Controllable SiOx Nanorod Memristive Neuron for Probabilistic Bayesian Inference

Sanghyeon Choi, Gwang Su Kim, Jehyeon Yang, Haein Cho, Chong Yun Kang, Gunuk Wang

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


Modern artificial neural network technology using a deterministic computing framework is faced with a critical challenge in dealing with massive data that are largely unstructured and ambiguous. This challenge demands the advances of an elementary physical device for tackling these uncertainties. Here, we designed and fabricated a SiOx nanorod memristive device by employing the glancing angle deposition (GLAD) technique, suggesting a controllable stochastic artificial neuron that can mimic the fundamental integrate-and-fire signaling and stochastic dynamics of a biological neuron. The nanorod structure provides the random distribution of multiple nanopores all across the active area, capable of forming a multitude of Si filaments at many SiOx nanorod edges after the electromigration process, leading to a stochastic switching event with very high dynamic range (≈5.15 × 1010) and low energy (≈4.06 pJ). Different probabilistic activation (ProbAct) functions in a sigmoid form are implemented, showing its controllability with low variation by manufacturing and electrical programming schemes. Furthermore, as an application prospect, based on the suggested memristive neuron, we demonstrated the self-resting neural operation with the local circuit configuration and revealed probabilistic Bayesian inferences for genetic regulatory networks with low normalized mean squared errors (≈2.41 × 10-2) and its robustness to the ProbAct variation.

Original languageEnglish
Article number2104598
JournalAdvanced Materials
Issue number1
Publication statusPublished - 2022 Jan 6

Bibliographical note

Publisher Copyright:
© 2021 Wiley-VCH GmbH.

ASJC Scopus subject areas

  • General Materials Science
  • Mechanics of Materials
  • Mechanical Engineering


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