention at 300 °C. We further build three-dimensinal crossbar arrays (up to five layers of 100 nm memristors) using fluid-supported silicon membranes, and experimentally confirm the successful suppression of both intra- and inter-layer sneak path currents through the built-in diodes. The current work opens up opportunities for low-cost mass production of three-dimensional memristor arrays on large silicon and flexible substrates without increasing circuit complexity.

Suggested Citation

  • Can Li & Lili Han & Hao Jiang & Moon-Hyung Jang & Peng Lin & Qing Wu & Mark Barnell & J. Joshua Yang & Huolin L. Xin & Qiangfei Xia, 2017. "Three-dimensional crossbar arrays of self-rectifying Si/SiO2/Si memristors," Nature Communications, Nature, vol. 8(1), pages 1-9, August.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15666
    DOI: 10.1038/ncomms15666
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    Cited by:

    1. Fernando Aguirre & Abu Sebastian & Manuel Gallo & Wenhao Song & Tong Wang & J. Joshua Yang & Wei Lu & Meng-Fan Chang & Daniele Ielmini & Yuchao Yang & Adnan Mehonic & Anthony Kenyon & Marco A. Villena, 2024. "Hardware implementation of memristor-based artificial neural networks," Nature Communications, Nature, vol. 15(1), pages 1-40, December.
    2. Bassem Tossoun & Di Liang & Stanley Cheung & Zhuoran Fang & Xia Sheng & John Paul Strachan & Raymond G. Beausoleil, 2024. "High-speed and energy-efficient non-volatile silicon photonic memory based on heterogeneously integrated memresonator," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    3. Sanghyeon Choi & Jaeho Shin & Gwanyeong Park & Jung Sun Eo & Jingon Jang & J. Joshua Yang & Gunuk Wang, 2024. "3D-integrated multilayered physical reservoir array for learning and forecasting time-series information," Nature Communications, Nature, vol. 15(1), pages 1-11, December.

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