Plasmonic colors are attracting attention for their subwavelength small size,vibrant hues,and environmental sustainability beyond traditional pigments while suffering from angular and/or polarization dependency due to...Plasmonic colors are attracting attention for their subwavelength small size,vibrant hues,and environmental sustainability beyond traditional pigments while suffering from angular and/or polarization dependency due to distinct excitations of lattice resonances and/or surface plasmon polaritons(SPPs).Here,we demonstrate the sodium metasurface-based plasmonic color palettes with polarization-independent wide-view angle(approximately>〓〓60 deg in experiment and up to〓〓90 deg in theory)and single-particlelevel pixel size(down to∼60 nm)that integrate both pigment-like and structure coloring advantages,fabricated by the templated nanorod-pixelated solidification of wetted liquid metals.Such intriguing performances are mainly attributed to the particle plasmon dominant spectral response by steering the filling profile and thus the interplay between localized surface plasmons and SPPs.Combining low material cost,potentially scalable manufacturing process,and pronounced optical performance,the proposed sodium-based metasurfaces will provide a promising route for advanced color information technology.展开更多
Artificial neural networks have dramatically improved the performance of many machine-learning applications such as image recognition and natural language processing. However, the electronic hardware implementations o...Artificial neural networks have dramatically improved the performance of many machine-learning applications such as image recognition and natural language processing. However, the electronic hardware implementations of the above-mentioned tasks are facing performance ceiling because Moore’s Law is slowing down. In this article, we propose an optical neural network architecture based on optical scattering units to implement deep learning tasks with fast speed, low power consumption and small footprint.The optical scattering units allow light to scatter back and forward within a small region and can be optimized through an inverse design method. The optical scattering units can implement high-precision stochastic matrix multiplication with mean squared error < 10-4 and a mere 4*4 um2 footprint.Furthermore, an optical neural network framework based on optical scattering units is constructed by introducing "Kernel Matrix", which can achieve 97.1% accuracy on the classic image classification dataset MNIST.展开更多
Camouflage technology has attracted growing interest for many thermal applications.Previous experimental demonstrations of thermal camouflage technology have not adequately explored the ability to continuously camoufl...Camouflage technology has attracted growing interest for many thermal applications.Previous experimental demonstrations of thermal camouflage technology have not adequately explored the ability to continuously camouflage objects either at varying background temperatures or for wide observation angles.In this study,a thermal camouflage device incorporating the phase-changing material Ge2Sb2Te5(GST)is experimentally demonstrated.It has been shown that near-perfect thermal camouflage can be continuously achieved for background temperatures ranging from 30℃ to 50℃ by tuning the emissivity of the device,which is attained by controlling the GST phase change.The thermal camouflage is robust when the observation angle is changed from 0°to 60°.This demonstration paves the way toward dynamic thermal emission control both within the scientific field and for practical applications in thermal information.展开更多
Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years.Inverse design of functional nanostructures is at the heart of th...Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years.Inverse design of functional nanostructures is at the heart of this topic,in which artificial intelligence(AI)furnishes various optimization toolboxes to speed up prototyping of photonic layouts with enhanced performance.In this review,we offer a systemic view on recent advancements in nanophotonic components designed by intelligence algorithms,manifesting a development trend from performance optimizations towards inverse creations of novel designs.To illustrate interplays between two fields,AI and photonics,we take meta-atom spectral manipulation as a case study to introduce algorithm operational principles,and subsequently review their manifold usages among a set of popular meta-elements.As arranged from levels of individual optimized piece to practical system,we discuss algorithm-assisted nanophotonic designs to examine their mutual benefits.We further comment on a set of open questions including reasonable applications of advanced algorithms,expensive data issue,and algorithm benchmarking,etc.Overall,we envision mounting photonic-targeted methodologies to substantially push forward functional artificial meta-devices to profit both fields.展开更多
基金supported by the National Key Research and Development Program of China(Grant Nos.2021YFA1400700 and 2022YFA1404300)the National Natural Science Foundation of China(Grant Nos.12022403 and 62375123)the Natural Science Foundation of Jiangsu Province(Grant No.BK20243009).
