Multivitamin Use Among Hispanic Women of Reproductive Age in the United States, 2013–2022
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General & Internal Medicine↗
Engineering topics
Yue, Xin
Publications and source records attributed to Yue, Xin.
Ultrafast radiographic imaging and tracking: An overview of instruments, methods, data, and applications
Ultrafast radiographic imaging and tracking (U-RadIT) use state-of-the-art ionizing particle and light sources to experimentally study sub-nanosecond transients or dynamic processes in physics, chemistry, biology, geology, materials science and other fields. These processes are fundamental to modern technologies and applications, such as nuclear fusion energy, advanced manufacturing, communication, and green transportation, which often involve one mole or more atoms and elementary particles, and thus are challenging to compute by using the first principles of quantum physics or other forward models. One of the central problems in U-RadIT is to optimize information yield through, e.g. high-luminosity X-ray and particle sources, efficient imaging and tracking detectors, novel methods to collect data, and large-bandwidth online and offline data processing, regulated by the underlying physics, statistics, and computing power. We review and highlight recent progress in: (a.) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; (b.) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; (c.) U-RadIT data and algorithms such as neural networks and machine learning, and (d.) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardware-centric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs. Interpretation of experimental data, including comparisons with forward models, is frequently hindered by sparse measurements, model and measurement uncertainties, and noise. Alternatively, U-RadIT make increasing use of data science and machine learning algorithms, including experimental implementations of compressed sensing. Machine learning and artificial intelligence approaches, refined by physics and materials information, may also contribute significantly to data interpretation, uncertainty quantification and U-RadIT optimization.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Design of Monolithic Bi-Layer High-Z PAL-Si Hard X-ray CMOS Image Sensors for Quantum Efficiency Enhancement
This article experimentally investigates the inception of an innovative hard X-ray photon energy attenuation layer (PAL) to advance high-energy X-ray detection (20–50 keV). A bi-layer design with a thin film high-Z PAL on the top and Si image sensor on the bottom has previously demon-strated quantum yield enhancement via computational methods by the principle of photon energy down conversion (PEDC), where high-energy X-ray photon energies are attenuated via inelastic scattering down to ≤10 keV, which is suitable for efficient photoelectric absorption by Si. Quantum yield enhancement has been experimentally confirmed via a preliminary demonstration using PAL-integrated Si-based CMOS image sensors (Si CIS). Furthermore, substituting the high-Z PAL with a lower-Z material—Sn—and alternatively coupling it with a conventional scintillator ma-terial—Lutetium-yttrium oxyorthosilicate (LYSO)—have been compared to demonstrate the most prominent efficacy of monolithic integration of high-Z PAL on Si CIS to detect hard X-rays, paving the way for next-generation high-energy X-ray detection methods.
47 OTHER INSTRUMENTATION↗
Design and Characterization of a Burst Mode 20 Mfps Low Noise CMOS Image Sensor
This paper presents a novel ultra-high speed, high conversion-gain, low noise CMOS image sensor (CIS) based on charge-sweep transfer gates implemented in a standard 180 nm CIS process. Through the optimization of the photodiode geometry and the utilization of charge-sweep transfer gates, the proposed pixels achieve a charge transfer time of less than 10 ns without requiring any process modifications. Moreover, the gate structure significantly reduces the floating diffusion capacitance, resulting in an increased conversion gain of 183 µV/e−. This advancement enables the image sensor to achieve the lowest reported noise of 5.1 e− rms. To demonstrate the effectiveness of both optimizations, a proof-of-concept CMOS image sensor is designed, taped-out and characterized.
47 OTHER INSTRUMENTATION↗
Ultrafast CMOS image sensors and data-enabled super-resolution for multimodal radiographic imaging and tomography
We summarize recent progress in ultrafast Complementary Metal Oxide Semiconductor (CMOS) image sensor development and the application of neural networks for post-processing of CMOS and charge-coupled device (CCD) image data to achieve sub-pixel resolution (thus ‘super-resolution’). The combination of novel CMOS pixel designs and data-enabled image post-processing provides a promising path towards ultrafast high-resolution multi-modal radiographic imaging and tomography applications.
47 OTHER INSTRUMENTATION↗
Ultrafast CMOS image sensors and data-enabled super-resolution for multimodal radiographic imaging and tomography
We summarize recent progress in ultrafast Complementary Metal Oxide Semiconductor (CMOS) image sensor development and the application of neural networks for post-processing of CMOS and charge-coupled device (CCD) image data to achieve sub-pixel resolution (thus $super$-$resolution$). The combination of novel CMOS pixel designs and data-enabled image post-processing provides a promising path towards ultrafast high-resolution multi-modal radiographic imaging and tomography applications.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Billion-pixel x-ray camera (BiPC-X)
The continuing improvement in quantum efficiency (above 90% for single visible photons), reduction in noise (below 1 electron per pixel), and shrink in pixel pitch (less than 1 μm) enable billion-pixel x-ray cameras (BiPC-X) based on commercial complementary metal–oxide–semiconductor (CMOS) imaging sensors. We describe BiPC-X designs and prototype construction based on flexible tiling of commercial CMOS imaging sensors with millions of pixels. Device models are given for direct detection of low energy x rays (<10 keV) and indirect detection of higher energies using scintillators. Modified Birks’s law is proposed for light yield non-proportionality in scintillators as a function of x-ray energy. Single x-ray sensitivity and spatial resolution have been validated experimentally using a laboratory x-ray source and the Argonne Advanced Photon Source. Possible applications include wide field-of-view or large x-ray aperture measurements in high-temperature plasmas, the state-of-the-art synchrotron, x-ray free electron laser, and pulsed power facilities.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