Automatic Error Detection in Integrated Circuits Image Segmentation: A Data-driven Approach

Due to the complicated nanoscale structures of current integrated circuits(IC) builds and low error tolerance of IC image segmentation tasks, most existing automated IC image segmentation approaches require human experts for visual inspection to ensure correctness, which is one of the major bottlenecks in large-scale industrial applications. In this paper, we present the first data-driven automatic error detection approach targeting two types of IC segmentation errors: wire errors and via errors. On an IC image dataset collected from real industry, we demonstrate that, by adapting existing CNN-based approaches of image classification and image translation with additional pre-processing and post-processing techniques, we are able to achieve recall/precision of 0.92/0.93 in wire error detection and 0.96/0.90 in via error detection, respectively

A sparsity-based framework for resolution enhancement in optical fault analysis of integrated circuits

The increasing density and smaller length scales in integrated circuits (ICs) create resolution challenges for optical failure analysis techniques. Due to flip-chip bonding and dense metal layers on the front side, optical analysis of ICs is restricted to backside imaging through the silicon substrate, which limits the spatial resolution due to the minimum wavelength of transmission and refraction at the planar interface. The state-of-the-art backside analysis approach is to use aplanatic solid immersion lenses in order to achieve the highest possible numerical aperture of the imaging system. Signal processing algorithms are essential to complement the optical microscopy efforts to increase resolution through hardware modifications in order to meet the resolution requirements of new IC technologies. The focus of this thesis is the development of sparsity-based image reconstruction techniques to improve resolution of static IC images and dynamic optical measurements of device activity. A physics-based observation model is exploited in order to take advantage of polarization diversity in high numerical aperture systems. Multiple-polarization observation data are combined to produce a single enhanced image with higher resolution. In the static IC image case, two sparsity paradigms are considered. The first approach, referred to as analysis-based sparsity, creates enhanced resolution imagery by solving a linear inverse problem while enforcing sparsity through non-quadratic regularization functionals appropriate to IC features. The second approach, termed synthesis-based sparsity, is based on sparse representations with respect to overcomplete dictionaries. The domain of IC imaging is particularly suitable for the application of overcomplete dictionaries because the images are highly structured; they contain predictable building blocks derivable from the corresponding computer-aided design layouts. This structure provides a strong and natural a-priori dictionary for image reconstruction. In the dynamic case, an extension of the synthesis-based sparsity paradigm is formulated. Spatial regions of active areas with the same behavior over time or over frequency are coupled by an overcomplete dictionary consisting of space-time or space-frequency blocks. This extended dictionary enables resolution improvement through sparse representation of dynamic measurements. Additionally, extensions to darkfield subsurface microscopy of ICs and focus determination based on image stacks are provided. The resolution improvement ability of the proposed methods has been validated on both simulated and experimental data

Enabling Scalable Neurocartography: Images to Graphs for Discovery

In recent years, advances in technology have enabled researchers to ask new questions predicated on the collection and analysis of big datasets that were previously too large to study. More specifically, many fundamental questions in neuroscience require studying brain tissue at a large scale to discover emergent properties of neural computation, consciousness, and etiologies of brain disorders. A major challenge is to construct larger, more detailed maps (e.g., structural wiring diagrams) of the brain, known as connectomes. Although raw data exist, obstacles remain in both algorithm development and scalable image analysis to enable access to the knowledge within these data volumes. This dissertation develops, combines and tests state-of-the-art algorithms to estimate graphs and glean other knowledge across six orders of magnitude, from millimeter-scale magnetic resonance imaging to nanometer-scale electron microscopy. This work enables scientific discovery across the community and contributes to the tools and services offered by NeuroData and the Open Connectome Project. Contributions include creating, optimizing and evaluating the first known fully-automated brain graphs in electron microscopy data and magnetic resonance imaging data; pioneering approaches to generate knowledge from X-Ray tomography imaging; and identifying and solving a variety of image analysis challenges associated with building graphs suitable for discovery. These methods were applied across diverse datasets to answer questions at scales not previously explored

