In a very recent study,Prof.Lingling Huang and co-workers proposed and demonstrated reconfigurable optical neural networks based on cascaded metasurfaces.By fixing one metasurface and switching the other pluggable met...In a very recent study,Prof.Lingling Huang and co-workers proposed and demonstrated reconfigurable optical neural networks based on cascaded metasurfaces.By fixing one metasurface and switching the other pluggable metasurfaces,the neural networks,which operate at near-infrared wavelengths,can perform distinct recognition tasks for handwritten digits and fashion products.This innovative device opens up an avenue for all-optical,high-speed,low-power,and multifunctional artificial intelligence systems.展开更多
Over the past decades,photonics has transformed many areas in both fundamental research and practical applications.In particular,we can manipulate light in a desired and prescribed manner by rationally designed subwav...Over the past decades,photonics has transformed many areas in both fundamental research and practical applications.In particular,we can manipulate light in a desired and prescribed manner by rationally designed subwavelength structures.However,constructing complex photonic structures and devices is still a time-consuming process,even for experienced researchers.As a subset of artificial intelligence,artificial neural networks serve as one potential solution to bypass the complicated design process,enabling us to directly predict the optical responses of photonic structures or perform the inverse design with high efficiency and accuracy.In this review,we will introduce several commonly used neural networks and highlight their applications in the design process of various optical structures and devices,particularly those in recent experimental works.We will also comment on the future directions to inspire researchers from different disciplines to collectively advance this emerging research field.展开更多
Digital signal processors are extensively used to execute mathematical operations and advanced computational tasks on digital data.However,they suffer from several inherent limitations,including low speed,high energy ...Digital signal processors are extensively used to execute mathematical operations and advanced computational tasks on digital data.However,they suffer from several inherent limitations,including low speed,high energy consumption,and large memory requirements,because of the hardware bottleneck and the imperative conversion between digital and analogue signals.展开更多
Effective medium theory(EMT)has been widely applied in material science,electromagnetics and photonics to determine the effective material properties for inhomogeneous composites comprising subwavelength structures.Th...Effective medium theory(EMT)has been widely applied in material science,electromagnetics and photonics to determine the effective material properties for inhomogeneous composites comprising subwavelength structures.The versatility of this foundational approach has been established through its application in classical Maxwell-Garnet models,which encompass relatively simple structures。展开更多
With its tremendous success in many machine learning and pattern recognition tasks,deep learning,as one type of data-driven models,has also led to many breakthroughs in other disciplines including physics,chemistry an...With its tremendous success in many machine learning and pattern recognition tasks,deep learning,as one type of data-driven models,has also led to many breakthroughs in other disciplines including physics,chemistry and material science.Nevertheless,the supremacy of deep learning over conventional optimization approaches heavily depends on the huge amount of data collected in advance to train the model,which is a common bottleneck of such a data-driven technique.In this work,we present a comprehensive deep learning model for the design and characterization of nanophotonic structures,where a self-supervised learning mechanism is introduced to alleviate the burden of data acquisition.Taking reflective metasurfaces as an example,we demonstrate that the self-supervised deep learning model can effectively utilize randomly generated unlabeled data during training,with the total test loss and prediction accuracy improved by about 15%compared with the fully supervised counterpart.The proposed self-supervised learning scheme provides an efficient solution for deep learning models in some physics-related tasks where labeled data are limited or expensive to collect.展开更多
Direct Ink Writing(DIW)has demonstrated great potential as a versatile method to 3D print multifunctional structures.In this work,we report the implementation of hydrogel meta-structures using DIW at room temperature,...Direct Ink Writing(DIW)has demonstrated great potential as a versatile method to 3D print multifunctional structures.In this work,we report the implementation of hydrogel meta-structures using DIW at room temperature,which seamlessly integrate large specific surface areas,interconnected porous characteristics,mechanical toughness,biocompatibility,and water absorption and retention capabilities.Robust but hydrophobic polymers and weakly crosslinked nature-origin hydrogels form a balance in the self-supporting ink,allowing us to directly print complex meta-structures without sacrificial materials and heating extrusion.Mechanically,the mixed bending or stretching of symmetrical re-entrant cellular lattices and the unique curvature patterns are combined to provide little lateral expansion and large compressive energy absorbance when external forces are applied on the printed meta-structures.In addition,we have successfully demonstrated ear,aortic valve conduits and hierarchical architectures.We anticipate that the reported 3D meta-structured hydrogel would offer a new strategy to develop functional biomaterials for tissue engineering applications in the future.展开更多
基金support of the National Science Foundation(ECCS-1916839 and DMR-2202268).
