Matching Items (4)
Filtering by
- Genre: Doctoral Dissertation
Description
In this work, a highly sensitive strain sensing technique is developed to realize in-plane strain mapping for microelectronic packages or emerging flexible or foldable devices, where mechanical or thermal strain is a major concern that could affect the performance of the working devices or even lead to the failure of the devices. Therefore strain sensing techniques to create a contour of the strain distribution is desired.
The developed highly sensitive micro-strain sensing technique differs from the existing strain mapping techniques, such as digital image correlation (DIC)/micro-Moiré techniques, in terms of working mechanism, by filling a technology gap that requires high spatial resolution while simultaneously maintaining a large field-of-view. The strain sensing mechanism relies on the scanning of a tightly focused laser beam onto the grating that is on the sample surface to detect the change in the diffracted beam angle as a result of the strain. Gratings are fabricated on the target substrates to serve as strain sensors, which carries the strain information in the form of variations in the grating period. The geometric structure of the optical system inherently ensures the high sensitivity for the strain sensing, where the nanoscale change of the grating period is amplified by almost six orders into a diffraction peak shift on the order of several hundred micrometers. It significantly amplifies the small signal measurements so that the desired sensitivity and accuracy can be achieved.
The important features, such as strain sensitivity and spatial resolution, for the strain sensing technique are investigated to evaluate the technique. The strain sensitivity has been validated by measurements on homogenous materials with well known reference values of CTE (coefficient of thermal expansion). 10 micro-strain has been successfully resolved from the silicon CTE extraction measurements. Furthermore, the spatial resolution has been studied on predefined grating patterns, which are assembled to mimic the uneven strain distribution across the sample surface. A resolvable feature size of 10 µm has been achieved with an incident laser spot size of 50 µm in diameter.
In addition, the strain sensing technique has been applied to a composite sample made of SU8 and silicon, as well as the microelectronic packages for thermal strain mappings.
The developed highly sensitive micro-strain sensing technique differs from the existing strain mapping techniques, such as digital image correlation (DIC)/micro-Moiré techniques, in terms of working mechanism, by filling a technology gap that requires high spatial resolution while simultaneously maintaining a large field-of-view. The strain sensing mechanism relies on the scanning of a tightly focused laser beam onto the grating that is on the sample surface to detect the change in the diffracted beam angle as a result of the strain. Gratings are fabricated on the target substrates to serve as strain sensors, which carries the strain information in the form of variations in the grating period. The geometric structure of the optical system inherently ensures the high sensitivity for the strain sensing, where the nanoscale change of the grating period is amplified by almost six orders into a diffraction peak shift on the order of several hundred micrometers. It significantly amplifies the small signal measurements so that the desired sensitivity and accuracy can be achieved.
The important features, such as strain sensitivity and spatial resolution, for the strain sensing technique are investigated to evaluate the technique. The strain sensitivity has been validated by measurements on homogenous materials with well known reference values of CTE (coefficient of thermal expansion). 10 micro-strain has been successfully resolved from the silicon CTE extraction measurements. Furthermore, the spatial resolution has been studied on predefined grating patterns, which are assembled to mimic the uneven strain distribution across the sample surface. A resolvable feature size of 10 µm has been achieved with an incident laser spot size of 50 µm in diameter.
In addition, the strain sensing technique has been applied to a composite sample made of SU8 and silicon, as well as the microelectronic packages for thermal strain mappings.