文摘Plasmonic colors are attracting attention for their subwavelength small size,vibrant hues,and environmental sustainability beyond traditional pigments while suffering from angular and/or polarization dependency due to distinct excitations of lattice resonances and/or surface plasmon polaritons(SPPs).Here,we demonstrate the sodium metasurface-based plasmonic color palettes with polarization-independent wide-view angle(approximately>〓〓60 deg in experiment and up to〓〓90 deg in theory)and single-particlelevel pixel size(down to∼60 nm)that integrate both pigment-like and structure coloring advantages,fabricated by the templated nanorod-pixelated solidification of wetted liquid metals.Such intriguing performances are mainly attributed to the particle plasmon dominant spectral response by steering the filling profile and thus the interplay between localized surface plasmons and SPPs.Combining low material cost,potentially scalable manufacturing process,and pronounced optical performance,the proposed sodium-based metasurfaces will provide a promising route for advanced color information technology.
基金This work was supported by the National Key Research and Development Program of China(2017YFA0205700)the National Natural Science Foundation of China(61927820)Yurui Qu was supported by Zhejiang Lab’s International Talent Fund for Young Professionals.
文摘Artificial neural networks have dramatically improved the performance of many machine-learning applications such as image recognition and natural language processing. However, the electronic hardware implementations of the above-mentioned tasks are facing performance ceiling because Moore’s Law is slowing down. In this article, we propose an optical neural network architecture based on optical scattering units to implement deep learning tasks with fast speed, low power consumption and small footprint.The optical scattering units allow light to scatter back and forward within a small region and can be optimized through an inverse design method. The optical scattering units can implement high-precision stochastic matrix multiplication with mean squared error < 10-4 and a mere 4*4 um2 footprint.Furthermore, an optical neural network framework based on optical scattering units is constructed by introducing "Kernel Matrix", which can achieve 97.1% accuracy on the classic image classification dataset MNIST.
基金supported by the National Key Research and Development Program of China(Grant Number 2017YFA0205700)the National Natural Science Foundation of China(Grant Numbers 61425023,61575177,and 61235007).
文摘Camouflage technology has attracted growing interest for many thermal applications.Previous experimental demonstrations of thermal camouflage technology have not adequately explored the ability to continuously camouflage objects either at varying background temperatures or for wide observation angles.In this study,a thermal camouflage device incorporating the phase-changing material Ge2Sb2Te5(GST)is experimentally demonstrated.It has been shown that near-perfect thermal camouflage can be continuously achieved for background temperatures ranging from 30℃ to 50℃ by tuning the emissivity of the device,which is attained by controlling the GST phase change.The thermal camouflage is robust when the observation angle is changed from 0°to 60°.This demonstration paves the way toward dynamic thermal emission control both within the scientific field and for practical applications in thermal information.
基金National Natural Science Foundation of China(No.62005224,61927820)National Key Research and Development Program of China(2017YFA0205700)。
文摘Applying intelligence algorithms to conceive nanoscale meta-devices becomes a flourishing and extremely active scientific topic over the past few years.Inverse design of functional nanostructures is at the heart of this topic,in which artificial intelligence(AI)furnishes various optimization toolboxes to speed up prototyping of photonic layouts with enhanced performance.In this review,we offer a systemic view on recent advancements in nanophotonic components designed by intelligence algorithms,manifesting a development trend from performance optimizations towards inverse creations of novel designs.To illustrate interplays between two fields,AI and photonics,we take meta-atom spectral manipulation as a case study to introduce algorithm operational principles,and subsequently review their manifold usages among a set of popular meta-elements.As arranged from levels of individual optimized piece to practical system,we discuss algorithm-assisted nanophotonic designs to examine their mutual benefits.We further comment on a set of open questions including reasonable applications of advanced algorithms,expensive data issue,and algorithm benchmarking,etc.Overall,we envision mounting photonic-targeted methodologies to substantially push forward functional artificial meta-devices to profit both fields.
