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We report on the 1,000-element transition-edge sensor (TES) x-ray spectrometer implementation of the TOMographic Circuit Analysis Tool (TOMCAT). TOMCAT combines a high spatial resolution scanning electron microscope (SEM) with a highly efficient and pixelated TES spectrometer to reconstruct three-dimensional maps of nanoscale integrated circuits (ICs). A 240-pixel prototype spectrometer was recently used to reconstruct ICs at the 130 nm technology node, but to increase imaging speed to more practical levels, the detector efficiency needs to be improved. For this reason, we are building a spectrometer that will eventually contain 3,000 TES microcalorimeters read out with microwave superconducting quantum interference device (SQUID) multiplexing, and we currently have commissioned a 1,000 TES subarray. This still represents a significant improvement from the 240-pixel system and allows us to begin characterizing the full spectrometer performance. Of the 992 maximimum available readout channels, we have yielded 818 devices, representing the largest number of TES x-ray microcalorimeters simultaneously read out to date. These microcalorimeters have been optimized for pulse speed rather than purely energy resolution, and we measure a FWHM energy resolution of 14 eV at the 8.0 keV Cu K$\alpha$ line., Comment: 5 pages, 4 figures, submitted to IEEE Transactions on Applied Superconductivity

Feature sizes in integrated circuits have decreased substantially over time, and it has become increasingly difficult to three-dimensionally image these complex circuits after fabrication. This can be important for process development, defect analysis, and detection of unexpected structures in externally sourced chips, among other applications. Here, we report on a non-destructive, tabletop approach that addresses this imaging problem through x-ray tomography, which we uniquely realize with an instrument that combines a scanning electron microscope (SEM) with a transition-edge sensor (TES) x-ray spectrometer. Our approach uses the highly focused SEM electron beam to generate a small x-ray generation region in a carefully designed target layer that is placed over the sample being tested. With the high collection efficiency and resolving power of a TES spectrometer, we can isolate x-rays generated in the target from background and trace their paths through regions of interest in the sample layers, providing information about the various materials along the x-ray paths through their attenuation functions. We have recently demonstrated our approach using a 240 Mo/Cu bilayer TES prototype instrument on a simplified test sample containing features with sizes of $\sim$1 $\mu$m. Currently, we are designing and building a 3000 Mo/Au bilayer TES spectrometer upgrade, which is expected to improve the imaging speed by factor of up to 60 through a combination of increased detector number and detector speed., Comment: 5 pages, 3 figures, published in IEEE Transactions on Applied Superconductivity

Speckle-based X-ray phase contrast imaging (XPCI) is a relatively simple implementation of phase contrast imaging. At low energies, the technique has been demonstrated with masks made from steel wool and sandpaper. However, these materials are too transparent for higher energy applications. The simple geometry and easy identification of, or fabrication of, materials for relevant speckle masks make speckle-based XPCI a compelling technique for widespread use. We have analyzed the trade space for higher energy speckle-based XPCI systems based on portable X-ray tube sources. We have demonstrated several fabrication techniques compatible with a range of materials. Together these enable variation in feature size, material density, and randomness in the mask. This ability to tune the mask parameters allows optimization of the mask for the application space and system design.

X-ray phase contrast imaging (XPCI) is a nondestructive evaluation technique that enables high-contrast detection of low-attenuation materials that are largely transparent in traditional radiography. Extending a grating-based Talbot-Lau XPCI system to three-dimensional imaging with computed tomography (CT) imposes two motion requirements: the analyzer grating must translate transverse to the optical axis to capture image sets for XPCI reconstruction, and the sample must rotate to capture angular data for CT reconstruction. The acquisition algorithm choice determines the order of movement and positioning of the two stages. The choice of the image acquisition algorithm for XPCI CT is instrumental to collecting high fidelity data for reconstruction. We investigate how data acquisition influences XPCI CT by comparing two simple data acquisition algorithms and determine that capturing a full phase-stepping image set for a CT projection before rotating the sample results in higher quality data.