ContributorsLiang, Hanshuang (Author) / Yu, Hongbin (Thesis advisor) / Poon, Poh Chieh Benny (Committee member) / Jiang, Hanqing (Committee member) / Zhang, Yong-Hang (Committee member) / Arizona State University (Publisher)
Created2014
Description
Genomic and proteomic sequences, which are in the form of deoxyribonucleic acid (DNA) and amino acids respectively, play a vital role in the structure, function and diversity of every living cell. As a result, various genomic and proteomic sequence processing methods have been proposed from diverse disciplines, including biology, chemistry, physics, computer science and electrical engineering. In particular, signal processing techniques were applied to the problems of sequence querying and alignment, that compare and classify regions of similarity in the sequences based on their composition. However, although current approaches obtain results that can be attributed to key biological properties, they require pre-processing and lack robustness to sequence repetitions. In addition, these approaches do not provide much support for efficiently querying sub-sequences, a process that is essential for tracking localized database matches. In this work, a query-based alignment method for biological sequences that maps sequences to time-domain waveforms before processing the waveforms for alignment in the time-frequency plane is first proposed. The mapping uses waveforms, such as time-domain Gaussian functions, with unique sequence representations in the time-frequency plane. The proposed alignment method employs a robust querying algorithm that utilizes a time-frequency signal expansion whose basis function is matched to the basic waveform in the mapped sequences. The resulting WAVEQuery approach is demonstrated for both DNA and protein sequences using the matching pursuit decomposition as the signal basis expansion. The alignment localization of WAVEQuery is specifically evaluated over repetitive database segments, and operable in real-time without pre-processing. It is demonstrated that WAVEQuery significantly outperforms the biological sequence alignment method BLAST for queries with repetitive segments for DNA sequences. A generalized version of the WAVEQuery approach with the metaplectic transform is also described for protein sequence structure prediction. For protein alignment, it is often necessary to not only compare the one-dimensional (1-D) primary sequence structure but also the secondary and tertiary three-dimensional (3-D) space structures. This is done after considering the conformations in the 3-D space due to the degrees of freedom of these structures. As a result, a novel directionality based 3-D waveform mapping for the 3-D protein structures is also proposed and it is used to compare protein structures using a matched filter approach. By incorporating a 3-D time axis, a highly-localized Gaussian-windowed chirp waveform is defined, and the amino acid information is mapped to the chirp parameters that are then directly used to obtain directionality in the 3-D space. This mapping is unique in that additional characteristic protein information such as hydrophobicity, that relates the sequence with the structure, can be added as another representation parameter. The additional parameter helps tracking similarities over local segments of the structure, this enabling classification of distantly related proteins which have partial structural similarities. This approach is successfully tested for pairwise alignments over full length structures, alignments over multiple structures to form a phylogenetic trees, and also alignments over local segments. Also, basic classification over protein structural classes using directional descriptors for the protein structure is performed.
ContributorsRavichandran, Lakshminarayan (Author) / Papandreou-Suppappola, Antonia (Thesis advisor) / Spanias, Andreas S (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Lacroix, Zoé (Committee member) / Arizona State University (Publisher)
Created2011
Description
Mostly, manufacturing tolerance charts are used these days for manufacturing tolerance transfer but these have the limitation of being one dimensional only. Some research has been undertaken for the three dimensional geometric tolerances but it is too theoretical and yet to be ready for operator level usage. In this research, a new three dimensional model for tolerance transfer in manufacturing process planning is presented that is user friendly in the sense that it is built upon the Coordinate Measuring Machine (CMM) readings that are readily available in any decent manufacturing facility. This model can take care of datum reference change between non orthogonal datums (squeezed datums), non-linearly oriented datums (twisted datums) etc. Graph theoretic approach based upon ACIS, C++ and MFC is laid out to facilitate its implementation for automation of the model. A totally new approach to determining dimensions and tolerances for the manufacturing process plan is also presented. Secondly, a new statistical model for the statistical tolerance analysis based upon joint probability distribution of the trivariate normal distributed variables is presented. 4-D probability Maps have been developed in which the probability value of a point in space is represented by the size of the marker and the associated color. Points inside the part map represent the pass percentage for parts manufactured. The effect of refinement with form and orientation tolerance is highlighted by calculating the change in pass percentage with the pass percentage for size tolerance only. Delaunay triangulation and ray tracing algorithms have been used to automate the process of identifying the points inside and outside the part map. Proof of concept software has been implemented to demonstrate this model and to determine pass percentages for various cases. The model is further extended to assemblies by employing convolution algorithms on two trivariate statistical distributions to arrive at the statistical distribution of the assembly. Map generated by using Minkowski Sum techniques on the individual part maps is superimposed on the probability point cloud resulting from convolution. Delaunay triangulation and ray tracing algorithms are employed to determine the assembleability percentages for the assembly.
