Anisotropic etching silicon (Si) is an active area of research for applications including energy storage (Enovix), energy conversion (photovoltaics), and now for x-ray phase contrast imaging (XPCI) diffraction gratings. Previously, a lack of control over precise alignment of the etch pattern to crystal planes and the constant evolution of hydrogen bubbles inhibited uniformity and limited the potential for higher aspect ratios. Sandia National Laboratories has made significant advancements in anisotropic silicon etching, including improvements to accurate crystal alignment using new equipment capabilities and methods of liberating hydrogen bubbles trapped in deep trenches. The possibility of reaching aspect ratios of 600:1 using anisotropic wet etching in Si have been cited in literature, but we have found no evidence of such aspect ratios being achieved. Our process is focused on improvements to yield, better anisotropy and uniformity, enabling gratings with aspect ratios as high as 170:1. The well-defined sharp edges and deep trenches that can be achieved using this technique make it a suitable method for optical grating fabrication. Deeper trenches support pushing XPCI to higher x-ray energies, which will allow access to imaging thicker or denser samples, or improved image contrast at lower energies. Higher aspect ratios in the gratings will better improve sensitivity and enable higher energy systems.
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.
Many optical systems are used for specific tasks such as classification. Of these systems, the majority are designed to maximize image quality for human observers. However, machine learning classification algorithms do not require the same data representation used by humans. We investigate the compressive optical systems optimized for a specific machine sensing task. Two compressive optical architectures are examined: an array of prisms and neutral density filters where each prism and neutral density filter pair realizes one datum from an optimized compressive sensing matrix, and another architecture using conventional optics to image the aperture onto the detector, a prism array to divide the aperture, and a pixelated attenuation mask in the intermediate image plane. We discuss the design, simulation, and trade-offs of these systems built for compressed classification of the Modified National Institute of Standards and Technology dataset. Both architectures achieve classification accuracies within 3% of the optimized sensing matrix for compression ranging from 98.85% to 99.87%. The performance of the systems with 98.85% compression were between an F / 2 and F / 4 imaging system in the presence of noise.
We investigate the feasibility of additively manufacturing optical components to accomplish task-specific classification in a computational imaging device. We report on the design, fabrication, and characterization of a non-traditional optical element that physically realizes an extremely compressed, optimized sensing matrix. The compression is achieved by designing an optical element that only samples the regions of object space most relevant to the classification algorithms, as determined by machine learning algorithms. The design process for the proposed optical element converts the optimal sensing matrix to a refractive surface composed of a minimized set of non-repeating, unique prisms. The optical elements are 3D printed using a Nanoscribe, which uses two-photon polymerization for high-precision printing. We describe the design of several computational imaging prototype elements. We characterize these components, including surface topography, surface roughness, and angle of prism facets of the as-fabricated elements.
High-quality image products in an X-Ray Phase Contrast Imaging (XPCI) system can be produced with proper system hardware and data acquisition. However, it may be possible to further increase the quality of the image products by addressing subtleties and imperfections in both hardware and the data acquisition process. Noting that addressing these issues entirely in hardware and data acquisition may not be practical, a more prudent approach is to determine the balance of how the apparatus may reasonably be improved and what can be accomplished with image post-processing techniques. Given a proper signal model for XPCI data, image processing techniques can be developed to compensate for many of the image quality degradations associated with higher-order hardware and data acquisition imperfections. However, processing techniques also have limitations and cannot entirely compensate for sub-par hardware or inaccurate data acquisition practices. Understanding system and image processing technique limitations enables balancing between hardware, data acquisition, and image post-processing. In this paper, we present some of the higher-order image degradation effects we have found associated with subtle imperfections in both hardware and data acquisition. We also discuss and demonstrate how a combination of hardware, data acquisition processes, and image processing techniques can increase the quality of XPCI image products. Finally, we assess the requirements for high-quality XPCI images and propose reasonable system hardware modifications and the limits of certain image processing techniques.
