Scholarlink

<p>Optical neural networks (ONNs) offer a promising solution for high-performance, energy-efficient artificial intelligence hardware by leveraging the parallelism and speed of light. However, the large-scale implementation of ONNs remains challenging due to the bulky footprint and complex control of optical synapses. In this work, we propose and simulate a plasmonic polarized synaptic architecture that overcomes the diffraction limit and enables ultra-compact ONNs. By tuning the polarization state of incident light, the optical transmittance through each plasmonic unit can be dynamically adjusted to represent a synaptic weight. Our plasmonic structures, with features as small as 40 nm, operate well below this limit in the visible spectrum (400-750 nm). Compared with diffraction and interference-based circuit designs, our proposed method achieves a substantial reduction in synaptic density by factors of 150000-fold and 1500-fold, respectively. Furthermore, we successfully demonstrate a proof-of-concept plasmonic ONN applied to the Canadian Institute for Advanced Research—10 classes (CIFAR-10) dataset using a Visual Geometry Group network with 16 layers (VGG16) model. After training for 80 epochs, the network achieves an accuracy of 93%. The polarization-tunable plasmonics paves the way towards scalable ONNs for next-generation artificial intelligence (AI) accelerators and smart sensors.</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

Polarization-encoded neural networks with simplified grating patch

  • 期刊论文

  • 发表日期：

    2025年1月13日

查看全文
  • Chengyan Zhong,
  • Xiang Wang,
  • Lingfei Li,
  • Yuanchi Cui,
  • Lei Xiao,
  • Dawei Song,
  • Junxiong Guo,
  • Wen Huang,
  • Yufeng Guo,
  • Yu Liu

摘要

Optical neural networks (ONNs) offer a promising solution for high-performance, energy-efficient artificial intelligence hardware by leveraging the parallelism and speed of light. However, the large-scale implementation of ONNs remains challenging due to the bulky footprint and complex control of optical synapses. In this work, we propose and simulate a plasmonic polarized synaptic architecture that overcomes the diffraction limit and enables ultra-compact ONNs. By tuning the polarization state of incident light, the optical transmittance through each plasmonic unit can be dynamically adjusted to represent a synaptic weight. Our plasmonic structures, with features as small as 40 nm, operate well below this limit in the visible spectrum (400-750 nm). Compared with diffraction and interference-based circuit designs, our proposed method achieves a substantial reduction in synaptic density by factors of 150000-fold and 1500-fold, respectively. Furthermore, we successfully demonstrate a proof-of-concept plasmonic ONN applied to the Canadian Institute for Advanced Research—10 classes (CIFAR-10) dataset using a Visual Geometry Group network with 16 layers (VGG16) model. After training for 80 epochs, the network achieves an accuracy of 93%. The polarization-tunable plasmonics paves the way towards scalable ONNs for next-generation artificial intelligence (AI) accelerators and smart sensors.