X‐ray microscopy and automatic detection of defects in through silicon vias in three‐dimensional integrated circuits

Through silicon vias (TSVs) are a key enabling technology for interconnection and realization of complex three-dimensional integrated circuit (3D-IC) components. In order to perform failure analysis without the need of destructive sample preparation, x-ray microscopy (XRM) is a rising method of analyzing the internal structure of samples. However, there is still a lack of evaluated scan recipes or best practices regarding XRM parameter settings for the study of TSVs in the current state of literature. There is also an increased interest in automated machine learning and deep learning approaches for qualitative and quantitative inspection processes in recent years. Especially deep learning based object detection is a well-known methodology for fast detection and classification capable of working with large volumetric XRM datasets. Therefore, a combined XRM and deep learning object detection workflow for automatic micrometer accurate defect location on liner-TSVs was developed throughout this work. Two measurement setups including detailed information about the used parameters for either full IC device scan or detailed TSV scan were introduced. Both are able to depict delamination defects and finer structures in TSVs with either a low or high resolution. The combination of a 0.4 objective with a beam voltage of 40 kV proved to be a good combination for achieving optimal imaging contrast for the full-device scan. However, detailed TSV scans have demonstrated that the use of a 20 objective along with a beam voltage of 140 kV significantly improves image quality. A database with 30,000 objects was created for automated data analysis, so that a well-established object recognition method for automated defect analysis could be integrated into the process analysis. This RetinaNet-based object detection method achieves a very strong average precision of 0.94. It supports the detection of erroneous TSVs in both top view and side view, so that defects can be detected at different depths. Consequently, the proposed workflow can be used for failure analysis, quality control or process optimization in R&D environments

Calibration by differentiation – Self‐supervised calibration for X‐ray microscopy using a differentiable cone‐beam reconstruction operator

High‐resolution X‐ray microscopy (XRM) is gaining interest for biological investigations of extremely small‐scale structures. XRM imaging of bones in living mice could provide new insights into the emergence and treatment of osteoporosis by observing osteocyte lacunae, which are holes in the bone of few micrometres in size. Imaging living animals at that resolution, however, is extremely challenging and requires very sophisticated data processing converting the raw XRM detector output into reconstructed images. This paper presents an open‐source, differentiable reconstruction pipeline for XRM data which analytically computes the final image from the raw measurements. In contrast to most proprietary reconstruction software, it offers the user full control over each processing step and, additionally, makes the entire pipeline deep learning compatible by ensuring differentiability. This allows fitting trainable modules both before and after the actual reconstruction step in a purely data‐driven way using the gradient‐based optimizers of common deep learning frameworks. The value of such differentiability is demonstrated by calibrating the parameters of a simple cupping correction module operating on the raw projection images using only a self‐supervisory quality metric based on the reconstructed volume and no further calibration measurements. The retrospective calibration directly improves image quality as it avoids cupping artefacts and decreases the difference in grey values between outer and inner bone by 68–94%. Furthermore, it makes the reconstruction process entirely independent of the XRM manufacturer and paves the way to explore modern deep learning reconstruction methods for arbitrary XRM and, potentially, other flat‐panel computed tomography systems. This exemplifies how differentiable reconstruction can be leveraged in the context of XRM and, hence, is an important step towards the goal of reducing the resolution limit of in vivo bone imaging to the single micrometre domain

Arbetstidens anv\ue4ndning vid VVS-montage - en fr\ue5ga om struktur och ledarskap

Most of the narratives packaged for New Orleans\u27s many tourists cultivate a desire for black culture—jazz, cuisine, dance—while simultaneously targeting black people and their communities as sources and sites of political, social, and natural disaster. In this timely book, the Americanist and New Orleans native Lynnell L. Thomas delves into the relationship between tourism, cultural production, and racial politics. She carefully interprets the racial narratives embedded in tourist websites, travel guides, business periodicals, and newspapers; the thoughts of tour guides and owners; and the stories told on bus and walking tours as they were conducted both before and after Katrina. She describes how, with varying degrees of success, African American tour guides, tour owners, and tourism industry officials have used their own black heritage tours and tourism-focused businesses to challenge exclusionary tourist representations. Taking readers from the Lower Ninth Ward to the White House, Thomas highlights the ways that popular culture and public policy converge to create a mythology of racial harmony that masks a long history of racial inequality and structural inequity

