We outline an experimental setup for efficiently preparing a tweezer array of^(88)Sr atoms.Our setup uses permanent magnets to maintain a steady-state two-dimensional magneto-optical trap(MOT)which results in a loadin...We outline an experimental setup for efficiently preparing a tweezer array of^(88)Sr atoms.Our setup uses permanent magnets to maintain a steady-state two-dimensional magneto-optical trap(MOT)which results in a loading rate of up to10^(8)s^(-1)at 5 mK for the three-dimensional blue MOT.This enables us to trap 2×10^(6)^(88)Sr atoms at 2μK in a narrow-line red MOT with the^(1)S_(0)→^(3)P_(1)intercombination transition at 689 nm.With the Sisyphus cooling and pairwise loss processes,single atoms are trapped and imaged in 813 nm optical tweezers,exhibiting a lifetime of 2.5 min.We further investigate the survival fraction of a single atom in the tweezers and characterize the optical tweezer array using a release and recapture technique.Our experimental setup serves as an excellent reference for those engaged in experiments involving optical tweezer arrays,cold atom systems,and similar research.展开更多
Autonomous one-shot on-the-fly learning copes with the high privacy,small dataset,and in-stream data at the edge.Implementing such learning on digital hardware suffers from the well-known von-Neumann and scaling bottl...Autonomous one-shot on-the-fly learning copes with the high privacy,small dataset,and in-stream data at the edge.Implementing such learning on digital hardware suffers from the well-known von-Neumann and scaling bottlenecks.The optical neural networks featuring large parallelism,low latency,and high efficiency offer a promising solution.However,ex-situ training of conventional optical networks,where optical path configuration and deep learning model optimization are separated,incurs hardware,energy and time overheads,and defeats the advantages in edge learning.Here,we introduced a bio-inspired material-algorithm co-design to construct a hydrogel-based optical Willshaw model(HOWM),manifesting Hebbian-rule-based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto-chemical reactions.We first employed the HOWM as an all optical in-sensor AI processor for one-shot pattern classification,association and denoising.We then leveraged HOWM to function as a ternary content addressable memory(TCAM)of an optical memory augmented neural network(MANN)for one-shot learning the Omniglot dataset.The HOWM empowered one-shot on-the-fly edge learning leads to 1000boost of energy efficiency and 10boost of speed,which paves the way for the next-generation autonomous,efficient,and affordable smart edge systems.展开更多
基金support from the RGC through 16306119,16302420,16302821,16306321,16306922,16302123,C6009-20G,N-HKUST636-22,and RFS21226S04support from the Guangzhou and Nansha District Postdoctoral Projectsupport from the RGC for RGC Postdoctoral fellowship。
文摘We outline an experimental setup for efficiently preparing a tweezer array of^(88)Sr atoms.Our setup uses permanent magnets to maintain a steady-state two-dimensional magneto-optical trap(MOT)which results in a loading rate of up to10^(8)s^(-1)at 5 mK for the three-dimensional blue MOT.This enables us to trap 2×10^(6)^(88)Sr atoms at 2μK in a narrow-line red MOT with the^(1)S_(0)→^(3)P_(1)intercombination transition at 689 nm.With the Sisyphus cooling and pairwise loss processes,single atoms are trapped and imaged in 813 nm optical tweezers,exhibiting a lifetime of 2.5 min.We further investigate the survival fraction of a single atom in the tweezers and characterize the optical tweezer array using a release and recapture technique.Our experimental setup serves as an excellent reference for those engaged in experiments involving optical tweezer arrays,cold atom systems,and similar research.
基金supported by the National Key R&D Program of China(Grant No.2018YFA0701500)Hong Kong Research Grant Council(Grant No.27206321,17205922)+5 种基金the National Natural Science Foundation of China(Grant Nos.62122004,61874138,61888102,61771176,and 62171173)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB44000000)Research on the GaN Chip for 5G Applications(Grant No:JCYJ20210324120409025)Research on high-reliable GaN power device and the related industrial power system(Grant No:HZQBKCZYZ-2021052)Key Project of Department of Education of Guangdong Province(No.2018KCXTD026)supported by ACCESS-AI Chip Center for Emerging Smart Systems,sponsored by Innovation and Technology Fund(ITF),Hong Kong SAR.
文摘Autonomous one-shot on-the-fly learning copes with the high privacy,small dataset,and in-stream data at the edge.Implementing such learning on digital hardware suffers from the well-known von-Neumann and scaling bottlenecks.The optical neural networks featuring large parallelism,low latency,and high efficiency offer a promising solution.However,ex-situ training of conventional optical networks,where optical path configuration and deep learning model optimization are separated,incurs hardware,energy and time overheads,and defeats the advantages in edge learning.Here,we introduced a bio-inspired material-algorithm co-design to construct a hydrogel-based optical Willshaw model(HOWM),manifesting Hebbian-rule-based structural plasticity for simultaneous optical path configuration and deep learning model optimization thanks to the underlying opto-chemical reactions.We first employed the HOWM as an all optical in-sensor AI processor for one-shot pattern classification,association and denoising.We then leveraged HOWM to function as a ternary content addressable memory(TCAM)of an optical memory augmented neural network(MANN)for one-shot learning the Omniglot dataset.The HOWM empowered one-shot on-the-fly edge learning leads to 1000boost of energy efficiency and 10boost of speed,which paves the way for the next-generation autonomous,efficient,and affordable smart edge systems.
