Scholarlink

<p>The continuing development of artificial intelligence requires more powerful computing architectures. However, the large footprint of complementary-metal–oxide–semiconductor-based neurons and constraints on electric routing hinder the scaling of conventional artificial neurons and their synaptic connectivity. Here we show that memristive blinking neurons can be used to build scalable photonically linked three-dimensional neural networks. Our artificial neuron is based on a silver/poly(methyl methacrylate)/silver metal–insulator–metal memristive switching in-plane junction. Its resistive switching relies on atomic-scale filamentary dynamics and the device emits photon pulses on integrating a critical number of incoming electrical spikes, which eliminates the need for bulky peripheral circuit read-out and electrical wiring for transmitting signals. We use the memristive blinking neuron, which has a footprint of 170 nm × 240 nm, to build a photonically linked three-dimensional spiking neural network. We show that the network can perform a four-class classification task within the Google Speech dataset with an accuracy of 91.51%. We also create a high-density artificial neuron array with a pitch of 1 μm and show that it can perform an MNIST classification task with an accuracy of 92.27%.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Photonically linked three-dimensional neural networks based on memristive blinking neurons

  • 期刊论文

  • 发表日期：

    2026年1月8日

查看全文
  • Yue Zhou,
  • Yuetong Fang,
  • Raphael Gisler,
  • Hongwei Ren,
  • Haotian Fu,
  • Zelin Ma,
  • Yulong Huang,
  • Renjing Xu,
  • Alexandre Bouhelier,
  • Juerg Leuthold,
  • Bojun Cheng

摘要

The continuing development of artificial intelligence requires more powerful computing architectures. However, the large footprint of complementary-metal–oxide–semiconductor-based neurons and constraints on electric routing hinder the scaling of conventional artificial neurons and their synaptic connectivity. Here we show that memristive blinking neurons can be used to build scalable photonically linked three-dimensional neural networks. Our artificial neuron is based on a silver/poly(methyl methacrylate)/silver metal–insulator–metal memristive switching in-plane junction. Its resistive switching relies on atomic-scale filamentary dynamics and the device emits photon pulses on integrating a critical number of incoming electrical spikes, which eliminates the need for bulky peripheral circuit read-out and electrical wiring for transmitting signals. We use the memristive blinking neuron, which has a footprint of 170 nm × 240 nm, to build a photonically linked three-dimensional spiking neural network. We show that the network can perform a four-class classification task within the Google Speech dataset with an accuracy of 91.51%. We also create a high-density artificial neuron array with a pitch of 1 μm and show that it can perform an MNIST classification task with an accuracy of 92.27%.