Characterizing Intraindividual Podocyte Morphology In Vitro with Different Innovative Microscopic and Spectroscopic Techniques

Podocytes are critical components of the glomerular filtration barrier, sitting on the outside of the glomerular basement membrane. Primary and secondary foot processes are characteristic for podocytes, but cell processes that develop in culture were not studied much in the past. Moreover, protocols for diverse visualization methods mostly can only be used for one technique, due to differences in fixation, drying and handling. However, we detected by single-cell RNA sequencing (scRNAseq) analysis that cells reveal high variability in genes involved in cell type-specific morphology, even within one cell culture dish, highlighting the need for a compatible protocol that allows measuring the same cell with different methods. Here, we developed a new serial and correlative approach by using a combination of a wide variety of microscopic and spectroscopic techniques in the same cell for a better understanding of podocyte morphology. In detail, the protocol allowed for the sequential analysis of identical cells with light microscopy (LM), Raman spectroscopy, scanning electron microscopy (SEM) and atomic force microscopy (AFM). Skipping the fixation and drying process, the protocol was also compatible with scanning ion-conductance microscopy (SICM), allowing the determination of podocyte surface topography of nanometer-range in living cells. With the help of nanoGPS Oxyo®, tracking concordant regions of interest of untreated podocytes and podocytes stressed with TGF-β were analyzed with LM, SEM, Raman spectroscopy, AFM and SICM, and revealed significant morphological alterations, including retraction of podocyte process, changes in cell surface morphology and loss of cell-cell contacts, as well as variations in lipid and protein content in TGF-β treated cells. The combination of these consecutive techniques on the same cells provides a comprehensive understanding of podocyte morphology. Additionally, the results can also be used to train automated intelligence networks to predict various outcomes related to podocyte injury in the future

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as size, shape, and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, an elegant, flexible, and versatile method to bypass this costly and tedious data acquisition process is presented. It shows that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, a segmentation accuracy can be derived that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which were chosen as examples. The presented study paves the way toward the use of deep learning for automated, high-throughput particle detection in a variety of imaging techniques such as in microscopies and spectroscopies, for a wide range of applications, including the detection of micro- and nanoplastic particles in water and tissue samples

Hollow silica capsules for amphiphilic transport and sustained delivery of antibiotic and anticancer drugs

Hollow mesoporous silica capsules (HMSC) are potential drug transport vehicles due to their biocompatibility, high loading capacity and sufficient stability in biological milieu. Herein, we report the synthesis of ellipsoid-shaped HMSC (aspect ratio ∼2) performed using hematite particles as solid templates that were coated with a conformal silica shell through cross-condensation reactions. For obtaining hollow silica capsules, the iron oxide core was removed by acidic leaching. Gas sorption studies on HMSC revealed mesoscopic pores (main pore width ∼38 Å) and a high surface area of 308.8 m2 g−1. Cell uptake of dye-labeled HMSC was confirmed by incubating them with human cervical cancer (HeLa) cells and analyzing the internalization through confocal microscopy. The amphiphilic nature of HMSC for drug delivery applications was tested by loading antibiotic (ciprofloxacin) and anticancer (curcumin) compounds as model drugs for hydrophilic and hydrophobic therapeutics, respectively. The versatility of HMSC in transporting hydrophilic as well as hydrophobic drugs and a pH dependent drug release over several days under physiological conditions was demonstrated in both cases by UV-vis spectroscopy. Ciprofloxacin-loaded HMSC were additionally evaluated towards Gram negative (E. coli) bacteria and demonstrated their efficacy even at low concentrations (10 μg ml−1) in inhibiting complete bacterial growth over 18 hours