Polyurethane foams are used widely for encapsulation and structural purposes because they are inexpensive, straightforward to process, amenable to a wide range of density variations (1–50 lb/ft3), and able to fill complex molds quickly and effectively. Computational models of the filling and curing process are needed to reduce defects such as voids, out-of-specification density, density gradients, foam decomposition from high temperatures due to exotherms, and incomplete filling. This paper details the development of a computational fluid dynamics model of a moderate density PMDI structural foam, PMDI-10. PMDI is an isocyanate-based polyurethane foam, which is chemically blown with water. The polyol reacts with isocyanate to produce the polymer. PMDI-10 is catalyzed giving it a short pot life, meaning that the foam is liquid and flowable only for a short-time; it foams and polymerizes to a solid within 5 min during normal processing. To achieve a higher density, the foam is over-packed to twice or more of its free-rise density of 10 lb/ft3. The goal for modeling is to represent the expansion, filling of molds, and the polymerization of the foam. This model will be used to reduce defects, optimize the mold design, understand processing parameter sensitivities, and predict the final foam properties. A homogenized continuum model for foaming and curing was developed based on reaction kinetics, documented in a recent paper; it uses a simplified mathematical formalism that decouples these two reactions. The chemo-rheology of PMDI is measured experimentally and fit to a generalized-Newtonian viscosity model that is dependent on the extent of cure, gas volume fraction, and temperature. The conservation equations, including the equations of motion, an energy balance, and three rate equations are solved using a stabilized finite element method. The equations are combined with a level set method to determine the location of the foam-gas interface as it evolves to fill the mold. Understanding the thermal history and loads on the foam due to exothermicity and oven curing is very important to the results, since the kinetics, viscosity, and other material properties are all sensitive to temperature. Results from the model are compared to experimental flow visualization data to understand the interface evolution with time and post-test X-ray computed tomography (CT) data for the density prediction validation. Several geometries are investigated including two configurations of a mock structural part and a bar geometry to specifically test the density model. We have found that the model predicts both average density and filling profiles well. However, it under predicts density gradients, especially in the gravity direction. Further model improvements are also discussed for future work.

Sandia National Laboratories is developing a laboratory-based x-ray phase contrast imaging (XPCI) computed tomography (CT) system. This system utilizes a Talbot-Lau interferometer based on in-house fabricated gratings and a conventional x-ray system. Initial work has focused on adding CT capabilities to a 28 keV XPCI system. A new set of gratings tuned for an x-ray energy of 100 keV is being developed. This new grating set will facilitate imaging denser components. System configuration details will be presented as well as a discussion of the challenges associated with building an XPCI CT system. Additionally, initial imaging results will be presented.

Sandia National Laboratories has developed a method that applies machine learning methods to high-energy spectral x-ray computed tomography data to identify material composition for every reconstructed voxel in the field-of-view. While initial experiments led by Koundinyan et al. demonstrated that supervised machine learning techniques perform well in identifying a variety of classes of materials, this work presents an unsupervised approach that differentiates isolated materials with highly similar properties, and can be applied on spectral computed tomography data to identify materials more accurately compared to traditional performance. Additionally, if regions of the spectrum for multiple voxels become unusable due to artifacts, this method can still reliably perform material identification. This enhanced capability can tremendously impact fields in security, industry, and medicine that leverage non-destructive evaluation for detection, verification, and validation applications.

Sandia National Laboratories has recently developed the capability to acquire multi-channel radio- graphs for multiple research and development applications in industry and security. This capability allows for the acquisition of x-ray radiographs or sinogram data to be acquired at up to 300 keV with up to 128 channels per pixel. This work will investigate whether multiple quality metrics for computed tomography can actually benefit from binned projection data compared to traditionally acquired grayscale sinogram data. Features and metrics to be evaluated include the ability to dis- tinguish between two different materials with similar absorption properties, artifact reduction, and signal-to-noise for both raw data and reconstructed volumetric data. The impact of this technology to non-destructive evaluation, national security, and industry is wide-ranging and has to potential to improve upon many inspection methods such as dual-energy methods, material identification, object segmentation, and computer vision on radiographs.