Many optical systems are used for specific tasks such as classification. Of these systems, the majority are designed to maximize image quality for human observers; however, machine learning classification algorithms do not require the same data representation used by humans. In this work we investigate compressive optical systems optimized for a specific machine sensing task. Two compressive optical architectures are examined: an array of prisms and neutral density filters where each prism and neutral density filter pair realizes one datum from an optimized compressive sensing matrix, and another architecture using conventional optics to image the aperture onto the detector, a prism array to divide the aperture, and a pixelated attenuation mask in the intermediate image plane. We discuss the design, simulation, and tradeoffs of these compressive imaging systems built for compressed classification of the MNSIT data set. To evaluate the tradeoffs of the two architectures, we present radiometric and raytrace models for each system. Additionally, we investigate the impact of system aberrations on classification accuracy of the system. We compare the performance of these systems over a range of compression. Classification performance, radiometric throughput, and optical design manufacturability are discussed.
Advancements in machine learning (ML) and deep learning (DL) have enabled imaging systems to perform complex classification tasks, opening numerous problem domains to solutions driven by high quality imagers coupled with algorithmic elements. However, current ML and DL methods for target classification typically rely upon algorithms applied to data measured by traditional imagers. This design paradigm fails to enable the ML and DL algorithms to influence the sensing device itself, and treats the optimization of the sensor and algorithm as separate sequential elements. Additionally, this current paradigm narrowly investigates traditional images, and therefore traditional imaging hardware, as the primary means of data collection. We investigate alternative architectures for computational imaging systems optimized for specific classification tasks, such as digit classification. This involves a holistic approach to the design of the system from the imaging hardware to algorithms. Techniques to find optimal compressive representations of training data are discussed, and most-useful object-space information is evaluated. Methods to translate task-specific compressed data representations into non-traditional computational imaging hardware are described, followed by simulations of such imaging devices coupled with algorithmic classification using ML and DL techniques. Our approach allows for inexpensive, efficient sensing systems. Reduced storage and bandwidth are achievable as well since data representations are compressed measurements which is especially important for high data volume systems.
X-ray phase contrast imaging (XPCI) reveals structure and detail of low density materials with a sensitivity not accessible to conventional absorption based x-ray imaging or other non-destructive inspection techniques. The wide use of low density materials in defense and security applications has driven development outside the medical domain. In the laboratory environment (instantiations that do not employ a synchrotron), XPCI has moved beyond nascent demonstrations. Advances have been made in grating fabrication, source development, and specialized detectors. As the application space grows, new algorithms for acquisition, reconstruction, and corrections are being developed. I will review the state of the art in laboratory grating-based XPCI with emphasis on the growing interest in materials science applications. Hurdles remain for XPCI to move beyond laboratory demonstrations and become a widely used non-destructive inspection technique. The most common three-grating system has limitations defined by grating fabrication limits, which determine attainable energy levels, and relevant samples. The system geometry, signal levels, and speed of acquisition must be realistic for real world applications. This talk will provide a perspective on the global state of XPCI and development trends that seek to expand the operational space.
Foams and encapsulants serve important roles in the protection of the components they surround. These low density materials may be used to provide shock protection, to protect against high voltage breakdown, or to minimize thermal fluctuations. Voids and gaps in the material, delaminations from a mating material, or non-uniformities in the encapsulating materials can lead to critical failures in the encapsulated component. Despite the important role these low density materials serve, traditional non-destructive inspection tools are limited in their ability to study this material set, especially in the presence of high density materials such as wires. The default approach has been destructive post-mortums where components are deconstructed after a failure and cause and effect are difficult to distinguish. X-ray phase contrast imaging has a longer history at synchrotrons, but this is not a realistic solution for non-destructive inspection. We have demonstrated grating-based x-ray phase contrast 3-D tomography in a laboratory environment with a conventional x-ray tube. Our large format grating fabrication capability enables imaging with large fields of view (10 cm2) at 28 keV for the successful non-destructive inspection of these low-density materials. We demonstrate that the complementary image modalities available with XPCI provide unique information and higher contrast for the inspection of defects in low density materials than conventional x-ray alone.
The modeling and simulation of non-traditional imaging systems require holistic consideration of the end-to-end system. We demonstrate this approach through a tolerance analysis of a random scattering lensless imaging system.
