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<p>Neuromorphic hardware can mitigate the unsustainable energy demand of artificial intelligence infrastructure. Photonic approaches provide high speed, low energy, and high connectivity but existing solutions have large circuit footprints which limits scaling potential and they miss key biological functions, like inhibition. We report a nano-optoelectronic artificial neuron with at least 100-fold reduced circuit footprints compared to existing approaches and picowatt-level operating power. The device deterministically integrates excitatory and inhibitory inputs, performs a nonlinear transfer operation, and exhibits biologically relevant temporal dynamics. Neural weighting is implemented via tunable input gains, enabling controlled summation and thresholding. The architecture is compatible with commercial silicon technology, supports multi-wavelength operation, and can be used for both computing and optical sensing. This work paves the way for two important research paths: photonic neuromorphic computing with nanosized footprints and low power consumption, and adaptive optical sensing, using the same architecture as a compact, modular front end.</p>

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Nanoscale photonic artificial neuron with biological signal processing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月3日

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  • Joachim E. Sestoft,
  • Thomas K. Jensen,
  • Vidar Flodgren,
  • Abhijit Das,
  • Rasmus D. Schlosser,
  • David Alcer,
  • Mariia Lamers,
  • Thomas Kanne,
  • Magnus T. Borgström,
  • Jesper Nygård,
  • Anders Mikkelsen

摘要

Neuromorphic hardware can mitigate the unsustainable energy demand of artificial intelligence infrastructure. Photonic approaches provide high speed, low energy, and high connectivity but existing solutions have large circuit footprints which limits scaling potential and they miss key biological functions, like inhibition. We report a nano-optoelectronic artificial neuron with at least 100-fold reduced circuit footprints compared to existing approaches and picowatt-level operating power. The device deterministically integrates excitatory and inhibitory inputs, performs a nonlinear transfer operation, and exhibits biologically relevant temporal dynamics. Neural weighting is implemented via tunable input gains, enabling controlled summation and thresholding. The architecture is compatible with commercial silicon technology, supports multi-wavelength operation, and can be used for both computing and optical sensing. This work paves the way for two important research paths: photonic neuromorphic computing with nanosized footprints and low power consumption, and adaptive optical sensing, using the same architecture as a compact, modular front end.