Scholarlink

<p>Introducing probabilistic models into photonic neural networks, harnessing high-throughput and low-latency performance of photons, holds great promise for Bayesian inference. Photonic spiking neurons with stochasticity are essential to realize probabilistic computing. However, existing probabilistic neurons lack intrinsic stochasticity and rely on external entropy sources, adding architectural complexity that impedes high-density integration. Here, we report the first compact on-chip photonic spiking neuron with inherent stochasticity based on a novel phase-change material SbTe<sub>9</sub>, featuring an active footprint of only 1.5 μm<sup>2</sup>. This neuron enables stable and tunable probabilistic firing behaviors, arising from the intrinsic fluctuations of the melting point and temperature of the SbTe<sub>9</sub> layer driven by microstructure evolution during non-equilibrium melting and crystallization. Leveraging this stochasticity, the neuron enables the Bayesian inference achieving 98.67% accuracy with uncertainty quantification for breast cell diagnosis, and demonstrates remarkable tolerance to hardware synaptic variations (0.47% reduction, ten times smaller) and input noise (4.28% reduction at 15% noise, over twofold smaller) compared with deterministic neurons. Based on the novel volatile phase-change material, this neuron establishes a transformative pathway toward the development of large-scale, low-complexity and high-performance on-chip photonic neuromorphic computing systems.</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

Compact photonic spiking neuron with inherent stochasticity based on phase-change material for probabilistic computing

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月18日

查看全文
  • Yunxiao Dong,
  • Tianci Wang,
  • Jian Xia,
  • Zixuan Wang,
  • Chaohui Su,
  • Zhiyuan Li,
  • Gongmin Li,
  • Wei Tang,
  • Jie Gong,
  • Zilong Yu,
  • Beining Zhang,
  • Qingyang Li,
  • Xiangshui Miao,
  • Rui Yang

摘要

Introducing probabilistic models into photonic neural networks, harnessing high-throughput and low-latency performance of photons, holds great promise for Bayesian inference. Photonic spiking neurons with stochasticity are essential to realize probabilistic computing. However, existing probabilistic neurons lack intrinsic stochasticity and rely on external entropy sources, adding architectural complexity that impedes high-density integration. Here, we report the first compact on-chip photonic spiking neuron with inherent stochasticity based on a novel phase-change material SbTe9, featuring an active footprint of only 1.5 μm2. This neuron enables stable and tunable probabilistic firing behaviors, arising from the intrinsic fluctuations of the melting point and temperature of the SbTe9 layer driven by microstructure evolution during non-equilibrium melting and crystallization. Leveraging this stochasticity, the neuron enables the Bayesian inference achieving 98.67% accuracy with uncertainty quantification for breast cell diagnosis, and demonstrates remarkable tolerance to hardware synaptic variations (0.47% reduction, ten times smaller) and input noise (4.28% reduction at 15% noise, over twofold smaller) compared with deterministic neurons. Based on the novel volatile phase-change material, this neuron establishes a transformative pathway toward the development of large-scale, low-complexity and high-performance on-chip photonic neuromorphic computing systems.