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...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.展开更多
基金supported in part by the National Natural Science Foundation of China(Grant Nos.62371095,62201096,62401276)by the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications(Grant No.NY223161)+4 种基金in part by the Jiangsu Provincial Key Research and Development Program(Grant No.BE2022126)the Key R&D Program of Sichuan Province(Grant No.2022ZHCG0041)the National Key Research and Development Program of China(Grant No.2022YFB3206100)the Natural Science Foundation of Sichuan Province(Grant No.2024NSFSC0509)the China Postdoctoral Science Foundation(Grant Nos.2024T170097,2024M760343).
文摘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.