Computational imagers fundamentally enable new optical hardware through the use of both physical and algorithmic elements. We report on the creation of a static lensless computational imaging system enabled by this paradigm.
We report on the design of a refracting prism array for use in a computational lensless imaging system. The technique discussed enables creation of a refracting element that maximizes signal on a detector region. Examples of pseudo-random prism arrays for the generation of images are provided. The pseudo-random prism array is compared to a randomly oriented prism array and the advantages of the optimal scattering element are highlighted.
Lensless imaging systems have the potential to provide new capabilities for lower size and weight configuration than traditional imaging systems. Lensless imagers frequently utilize computational imaging techniques, which moves the complexity of the system away from optical subcomponents and into a calibration process whereby the measurement matrix is estimated.
We report on the design, simulation, and prototyping of a lensless imaging system that utilizes a 3D printed optically transparent random scattering element. Development of end-to-end system simulations, which includes simulations of the calibration process, as well as the data processing algorithm used to generate an image from the raw data are presented. These simulations utilize GPU-based raytracing software, and parallelized minimization algorithms to bring complete system simulation times down to the order of seconds.
Hardware prototype results are presented, and practical lessons such as the effect of sensor noise on reconstructed image quality are discussed. System performance metrics are proposed and evaluated to discuss image quality in a manner that is relatable to traditional image quality metrics. Various hardware instantiations are discussed.
This position paper describes a potential implementation of a large-scale grating-based X-ray Phase Contrast Imaging System (XPCI) simulation tool along with the associated challenges in its implementation. This work proposes an implementation based off of an implementation by Peterzol et. al. where each grating is treated as an object imaged in the field-of-view. Two main challenges exist; the first, is the required sampling and information management in object space due to the micron-scale periods of each grating propagating over significant distances. The second is maintaining algorithmic numerical stability for imaging systems relevant to industrial applications. We present preliminary results for a numerical stability study using a simplified algorithm that performs Talbot imaging in a big-data context
The integration of optics for efficient light delivery and the collection of fluorescence from trapped ions in surface
electrode ion traps is a key component to achieving scalability for quantum information processing. Diffractive optical
elements (DOEs) present a promising approach as compared to bulk optics because of their small physical profile and
their flexibility in tailoring the optical wavefront. The precise alignment of the optics for coupling fluorescence to and
from the ions, however, poses a particular challenge. Excitation and manipulation of the ions requires a high degree of
optical access, significantly restricting the area available for mounting components. The ion traps, DOEs, and other
components are compact, constraining the manipulation of various elements. For efficient fluorescence collection from
the ions the DOE must be have a large numerical aperture (NA), which results in greater sensitivity to misalignment.
The ion traps are sensitive devices, a mechanical approach to alignment such as contacting the trap and using precision
motors to back-off a set distance not only cannot achieve the desired alignment precision, but risks damage to the ion
trap.
We have developed a non-contact precision optical alignment technique. We use line foci produced by off-axis linear
Fresnel zone plates (FZPs) projected on alignment targets etched in the top metal layer of the ion trap and demonstrate
micron-level alignment accuracy.
The viability of neutral atom based quantum computers is dependent upon scalability to large numbers of qubits.
Diffractive optical elements (DOEs) offer the possibility to scale up to many qubit systems by enabling the manipulation
of light to collect signal or deliver a tailored spatial trapping pattern. DOEs have an advantage over refractive microoptics since they do not have measurable surface sag, making significantly larger numerical apertures (NA) accessible with a smaller optical component. The smaller physical size of a DOE allows the micro-lenses to be placed in vacuum with the atoms, reducing aberration effects that would otherwise be introduced by the cell walls of the vacuum chamber. The larger collection angle accessible with DOEs enable faster quantum computation speeds. We have designed a set of DOEs for collecting the 852 nm fluorescence from the D2 transition in trapped cesium atoms,
and compare these DOEs to several commercially available refractive micro-lenses. The largest DOE is able to collect
over 20% of the atom’s radiating sphere whereas the refractive micro-optic is able to collect just 8% of the atom’s
radiating sphere.
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