ContributorsKhan, M Nadeem Shafi (Author) / Phelan, Patrick E (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Farin, Gerald (Committee member) / Roberts, Chell (Committee member) / Henderson, Mark (Committee member) / Arizona State University (Publisher)
Created2011
Description
Multi-sensor fusion is a fundamental problem in Robot Perception. For a robot to operate in a real world environment, multiple sensors are often needed. Thus, fusing data from various sensors accurately is vital for robot perception. In the first part of this thesis, the problem of fusing information from a LIDAR, a color camera and a thermal camera to build RGB-Depth-Thermal (RGBDT) maps is investigated. An algorithm that solves a non-linear optimization problem to compute the relative pose between the cameras and the LIDAR is presented. The relative pose estimate is then used to find the color and thermal texture of each LIDAR point. Next, the various sources of error that can cause the mis-coloring of a LIDAR point after the cross- calibration are identified. Theoretical analyses of these errors reveal that the coloring errors due to noisy LIDAR points, errors in the estimation of the camera matrix, and errors in the estimation of translation between the sensors disappear with distance. But errors in the estimation of the rotation between the sensors causes the coloring error to increase with distance.
On a robot (vehicle) with multiple sensors, sensor fusion algorithms allow us to represent the data in the vehicle frame. But data acquired temporally in the vehicle frame needs to be registered in a global frame to obtain a map of the environment. Mapping techniques involving the Iterative Closest Point (ICP) algorithm and the Normal Distributions Transform (NDT) assume that a good initial estimate of the transformation between the 3D scans is available. This restricts the ability to stitch maps that were acquired at different times. Mapping can become flexible if maps that were acquired temporally can be merged later. To this end, the second part of this thesis focuses on developing an automated algorithm that fuses two maps by finding a congruent set of five points forming a pyramid.
Mapping has various application domains beyond Robot Navigation. The third part of this thesis considers a unique application domain where the surface displace- ments caused by an earthquake are to be recovered using pre- and post-earthquake LIDAR data. A technique to recover the 3D surface displacements is developed and the results are presented on real earthquake datasets: El Mayur Cucupa earthquake, Mexico, 2010 and Fukushima earthquake, Japan, 2011.
On a robot (vehicle) with multiple sensors, sensor fusion algorithms allow us to represent the data in the vehicle frame. But data acquired temporally in the vehicle frame needs to be registered in a global frame to obtain a map of the environment. Mapping techniques involving the Iterative Closest Point (ICP) algorithm and the Normal Distributions Transform (NDT) assume that a good initial estimate of the transformation between the 3D scans is available. This restricts the ability to stitch maps that were acquired at different times. Mapping can become flexible if maps that were acquired temporally can be merged later. To this end, the second part of this thesis focuses on developing an automated algorithm that fuses two maps by finding a congruent set of five points forming a pyramid.
Mapping has various application domains beyond Robot Navigation. The third part of this thesis considers a unique application domain where the surface displace- ments caused by an earthquake are to be recovered using pre- and post-earthquake LIDAR data. A technique to recover the 3D surface displacements is developed and the results are presented on real earthquake datasets: El Mayur Cucupa earthquake, Mexico, 2010 and Fukushima earthquake, Japan, 2011.
ContributorsKrishnan, Aravindhan K (Author) / Saripalli, Srikanth (Thesis advisor) / Klesh, Andrew (Committee member) / Fainekos, Georgios (Committee member) / Thangavelautham, Jekan (Committee member) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2016