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1. High-efficiency high-numerical-aperture metalens designed by maximizing the efficiency limit

   https://doi.org/10.1364/OPTICA.514907  

   Li, Shiyu; Lin, Ho-Chun; Hsu, Chia_Wei (March 2024, Optica)

   Theoretical bounds are commonly used to assess the limitations of photonic design. Here we introduce a more active way to use theoretical bounds, integrating them into part of the design process and identifying optimal system parameters that maximize the efficiency limit itself. As an example, we consider wide-field-of-view high-numerical-aperture metalenses, which can be used for high-resolution imaging in microscopy and endoscopy, but no existing design has achieved a high efficiency. By choosing aperture sizes to maximize an efficiency bound, setting the thickness according to a thickness bound, and then performing inverse design, we come up with high-numerical-aperture (NA=0.9) metalens designs with, to our knowledge, record-high 98% transmission efficiency and 92% Strehl ratio across all incident angles within a 60° field of view, reaching the maximized bound. This maximizing-efficiency-limit approach applies to any multi-channel system and can help a wide range of optical devices reach their highest possible performance. 

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2. Transmission Efficiency Limit for Nonlocal Metalenses

   https://doi.org/10.1002/lpor.202300201  

   Li, Shiyu; Hsu, Chia Wei (June 2023, Laser & Photonics Reviews)

   Abstract The rapidly advancing capabilities in nanophotonic design are enabling complex functionalities limited mainly by physical bounds. The efficiency of transmission is a major consideration, but its ultimate limit remains unknown for most systems. This study introduces a matrix formalism that puts a fundamental bound on the channel‐averaged transmission efficiency of any passive multi‐channel optical system based only on energy conservation and the desired functionality, independent of the interior structure and material composition. Applying this formalism to diffraction‐limited nonlocal metalenses with a wide field of view shows that the transmission efficiency must decrease with the numerical aperture for the commonly adopted designs with equal entrance and output aperture diameters. It also shows that reducing the size of the entrance aperture can raise the efficiency bound. This study reveals a fundamental limit on the transmission efficiency as well as provides guidance for the design of high‐efficiency multi‐channel optical systems. 

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3. Delineation of the impact on temporal behaviors of off-axis photoemission in an ultrafast electron microscope

   https://doi.org/10.1063/5.0222993  

   Chen, Jialiang; Willis, Simon A; Flannigan, David J (September 2024, Review of Scientific Instruments)

   Efforts to push the spatiotemporal imaging-resolution limits of femtosecond laser-driven ultrafast electron microscopes (UEMs) to the combined angstrom–fs range will benefit from stable sources capable of generating high bunch charges. Recent demonstrations of unconventional off-axis photoemitting geometries are promising, but connections to the observed onset of structural dynamics are yet to be established. Here we use the in-situ photoexcitation of coherent phonons to quantify the relative time-of-flight (r-TOF) of photoelectron packets generated from the Ni Wehnelt aperture and from a Ta cathode set-back from the aperture plane. We further support the UEM experiments with particle-tracing simulations of the precise electron-gun architecture and photoemitting geometries. In this way, we measure discernible shifts in electron-packet TOF of tens of picoseconds for the two photoemitting surfaces. These shifts arise from the impact that the Wehnelt-aperture off-axis orientation has on the electron-momentum distribution, which modifies both the collection efficiency and the temporal-packet distribution relative to on-axis emission. Future needs are identified; we expect this and other developments in UEM electron-gun configuration to expand the range of material phenomena that can be directly imaged on scales commensurate with fundamental structural dynamics. 

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4. Fast extended depth of focus meta-optics for varifocal functionality

   https://doi.org/10.1364/PRJ.434681  

   Whitehead, James E. M.; Zhan, Alan; Colburn, Shane; Huang, Luocheng; Majumdar, Arka (March 2022, Photonics Research)

   Extended depth of focus (EDOF) optics can enable lower complexity optical imaging systems when compared to active focusing solutions. With existing EDOF optics, however, it is difficult to achieve high resolution and high collection efficiency simultaneously. The subwavelength spacing of scatterers in a meta-optic enables the engineering of very steep phase gradients; thus, meta-optics can achieve both a large physical aperture and a high numerical aperture. Here, we demonstrate a fast $(f/1.75)$EDOF meta-optic operating at visible wavelengths, with an aperture of 2 mm and focal range from 3.5 mm to 14.5 mm (286 diopters to 69 diopters), which is a $250\times$elongation of the depth of focus relative to a standard lens. Depth-independent performance is shown by imaging at a range of finite conjugates, with a minimum spatial resolution of $\sim 9.84\mathrm{\mu m}$(50.8 cycles/mm). We also demonstrate operation of a directly integrated EDOF meta-optic camera module to evaluate imaging at multiple object distances, a functionality which would otherwise require a varifocal lens. 

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5. Decoding the Hidden: Direct Image Classification Using Coded Aperture Imaging

   https://doi.org/10.1109/CAMSAP58249.2023.10403419  

   Munoz, Jocelyn Ornelas; Rutter, Erica M.; Marcia, Roummel F. (December 2023, 2023 IEEE 9th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP))

   Coded aperture imaging has emerged as a solution to enhance light sensitivity and enable imaging in challenging conditions. However, the computational expense of image reconstruction poses limitations in processing efficiency. To address this, we propose a direct classification method using convolutional neural networks. By leveraging raw coded measurements, our approach eliminates the need for explicit image reconstruction, reducing computational overhead. We evaluate the effectiveness of this approach compared to traditional methods on the MNIST and CIFAR10 datasets. Our results demonstrate that direct image classification using raw coded measurements achieves comparable performance to traditional methods while reducing computational overhead and enabling real-time processing. These findings highlight the potential of machine learning in enhancing the decoding process and improving the overall performance of coded aperture imaging systems. 

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