Safe storage of spent nuclear fuel (SNF) is critical to the nuclear fuel cycle and the future of nuclear energy. In the United States, SNF is primarily stored via two methods regulated by the U.S. Nuclear Regulatory Commission (U.S. NRC): wet storage in SNF pools, and dry storage in dry cask storage systems (DCSSs). After about five years of cooling in spent fuel pools, the fuel assemblies are transferred into DCSSs, and the systems are filled with helium and sealed by welding. Deterioration of conditions inside of a DCSS will be reflected by changes in the internal gas properties which motivates the development of acoustic techniques to monitor internal gas properties, over extended storage periods, using sensors mounted on the exterior of the storage packages. However, a major challenge in collecting acoustic signals is the impedance mismatch between the steel canister shell and the gas. Only a small fraction of the ultrasonic signal can be transmitted through the gas medium. In this paper, experimental studies on a full-scale canister mock-up were conducted to capture the gas-borne signals. Damping materials were pasted on the outside and blocking and unblocking tests were conducted to identify the gas-borne signal. The results showed that the excitation frequency plays an important role in maximizing the gas-borne signal. The gas-borne signal was successfully detected at around the theoretical time-of-flight (TOF). A high signal-to-noise ratio (SNR) was achieved in the measurements. Next, the acoustic impedance matching (AIM) layers were introduced, and the gas signal was drastically improved compared with no AIM layers.
Defects in semiconductor processes can limit yield, increase overall production cost, and also lead to time-dependent critical component failures. Current state-of-the-art optical and electron beam (EB) inspection systems rely on rule-based techniques for defect detection and classification, which are usually rigid in their comparative processes. This rigidity limits overall capability and increases relative engineering time to classify nuisance defects. This is further challenged due to shrinkage of pattern dimensions for advanced nodes. We propose a deep learning-based workflow that circumvents these challenges and enables accurate defect detection, classification, and localization in a unified framework. In particular, we train convolutional neural network-based models using high-resolution EB images of wafers patterned with various types of intentional defects and achieve robust defect detection and classification performance. Furthermore, we generate class activation maps to demonstrate defect localization capability of the model “without” explicitly training it with defect location information. To understand the underlying decision-making process of these deep models, we analyze the learned filters in pixel space and Fourier space and interpret the various operations at different layers. We achieve high sensitivity (97%) and specificity (100%) along with rapid and accurate defect localization. We also test performance of the proposed workflow on images from two distinct patterns and find that in order to retain high accuracy a modest level of retraining is necessary.
Defects are ubiquitous in the semiconductor industry and detection, classification at various levels is a challenge and requires rigorous sampling. Every technology offers a new challenge in this area and requires numerous hours of setup, debug time for semiconductor integrated chip manufacturers. Intentional defects on patterns at various sizes in comparison to the critical dimension of a particular technology are used to preview their modulation and assess inspection tool performances. Figure 1 shows a few types of intentional defects as designed and their respective manifestation on a photomask. The chart also shows detection and printability limit and it varies by each defect type.1, 2
In recent years deep learning has made tremendous progress and has substantially improved the state-of-art in many applications that include object detection, speech recognition, and image classification3 The application and design of these deep learning tools in semiconductor defect recognition can be very useful, as it has the potential to reduce the setup time, cost and improving the overall (defect) detection and classification accuracy.
In this work we demonstrate the application of convolution neural networks on e-beam images of intentional defects on various types of patterns. We assess various filters and their outputs at each layer of a custom convolutional neural network (Figure 2) to ultimately improve the architecture and its accuracy. We also demonstrate the relative effectiveness of transfer learning in this application with limited availability of labelled data. Using the architecture illustrated in Figure 2 we are able to achieve a classification accuracy of 95%.