文摘In a very recent study,Prof.Lingling Huang and co-workers proposed and demonstrated reconfigurable optical neural networks based on cascaded metasurfaces.By fixing one metasurface and switching the other pluggable metasurfaces,the neural networks,which operate at near-infrared wavelengths,can perform distinct recognition tasks for handwritten digits and fashion products.This innovative device opens up an avenue for all-optical,high-speed,low-power,and multifunctional artificial intelligence systems.
文摘Over the past decades,photonics has transformed many areas in both fundamental research and practical applications.In particular,we can manipulate light in a desired and prescribed manner by rationally designed subwavelength structures.However,constructing complex photonic structures and devices is still a time-consuming process,even for experienced researchers.As a subset of artificial intelligence,artificial neural networks serve as one potential solution to bypass the complicated design process,enabling us to directly predict the optical responses of photonic structures or perform the inverse design with high efficiency and accuracy.In this review,we will introduce several commonly used neural networks and highlight their applications in the design process of various optical structures and devices,particularly those in recent experimental works.We will also comment on the future directions to inspire researchers from different disciplines to collectively advance this emerging research field.
文摘Digital signal processors are extensively used to execute mathematical operations and advanced computational tasks on digital data.However,they suffer from several inherent limitations,including low speed,high energy consumption,and large memory requirements,because of the hardware bottleneck and the imperative conversion between digital and analogue signals.
文摘Effective medium theory(EMT)has been widely applied in material science,electromagnetics and photonics to determine the effective material properties for inhomogeneous composites comprising subwavelength structures.The versatility of this foundational approach has been established through its application in classical Maxwell-Garnet models,which encompass relatively simple structures。
基金supported by the National Science Foundation(Grant No.ECCS-1916839)。
文摘With its tremendous success in many machine learning and pattern recognition tasks,deep learning,as one type of data-driven models,has also led to many breakthroughs in other disciplines including physics,chemistry and material science.Nevertheless,the supremacy of deep learning over conventional optimization approaches heavily depends on the huge amount of data collected in advance to train the model,which is a common bottleneck of such a data-driven technique.In this work,we present a comprehensive deep learning model for the design and characterization of nanophotonic structures,where a self-supervised learning mechanism is introduced to alleviate the burden of data acquisition.Taking reflective metasurfaces as an example,we demonstrate that the self-supervised deep learning model can effectively utilize randomly generated unlabeled data during training,with the total test loss and prediction accuracy improved by about 15%compared with the fully supervised counterpart.The proposed self-supervised learning scheme provides an efficient solution for deep learning models in some physics-related tasks where labeled data are limited or expensive to collect.
基金the financial support of the National Science Foundation(ECCS-1916839 and CBET-1931777)the support of the National Institute of Health under grant number R21 HD090680-01support by the U.S.Army Research Office through the Institute for Soldier Nanotechnologies at MIT,under Contract Number W911NF-13-D-0001.
文摘Direct Ink Writing(DIW)has demonstrated great potential as a versatile method to 3D print multifunctional structures.In this work,we report the implementation of hydrogel meta-structures using DIW at room temperature,which seamlessly integrate large specific surface areas,interconnected porous characteristics,mechanical toughness,biocompatibility,and water absorption and retention capabilities.Robust but hydrophobic polymers and weakly crosslinked nature-origin hydrogels form a balance in the self-supporting ink,allowing us to directly print complex meta-structures without sacrificial materials and heating extrusion.Mechanically,the mixed bending or stretching of symmetrical re-entrant cellular lattices and the unique curvature patterns are combined to provide little lateral expansion and large compressive energy absorbance when external forces are applied on the printed meta-structures.In addition,we have successfully demonstrated ear,aortic valve conduits and hierarchical architectures.We anticipate that the reported 3D meta-structured hydrogel would offer a new strategy to develop functional biomaterials for tissue engineering applications in the future.
