Journal Article

Polarization-encoded neural networks with simplified grating patch

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

2025

Overview

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.

• Publisher: Science China Press,Springer Nature B.V
• Subject: Artificial intelligence / Diffraction / Engineering / Incident light / Light speed / Neural networks / Plasmonics / Polarization / Research Paper / Smart sensors / Synapses / Visible spectrum