Variations in the mechanical properties of the extracellular environment can alter important aspects of cell function such as proliferation, migration, differentiation and survival. However, many of the techniques available to study these effects lack the ability to characterise cell-to-cell and cell-to-environment interactions on the microscopic scale in three dimensions (3D). Quantitative micro-elastography (QME) is an extension of compression optical coherence elastography that utilizes a compliant layer with known mechanical properties to estimate the axial stress at the tissue surface, which combined with axial strain, is used to map the 3D microscale elasticity of tissue into an image. Despite being based on OCT, limitations in post-processing techniques used to determine axial strain prevented QME to quantify the elasticity of individual cells. In this study we extend the capability of QME to present, to the best of our knowledge, the first images of the elasticity of cells and their environment in 3D over millimeter field-of-views. We improve the accuracy and resolution of QME by incorporating an efficient, iterative solution to the inverse elasticity problem using adjoint elasticity equations to enable QME to visualize individual cells for the first time. We present images of human stem cells embedded in soft gelatin methacryloyl (GelMa) hydrogels and demonstrate these cells elevate the stiffness of the GelMa from 3-kPa to approximately 25-kPa. Our QME system is developed using commercially available components that can be readily made available to biologists, highlighting the potential for QME to emerge as an important tool in the field of mechanobiology.
In a typical experiment in compression elastography a sample is compressed to an overall strain of about 1-5%, and then perturbed with a much smaller strain in the range of 0.05%-0.1%. The displacement field corresponding to this perturbative excitation is measured using phase-sensitive OCT. This three-dimensional perturbative displacement data carries within it a wealth of information regarding the volumetric distribution of linear elastic properties of tissue. In this talk we will describe a class of iterative algorithms that use this data input and generate volumetric maps of linear elastic properties of biological specimens. The main idea behind these algorithms is to pose this inverse problem as a constrained minimization problem and use adjoint equations, spatially adaptive resolution and domain decomposition techniques to solve this problem.
We will also consider the case when the overall compression and the perturbative excitation steps are repeated several times while increasing the overall strain. For example, a sequence wherein the overall strain varies as 2, 4, 6, 8, and 10%, and each increment is followed by a small perturbative excitation. The measured displacement field corresponding to this small excitation is sensitive to the nonlinear elastic properties of the specimen, which determine how its elastic modulus varies with increasing strain. We will extend the algorithms designed to infer the linear elastic properties of biological specimens to infer these non-linear elastic properties. We will demonstrate our ability to infer linear and nonlinear elastic properties on tissue-phantom, and ex-vivo and in-vivo tissue samples.
In elastography, quantitative elastograms are desirable as they are system and operator independent. Such quantification also facilitates more accurate diagnosis, longitudinal studies and studies performed across multiple sites. In optical elastography (compression, surface-wave or shear-wave), quantitative elastograms are typically obtained by assuming some form of homogeneity. This simplifies data processing at the expense of smearing sharp transitions in elastic properties, and/or introducing artifacts in these regions.
Recently, we proposed an inverse problem-based approach to compression OCE that does not assume homogeneity, and overcomes the drawbacks described above. In this approach, the difference between the measured and predicted displacement field is minimized by seeking the optimal distribution of elastic parameters. The predicted displacements and recovered elastic parameters together satisfy the constraint of the equations of equilibrium. This approach, which has been applied in two spatial dimensions assuming plane strain, has yielded accurate material property distributions.
Here, we describe the extension of the inverse problem approach to three dimensions. In addition to the advantage of visualizing elastic properties in three dimensions, this extension eliminates the plane strain assumption and is therefore closer to the true physical state. It does, however, incur greater computational costs. We address this challenge through a modified adjoint problem, spatially adaptive grid resolution, and three-dimensional decomposition techniques. Through these techniques the inverse problem is solved on a typical desktop machine within a wall clock time of ~ 20 hours. We present the details of the method and quantitative elasticity images of phantoms and tissue samples.
Quantitative elasticity imaging, which retrieves elastic modulus maps from tissue, is preferred to qualitative strain imaging for acquiring system- and operator-independent images and longitudinal and multi-site diagnoses.
Quantitative elasticity imaging has already been demonstrated in optical elastography by relating surface-acoustic and shear wave speed to Young’s modulus via a simple algebraic relationship. Such approaches assume largely homogeneous samples and neglect the effect of boundary conditions.