Laser Powder Bed Fusion of AlSi10Mg for Fabrication of Fluid Power Components

Laser powder bed fusion (LPBF) is an additive manufacturing (AM) process that produces 3D objects in a layer-by-layer fashion by using a laser to selectively melt powdered material. Some of the advantages of LPBF include the potential for more design freedom, reduced waste, and flexible production strategies. It is possible to produce parts with complex geometries that cannot be created through traditional methods without great expense in terms of time, money, and resources. There is no need for expensive tooling, thus enabling fast production of unique designs. To implement LPBF in industry, adopters must develop personalized plans for product qualification, quality assurance, manufacturing, and post-processing requirements. The manufacturing strategy is particularly important, as the machine, material, and process parameters that are used have a large impact on the resulting microstructure and mechanical properties, ultimately controlling the quality of the end-product. The generation, control, and transmission of fluid power is critical for many engineering applications, such as the automotive transmission pump. Energy losses within the pump reduce the transmission’s ability to convert torque from the engine, so a more efficient, lightweight pump would reduce the fuel consumption of the vehicle. If LPBF is found suitable for the manufacture of aluminum fluid power components, design improvements could be implemented, and custom solutions could be offered to individual customers. This thesis aims to add to the body of knowledge for process development in LPBF of AlSi10Mg in order to improve the resulting part density, surface roughness, and material performance for fluid power applications. The material performance is evaluated through a comparison with an existing product: the cast aluminum pump housing for use in automotive transmissions. Process parameters were selected for the LPBF of AlSi10Mg on a modulated laser system to minimize porosity and surface roughness, and maximize production efficiency. This was accomplished through the use of initial process mapping, prediction of melt pool dimensions using a thermophysical model, and fine-tune adjustment of parameters. AlSi10Mg powder from two suppliers was characterized for morphology and particle size distribution. The density or solid fraction of manufactured artifacts was evaluated by optical microscopy and x-ray computed tomography (CT), and the surface roughness by laser confocal microscopy. Two process parameter sets were identified for manufacturing fluid power components. A relative density of 99.95% and surface roughness (Sa) of 11.39 μm were achieved. These process parameters were used to manufacture LPBF artifacts of various geometries for characterization of the relative density, surface roughness, and durability in terms of hardness, wear resistance, and corrosion resistance. The results were compared with benchmark values for a cast aluminum pump housing, which was also characterized for chemical composition and microstructure. The AM artifacts had a lower hardness (54.3 to 69.3 HRB) than the cast pump housing (72.8 to 81.5 HRB). The specific wear rate was determined through the dry sliding wear test, and the AM artifacts (3.92 x10-13 to 6.04 x10-13 m2N-1) had a lower wear resistance than the cast pump housing (2.50 x10-13 to 2.55 x10-13 m2N-1). Cyclic polarization testing revealed that the corrosion resistance and pitting potential were better for the AM artifacts (-0.57 to 0.48 V vs SCE in 0.001M Cl-) than the cast pump housing, which exhibited general corrosion. Linear polarization resistance tests also suggested a better corrosion resistance for the AM artifacts, as the corrosion current density was lower. The surface roughness and durability of three different surface types for the AM artifacts (upskin, sideskin, and polished) and cast pump housing (as-cast, horizontal; as-cast, vertical; and machined) were also compared. The manufacturability of design features was investigated. Thin walls were printed with thicknesses of 1.0 to 3.0 mm. The wall thickness did not have a significant effect on the part density. Slot artifacts were printed with varying gap widths of 0.5 to 4.5 mm. The interior vertical walls were characterized for surface roughness, which was marginally lower for the smallest and largest gap widths. Straight circular channels were printed with hole diameters of 0.2 to 1.2 mm, and with a height of 10 or 20 mm. Powder was successfully removed from channels with a minimum diameter of 0.5 mm. Recommendations for future work include performing further in-depth study on the relationship between process parameters and the microstructure of LPBF-processed material in order to better understand and control the resulting mechanical properties. Further development of design constraints, especially those related to non-uniform channels, would also be of use to designers of fluid power components as it would provide more design freedom and ultimately enable innovation

Optical Methods in Sensing and Imaging for Medical and Biological Applications

The recent advances in optical sources and detectors have opened up new opportunities for sensing and imaging techniques which can be successfully used in biomedical and healthcare applications. This book, entitled ‘Optical Methods in Sensing and Imaging for Medical and Biological Applications’, focuses on various aspects of the research and development related to these areas. The book will be a valuable source of information presenting the recent advances in optical methods and novel techniques, as well as their applications in the fields of biomedicine and healthcare, to anyone interested in this subject

Micro/Nano Manufacturing

Micro manufacturing involves dealing with the fabrication of structures in the size range of 0.1 to 1000 µm. The scope of nano manufacturing extends the size range of manufactured features to even smaller length scales—below 100 nm. A strict borderline between micro and nano manufacturing can hardly be drawn, such that both domains are treated as complementary and mutually beneficial within a closely interconnected scientific community. Both micro and nano manufacturing can be considered as important enablers for high-end products. This Special Issue of Applied Sciences is dedicated to recent advances in research and development within the field of micro and nano manufacturing. The included papers report recent findings and advances in manufacturing technologies for producing products with micro and nano scale features and structures as well as applications underpinned by the advances in these technologies