Session ratings of perceived exertion (SRPE) during resistance training may be influenced by specific exercise parameters. The purpose of this study was to examine the influence of work rate (total work per unit time) and recording time on SRPE. Participants performed 3 exercise bouts of bench press, lat pull-down, overhead press, upright row, triceps extension, and biceps curl at 60% of predetermined 1 repetition maximum according to these protocols: (a) 3 sets × 8 repetitions (reps) × 1.5 minutes of recovery, (b) 3 sets × 8 reps × 3 minutes of recovery, and (c) 2 sets × 12 reps × 3 minutes of recovery. Session ratings of perceived exertion for the 3 × 8 × 1.5-minute recovery (5.3 ± 1.8) and 2 × 12 × 3-minute recovery trials (6.2 ± 1.7) were significantly greater vs. 3 × 8 × 3-minute recovery trial (4.2 ± 1.8). The difference approached significance between work rate-matched protocols (p = 0.08). No difference was observed between SRPE at 15 minutes (5.1 ± 1.8) vs. 30 minutes (5.2 ± 1.9) post exercise. Post-set in-task ratings of perceived exertion were higher for the 2 × 12 × 3-minute recovery trial (5.9 ± 1.4) vs. 3 × 8 × 1.5-minute recovery trial (4.8 ± 1.2) and 3 × 8 × 3-minute recovery trial (4.0 ± 1.6). The difference approached significance (p = 0.07) for the 3 × 8 × 3-minute recovery trial vs. 3 × 8 × 1.5-minute recovery trial. Session ratings of perceived exertion responded to changes in work rate with no significant difference at matched work rates, indicating that SRPE is responsive to training load. Results indicated that more proximal monitoring (15 minutes post exercise) yielded reliable estimates of SRPE increasing the practical utility of the measure.

This work will investigate the imaging capabilities of the Multix multi-channel linear array detector and its potential suitability for big-data industrial and security applications versus that which is currently deployed. Multi-channel imaging data holds huge promise in not only finer resolution in materials classification, but also in materials identification and elevated data quality for various radiography and computed tomography applications. The potential pitfall is the signal quality contained within individual channels as well as the required exposure and acquisition time necessary to obtain images comparable to those of traditional configurations. This work will present results of these detector technologies as they pertain to a subset of materials of interest to the industrial and security communities; namely, water, copper, lead, polyethylene, and tin.

Epoxy resins filled to a high solids loading (40–60% by volume) with noncolloidal particles are used to mitigate stress and vibration in electronic components. We perform continuum-level finite element method (Schunk et al., A Full-Newton Finite Element Program for Free and Moving Boundary Problems with Coupled Fluid/Solid Momentum, Energy, Mass, and Chemical Species Transport: User's Guide, Sandia National Laboratories) simulations of filler particle redistribution during the nonisothermal cure of the epoxy under both quiescent and bulk flow conditions. An extent of reaction is used to track the degree of cure. To determine the particle migration, we couple a diffusive flux suspension model (Zhang and Acrivos, Int J Multiphase Flow 1994, 20, 579.) with the curing model. The heat transfer, including the exothermic polymerization reaction, is also modeled. The result is a generalized Newtonian model that has viscosity as a function of temperature, cure and particle volume fraction. With x-ray computed tomography, we examine settling of the particulate phase in both flowing and quiescent curing systems and compare the experimental results to the model predictions. The model is also validated with temperature measurements. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011

Individual bubbles rising through suspensions of spherical particles neutrally buoyant in viscous Newtonian liquids are tracked with real time radiography. The effects of the containing cylinder, suspended particle, and bubble sizes are reported for particle volume fractions ranging from 0.20 to 0.50. In the most concentrated suspensions, the effect of the bottom surface is felt by the bubble much farther into the suspension than would be true for a single-phase Newtonian liquid. Corrected for wall effects, the apparent viscosity felt by the bubbles is independent of the relative size of the bubble to the particle over a wide range of parameters, and is approximately linear with the suspended particle volume fraction, in contrast to the exponential dependence derived in conventional rheometry. Finally, in the most concentrated suspensions, the bubble deforms to a prolate spheroidal shape despite the lack of significant inertial effects or a viscoelastic suspending liquid. © 2008 American Institute of Chemical Engineers AIChE J, 2008

The goal of this work is to develop techniques for measuring gradients in particle concentration within filled polymers, such as thermosetting polymer encapsulants. A high concentration of filler particles is added to such materials to tailor physical properties such as thermal expansion coefficient. Sedimentation and flow-induced migration of particles can produce concentration gradients that are most severe near boundaries. Therefore, techniques for measuring local particle concentration should be accurate near boundaries. Particle gradients in an alumina-filled epoxy resin are measured with a spatial resolution of 0.2 mm using an X-ray beam attenuation technique, but an artifact reduces accuracy near the specimen's edge. Local particle concentration near an edge can be measured more reliably using microscopy coupled with image analysis. This is illustrated by measuring concentration profiles of glass particles having 40 μm median diameter using images acquired with a confocal laser fluorescence microscope. The mean of the measured profiles of volume fraction agrees to better than 3% with the expected value and the shape of the profiles agrees qualitatively with simple theory for sedimentation of monodisperse particles. Evidence that the microscopic method can be extended to smaller particles is provided by local concentration measurements on an epoxy polymer containing particles having diameters of the order of 1 μm.

