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Classification with a disordered dopant-atom network in silicon

Author

Listed:
  • Tao Chen

    (University of Twente)

  • Jeroen van Gelder

    (University of Twente)

  • Bram van de Ven

    (University of Twente)

  • Sergey V. Amitonov

    (University of Twente)

  • Bram de Wilde

    (University of Twente)

  • Hans-Christian Ruiz Euler

    (University of Twente)

  • Hajo Broersma

    (University of Twente)

  • Peter A. Bobbert

    (University of Twente
    Eindhoven University of Technology)

  • Floris A. Zwanenburg

    (University of Twente)

  • Wilfred G. van der Wiel

    (University of Twente)

Abstract

Classification is an important task at which both biological and artificial neural networks excel1,2. In machine learning, nonlinear projection into a high-dimensional feature space can make data linearly separable3,4, simplifying the classification of complex features. Such nonlinear projections are computationally expensive in conventional computers. A promising approach is to exploit physical materials systems that perform this nonlinear projection intrinsically, because of their high computational density5, inherent parallelism and energy efficiency6,7. However, existing approaches either rely on the systems’ time dynamics, which requires sequential data processing and therefore hinders parallel computation5,6,8, or employ large materials systems that are difficult to scale up7. Here we use a parallel, nanoscale approach inspired by filters in the brain1 and artificial neural networks2 to perform nonlinear classification and feature extraction. We exploit the nonlinearity of hopping conduction9–11 through an electrically tunable network of boron dopant atoms in silicon, reconfiguring the network through artificial evolution to realize different computational functions. We first solve the canonical two-input binary classification problem, realizing all Boolean logic gates12 up to room temperature, demonstrating nonlinear classification with the nanomaterial system. We then evolve our dopant network to realize feature filters2 that can perform four-input binary classification on the Modified National Institute of Standards and Technology handwritten digit database. Implementation of our material-based filters substantially improves the classification accuracy over that of a linear classifier directly applied to the original data13. Our results establish a paradigm of silicon-based electronics for small-footprint and energy-efficient computation14.

Suggested Citation

  • Tao Chen & Jeroen van Gelder & Bram van de Ven & Sergey V. Amitonov & Bram de Wilde & Hans-Christian Ruiz Euler & Hajo Broersma & Peter A. Bobbert & Floris A. Zwanenburg & Wilfred G. van der Wiel, 2020. "Classification with a disordered dopant-atom network in silicon," Nature, Nature, vol. 577(7790), pages 341-345, January.
  • Handle: RePEc:nat:nature:v:577:y:2020:i:7790:d:10.1038_s41586-019-1901-0
    DOI: 10.1038/s41586-019-1901-0
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    Citations

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    Cited by:

    1. Herbert Jaeger & Beatriz Noheda & Wilfred G. Wiel, 2023. "Toward a formal theory for computing machines made out of whatever physics offers," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    2. Dmitrii V. Kriukov & Jurriaan Huskens & Albert S. Y. Wong, 2024. "Exploring the programmability of autocatalytic chemical reaction networks," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    3. Zhongfang Zhang & Xiaolong Zhao & Xumeng Zhang & Xiaohu Hou & Xiaolan Ma & Shuangzhu Tang & Ying Zhang & Guangwei Xu & Qi Liu & Shibing Long, 2022. "In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Liying Xu & Jiadi Zhu & Bing Chen & Zhen Yang & Keqin Liu & Bingjie Dang & Teng Zhang & Yuchao Yang & Ru Huang, 2022. "A distributed nanocluster based multi-agent evolutionary network," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    5. Mitsumasa Nakajima & Katsuma Inoue & Kenji Tanaka & Yasuo Kuniyoshi & Toshikazu Hashimoto & Kohei Nakajima, 2022. "Physical deep learning with biologically inspired training method: gradient-free approach for physical hardware," Nature Communications, Nature, vol. 13(1), pages 1-12, December.

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