首页|Two-photon nanolithography of micrometer scale diffractive neural network with cubical diffraction neurons at the visible wavelength
Two-photon nanolithography of micrometer scale diffractive neural network with cubical diffraction neurons at the visible wavelength
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
万方数据
维普
Free-space diffractive neural networks[DNNs]have been an intense research topic in machine learning for image recog-nition and encryption due to their high speed,lower power consumption,and high neuron density.Recent advances in DNNs have highlighted the need for smaller device footprints and the shift toward visible wavelengths.However,DNNs fabricated by electron beam lithography,are not suitable for microscopic imaging applications due to their large sizes,and DNNs fabricated by two-photon nanolithography with cylindrical neurons are not optimal for visible wavelengths,as the high-order diffraction could induce low diffraction efficiency.In this paper,we demonstrate that cubical diffraction neurons are more efficient diffraction elements for DNNs compared with cylindrical neurons.Based on the theoretical analysis of the relationship between the detector area sizes and classification accuracy,we reduced the size of DNNs operating at the wavelength of 532 nm for handwritten digit classification to micrometer scale by two-photon nanolithography.The DNNs with cubical neurons demonstrated an experimental classification accuracy[89.3％]for single-layer DNN,and 83.3％for two-layer DNN with device sizes similar to that of biological cells[about 100 μm × 100 μm].Our results paved the pathway to integrate 3D micrometer-scale DNNs with microscopic imaging systems for biological imaging and cell recognition.
Institute of Photonic Chips,University of Shanghai for Science and Technology,Shanghai 200093,China
Centre for Artificial-Intelligence Nanophotonics,School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China
万方数据
维普