Conventional CPU-based algorithms for Computed Tomography reconstruction lack the computational efficiency necessary to process large, industrial datasets in a reasonable amount of time. Specifically, processing time for a single-pass, trillion volumetric pixel (voxel) reconstruction requires months to reconstruct using a high performance CPU-based workstation. An optimized, single workstation multi-GPU approach has shown performance increases by 2–3 orders-of-magnitude; however, reconstruction of future-size, trillion voxel datasets can still take an entire day to complete. This paper details an approach that further decreases runtime and allows for more diverse workstation environments by using a cluster of GPU-capable workstations. Due to the irregularity of the reconstruction tasks throughout the volume, using a cluster of multi-GPU nodes requires inventive topological structuring and data partitioning to avoid network bottlenecks and achieve optimal GPU utilization. This paper covers the cluster layout and non-linear weighting scheme used in this high-performance multi-GPU CT reconstruction algorithm and presents experimental results from reconstructing two large-scale datasets to evaluate this approach's performance and applicability to future-size datasets. Specifically, our approach yields up to a 20 percent improvement for large-scale data.

This exploratory work investigates the feasibility of extracting linear attenuation functions with respect to energy from a multi-channel radiograph of an object of interest composed of a homogeneous material by simulating the entire imaging system combined with a digital phantom of the object of interest and leveraging this information along with the acquired multi-channel image. This synergistic combination of information allows for improved estimates on not only the attenuation for an effective energy, but for the entire spectrum of energy that is coincident with the detector elements. Material composition identification from radiographs would have wide applications in both medicine and industry. This work will focus on industrial radiography applications and will analyse a range of materials that vary in attenuative properties. This work shows that using iterative solvers holds encouraging potential to fully solve for the linear attenuation profile for the object and material of interest when the imaging system is characterized with respect to initial source x-ray energy spectrum, scan geometry, and accurate digital phantom.

This work describes a high-performance approach to radiograph (i.e. X-ray image for this work) simulation for arbitrary objects. The generation of radiographs is more generally known as the forward projection imaging model. The formation of radiographs is very computationally expensive and is not typically approached for large-scale applications such as industrial radiography. The approach described in this work revolves around a single GPU-based implementation that performs the attenuation calculation in a massively parallel environment. Additionally, further performance gains are realized by exploiting the GPU-specific hardware. Early results show that using a single GPU can increase computational performance by three orders-of- magnitude for volumes of 10003 voxels and images with 10002 pixels.

Estimation of the x-ray attenuation properties of an object with respect to the energy emitted from the source is a challenging task for traditional Bremsstrahlung sources. This exploratory work attempts to estimate the x-ray attenuation profile for the energy range of a given Bremsstrahlung profile. Previous work has shown that calculating a single effective attenuation value for a polychromatic source is not accurate due to the non-linearities associated with the image formation process. Instead, we completely characterize the imaging system virtually and utilize an iterative search method/constrained optimization technique to approximate the attenuation profile of the object of interest. This work presents preliminary results from various approaches that were investigated. The early results illustrate the challenges associated with these techniques and the potential for obtaining an accurate estimate of the attenuation profile for objects composed of homogeneous materials.

This paper will investigate energy-efficiency for various real-world industrial computed-tomography reconstruction algorithms, both CPU- and GPU-based implementations. This work shows that the energy required for a given reconstruction is based on performance and problem size. There are many ways to describe performance and energy efficiency, thus this work will investigate multiple metrics including performance-per-watt, energy-delay product, and energy consumption. This work found that irregular GPU-based approaches 1 realized tremendous savings in energy consumption when compared to CPU implementations while also significantly improving the performance-per- watt and energy-delay product metrics. Additional energy savings and other metric improvement was realized on the GPU-based reconstructions by improving storage I/O by implementing a parallel MIMD-like modularization of the compute and I/O tasks.

