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1. Real-time, deep-learning aided lensless microscope

   https://doi.org/10.1364/BOE.490199  

   Wu, Jimin ; Boominathan, Vivek ; Veeraraghavan, Ashok ; Robinson, Jacob T. ( July 2023 , Biomedical Optics Express)

   Traditional miniaturized fluorescence microscopes are critical tools for modern biology. Invariably, they struggle to simultaneously image with a high spatial resolution and a large field of view (FOV). Lensless microscopes offer a solution to this limitation. However, real-time visualization of samples is not possible with lensless imaging, as image reconstruction can take minutes to complete. This poses a challenge for usability, as real-time visualization is a crucial feature that assists users in identifying and locating the imaging target. The issue is particularly pronounced in lensless microscopes that operate at close imaging distances. Imaging at close distances requires shift-varying deconvolution to account for the variation of the point spread function (PSF) across the FOV. Here, we present a lensless microscope that achieves real-time image reconstruction by eliminating the use of an iterative reconstruction algorithm. The neural network-based reconstruction method we show here, achieves more than 10000 times increase in reconstruction speed compared to iterative reconstruction. The increased reconstruction speed allows us to visualize the results of our lensless microscope at more than 25 frames per second (fps), while achieving better than 7 µm resolution over a FOV of 10 mm². This ability to reconstruct and visualize samples in real-time empowers amore »more user-friendly interaction with lensless microscopes. The users are able to use these microscopes much like they currently do with conventional microscopes.

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2. Hyperspectral imaging for high-throughput, spatially resolved spectroscopic scatterometry of silicon nanopillar arrays

   https://doi.org/10.1364/OE.388158  

   Gawlik, Brian ; Barrera, Crystal ; Yu, Edward T. ; Sreenivasan, S. V. ( April 2020 , Optics Express)

   Modern high-throughput nanopatterning techniques, such as nanoimprint lithography, make it possible to fabricate arrays of nanostructures (features with dimensions of 10’s to 100’s of nm) over large area substrates (cm²to m²scale) such as Si wafers, glass sheets, and flexible roll-to-roll webs. The ability to make such large-area nanostructure arrays (LNAs) has created an extensive design space, enabling a wide array of applications including optical devices, such as wire-grid polarizers, transparent conductors, color filters, and anti-reflection surfaces, and building blocks for electronic components, such as ultracapacitors, sensors, and memory storage architectures. However, existing metrology methods will have trouble scaling alongside fabrication methods. Scanning electron microscopy (SEM) and atomic force microscopy (AFM), for instance, have micron scale fields of view (FOV) that preclude comprehensive characterization of LNAs, which may be manufactured at m²per minute rates. Scatterometry approaches have larger FOVs (typically 100’s of µm to a few mm), but traditional scatterometry systems measure samples one point at a time, which also makes them too slow for large-scale LNA manufacturing. In this work, we demonstrate parallelization of the traditional spectroscopic scatterometry approach using hyperspectral imaging, increasing the throughput of the technique by a factor of 10⁶-10⁷. We demonstrate this approach by using hyperspectralmore »imaging and inverse modeling of reflectance spectra to derive 3-dimensional geometric data for Si nanopillar array structures over both mm and cm-scale with µm-scale spatial resolution. This work suggests that geometric measurements for a variety of LNAs can be performed with the potential for high speed over large areas which may be critical for future LNA manufacturing.

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3. Design and development of WFOS, the Wide-Field Optical Spectrograph for the Thirty Meter Telescope

   https://doi.org/10.1117/12.2629464  

   Steidel, Charles C. ; Peng, Eric W. ; Fucik, Jason R. ; Nash, Reston ; Kaye, Stephen ; Jacoby, George H. ; Delabre, Bernard ; Sethuram, Ramya ; Divakar, Devika ; Varshney, Hari Mohan ; et al ( August 2022 , SPIE 12184, Ground-based and Airborne Instrumentation for Astronomy IX, 1218423 (29 August 2022)

   Evans, Christopher J. ; Bryant, Julia J. ; Motohara, Kentaro (Ed.)