We present a general approach to quantitative elasticity imaging based upon the solution of the inverse elasticity problem using an iterative technique and apply it to compression optical coherence elastography. The inverse problem is one of finding the distribution of Young’s modulus within a sample, that in response to an applied load, and a given displacement and traction boundary conditions, can produce a displacement field matching one measured in experiment. Key to our solution of the inverse elasticity problem is the use of the adjoint equations that allow the very efficient evaluation of the gradient of the objective function to be minimized with respect to the unknown values of Young’s modulus within the sample. Although we present the approach for the case of linear elastic, isotropic, incompressible solids, this method can be employed for arbitrarily complex mechanical models.
We present the details of the method and quantitative elastograms of phantoms and tissues. We demonstrate that by using the inverse approach, we can decouple the artefacts produced by mechanical tissue heterogeneity from the true distribution of Young’s modulus, which are often evident in techniques that employ first-order algebraic relationships.
It is now well recognized that a host of imaging modalities (a list that includes Ultrasound, MRI, Optical Coherence Tomography, and optical microscopy) can be used to “watch” tissue as it deforms in response to an internal or external excitation. The result is a detailed map of the deformation field in the interior of the tissue. This deformation field can be used in conjunction with a material mechanical response to determine the spatial distribution of material properties of the tissue by solving an inverse problem. Images of material properties thus obtained can be used to quantify the health of the tissue. Recently, they have been used to detect, diagnose and monitor cancerous lesions, detect vulnerable
plaque in arteries, diagnose liver cirrhosis, and possibly detect the onset of Alzheimer’s disease. In this talk I will describe
the mathematical and computational aspects of solving this class of inverse problems, and their applications in biology
and medicine.
In particular, I will discuss the well-posedness of these problems and quantify the amount of displacement data necessary to obtain a unique property distribution. I will describe an efficient algorithm for solving the resulting inverse problem. I will also describe some recent developments based on Bayesian inference in estimating the variance in the estimates of material properties. I will conclude with the applications of these techniques in diagnosing breast cancer and in characterizing the mechanical properties of cells with sub-cellular resolution.
Near-field interference lithography is a promising variant of multiple patterning in semiconductor device fabrication
that can potentially extend lithographic resolution beyond the current materials-based restrictions on the
Rayleigh resolution of projection systems. With H2O as the immersion medium, non-evanescent propagation
and optical design margins limit achievable pitch to approximately 0.53λ/nH2O = 0.37λ. Non-evanescent images
are constrained only by the comparatively large resist indices (typically1.7) to a pitch resolution of 0.5/nresist
(typically 0.29). Near-field patterning can potentially exploit evanescent waves and thus achieve higher spatial
resolutions. Customized near-field images can be achieved through the modulation of an incoming wavefront
by what is essentially an in-situ hologram that has been formed in an upper layer during an initial patterned
exposure. Contrast Enhancement Layer (CEL) techniques and Talbot near-field interferometry can be considered
special cases of this approach.
Since the technique relies on near-field interference effects to produce the required pattern on the resist, the
shape of the grating and the design of the film stack play a significant role on the outcome. As a result, it is
necessary to resort to full diffraction computations to properly simulate and optimize this process.
The next logical advance for this technology is to systematically design the hologram and the incident wavefront
which is generated from a reduction mask. This task is naturally posed as an optimization problem, where
the goal is to find the set of geometric and incident wavefront parameters that yields the closest fit to a desired
pattern in the resist. As the pattern becomes more complex, the number of design parameters grows, and the
computational problem becomes intractable (particularly in three-dimensions) without the use of advanced numerical
techniques. To treat this problem effectively, specialized numerical methods have been developed. First,
gradient-based optimization techniques are used to accelerate convergence to an optimal design. To compute
derivatives of the parameters, an adjoint-based method was developed. Using the adjoint technique, only two
electromagnetic problems need to be solved per iteration to evaluate the cost function and all the components
of the gradient vector, independent of the number of parameters in the design.
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