We are developing computational models to elucidate the expansion and dynamic filling process of a polyurethane foam, PMDI. The polyurethane of interest is chemically blown, where carbon dioxide is produced via the reaction of water, the blowing agent, and isocyanate. The isocyanate also reacts with polyol in a competing reaction, which produces the polymer. Here we detail the experiments needed to populate a processing model and provide parameters for the model based on these experiments. The model entails solving the conservation equations, including the equations of motion, an energy balance, and two rate equations for the polymerization and foaming reactions, following a simplified mathematical formalism that decouples these two reactions. Parameters for the polymerization kinetics model are reported based on infrared spectrophotometry. Parameters describing the gas generating reaction are reported based on measurements of volume, temperature and pressure evolution with time. A foam rheology model is proposed and parameters determined through steady-shear and oscillatory tests. Heat of reaction and heat capacity are determined through differential scanning calorimetry. Thermal conductivity of the foam as a function of density is measured using a transient method based on the theory of the transient plane source technique. Finally, density variations of the resulting solidmore » foam in several simple geometries are directly measured by sectioning and sampling mass, as well as through x-ray computed tomography. These density measurements will be useful for model validation once the complete model is implemented in an engineering code.« less

There has been much work done in implementing various GPU-based Computed Tomography reconstruction algorithms for medical applications showing tremendous improvement in computational performance. While many of these reconstruction algorithms could also be applied to industrial-scale datasets, the performance gains may be modest to non-existent due to a combination of algorithmic, hardware, or scalability limitations. Previous work presented showed an irregular dynamic approach to GPU-Reconstruction kernel execution for industrial-scale reconstructions that dramatically improved voxel processing throughput. However, the improved kernel execution magnified other system bottlenecks such as host memory bandwidth and storage read/write bandwidth, thus hindering performance gains. This paper presents a multi-GPU-based reconstruction algorithm capable of efficiently reconstructing large volumes (between 64 gigavoxels and 1 teravoxel volumes) not only faster than traditional CPU- and GPU-based reconstruction algorithms but also while consuming significantly less energy. The reconstruction algorithm exploits the irregular kernel approach from previous work as well as a modularized MIMD-like environment, heterogeneous parallelism, as well as macro- and micro-scale dynamic task allocation. The result is a portable and flexible reconstruction algorithm capable of executing on a wide range of architectures including mobile computers, workstations, supercomputers, and modestly-sized hetero or homogeneous clusters with any number of graphics processors.

Explosive growth in photovoltaic markets has fueled new creative approaches that promise to cut costs and improve reliability of system components. However, market demands require rapid development of these new and innovative technologies in order to compete with more established products and capture market share. Oftentimes diagnostics that assist in R&D do not exist or have not been applied due to the innovative nature of the proposed products. Some diagnostics such as IR imaging, electroluminescence, light IV, dark IV, x-rays, and ultrasound have been employed in the past and continue to serve in development of new products, however, innovative products with new materials, unique geometries, and previously unused manufacturing processes require additional or improved test capabilities. This fast-track product development cycle requires diagnostic capabilities to provide the information that confirms the integrity of manufacturing techniques and provides the feedback that can spawn confidence in process control, reliability and performance. This paper explores the use of digital radiography and computed tomography (CT) with other diagnostics to support photovoltaic R&D and manufacturing applications.

A series of experiments has been performed to allow observation of the foaming process and the collection of temperature, rise rate, and microstructural data. Microfocus video is used in conjunction with particle image velocimetry (PIV) to elucidate the boundary condition at the wall. Rheology, reaction kinetics and density measurements complement the flow visualization. X-ray computed tomography (CT) is used to examine the cured foams to determine density gradients. These data provide input to a continuum level finite element model of the blowing process.

Manufacturing applications for filled polymers include encapsulation of microelectronics and injection molding of composite parts. Predictive tools for simulating these manufacturing processes require knowledge of time- and temperature-dependent rheology of the polymer as well as information about local particle concentration. The overall system rheology is highly dependent on the particle concentration. The local particle concentration can change due to gravity, convection and shear-induced migration. For the epoxy systems of interest, an extent of reaction can be used to track the degree of cure. We couple the curing model with a diffusive flux suspension model [Zhang and Acrivos 1994] to determine the particle migration. This results in a generalized Newtonian model that has viscosity as a function of temperature, cure and concentration. Using this model, we examine settling of the particulate phase in both flowing and quiescent curing systems. We focus on settling in molds and flow in wide-gap counter-rotating cylinders. The heat transfer, including the exothermic polymerization reaction, must be modeled to achieve accurate results. The model is validated with temperature measurements and post-test microscopy data. Particle concentration is determined with x-ray microfocus visualization or confocal microscopy. Agreement between the simulations and experimental results is fair.Copyright © 2003 by ASME

As a result of an arms reduction treaty between the United States and the Russian Federation, both countries will each be storing over 40,000 containers of plutonium. To help detect any deterioration of the containers and prevent leakage, we are designing a digital radiography and computed tomography system capable of handling this volume reliably, efficiently, and at a lower cost.