   We present the current design of WFOS, a wide-field UV/optical (0.31-1.0 µm) imaging spectrograph planned for first-light on the TMT International Observatory 30 m telescope. WFOS is optimized for high sensitivity across the entire optical waveband for low-to-moderate resolution (R ∼ 1500-5000) long-slit and multi-slit spectroscopy of very faint targets over a contiguous field of view of 8′ .3×3 ′ .0 at the f/15 Nasmyth focus of TMT. A key design goal for WFOS is stability and repeatability in all observing modes, made possible by its gravity-invariant opto-mechanical structure, with a vertical rotation axis and all reconfigurable components moving only in planes defined by tiered optical benches parallel to the Nasmyth platform. WFOS’s optics include a linear ADC correcting a 9′ diameter field, including both the science FoV and 4 patrolling acquisition, guiding, and wavefront sensing camera systems; a novel 2-mirror reflective collimator allowing the science FoV to be centered on the telescope optical axis; a dichroic beamsplitter dividing the collimated beam into 2 wavelength-optimized spectrometer channels (blue: 0.31-0.56 µm; red: 0.54-1.04 µm); selectable transmissive dispersers (VPH and/or VBG) with remotely configurable grating tilt (angle of incidence) and camera articulation that enable optimization of diffraction efficiency and wavelength coverage inmore »each channel; all-refractive, wavelength-optimized f/2 spectrograph cameras, and UV/blue and red-optimized detector systems. The predicted instrumental through put of WFOS for spectroscopy averages > 56% over the full 0.31-1 µm range, from the ADC to the detector. When combined with the 30 m TMT aperture, WFOS will realize a factor of ∼20 gain in sensitivity compared to the current state of the art on 8-10 m-class telescopes.« less
4. Compact and low-cost deep-ultraviolet microscope system for label-free molecular imaging and point-of-care hematological analysis

   https://doi.org/10.1364/BOE.482294  

   Gorti, Viswanath ; Kaza, Nischita ; Williams, Evelyn Kendall ; Lam, Wilbur A. ; Robles, Francisco E. ( February 2023 , Biomedical Optics Express)

   Deep-ultraviolet (UV) microscopy enables label-free, high-resolution, quantitative molecular imaging and enables unique applications in biomedicine, including the potential for fast hematological analysis at the point-of-care. UV microscopy has been shown to quantify hemoglobin content and white blood cells (five-part differential), providing a simple alternative to the current gold standard, the hematological analyzer. Previously, however, the UV system comprised a bulky broadband laser-driven plasma light source along with a large and expensive camera and 3D translation stage. Here, we present a modified deep-UV microscope system with a compact footprint and low-cost components. We detail the novel design with simple, inexpensive optics and hardware to enable fast and accurate automated imaging. We characterize the system, including a modified low-cost web-camera and custom automated 3D translation stage, and demonstrate its ability to scan and capture large area images. We further demonstrate the capability of the system by imaging and analyzing blood smears, using previously trained networks for automatic segmentation, classification (including 5-part white blood cell differential), and colorization. The developed system is approximately 10 times less expensive than previous configurations and can serve as a point-of-care hematology analyzer, as well as be applied broadly in biomedicine as a simple compact, low-cost, quantitative molecularmore »imaging system.

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5. Intelligent nanoscope for rapid nanomaterial identification and classification

   https://doi.org/10.1039/D2LC00206J  

   Jin, Geonsoo ; Hong, Seongwoo ; Rich, Joseph ; Xia, Jianping ; Kim, Kyeri ; You, Lingchong ; Zhao, Chenglong ; Huang, Tony Jun ( June 2022 , Lab on a Chip)

   Machine learning image recognition and classification of particles and materials is a rapidly expanding field. However, nanomaterial identification and classification are dependent on the image resolution, the image field of view, and the processing time. Optical microscopes are one of the most widely utilized technologies in laboratories across the world, due to their nondestructive abilities to identify and classify critical micro-sized objects and processes, but identifying and classifying critical nano-sized objects and processes with a conventional microscope are outside of its capabilities, due to the diffraction limit of the optics and small field of view. To overcome these challenges of nanomaterial identification and classification, we developed an intelligent nanoscope that combines machine learning and microsphere array-based imaging to: (1) surpass the diffraction limit of the microscope objective with microsphere imaging to provide high-resolution images; (2) provide large field-of-view imaging without the sacrifice of resolution by utilizing a microsphere array; and (3) rapidly classify nanomaterials using a deep convolution neural network. The intelligent nanoscope delivers more than 46 magnified images from a single image frame so that we collected more than 1000 images within 2 seconds. Moreover, the intelligent nanoscope achieves a 95% nanomaterial classification accuracy using 1000 images of trainingmore »sets, which is 45% more accurate than without the microsphere array. The intelligent nanoscope also achieves a 92% bacteria classification accuracy using 50 000 images of training sets, which is 35% more accurate than without the microsphere array. This platform accomplished rapid, accurate detection and classification of nanomaterials with miniscule size differences. The capabilities of this device wield the potential to further detect and classify smaller biological nanomaterial, such as viruses or extracellular vesicles.« less