Advanced Adhesion Strength Testing Methods of Thin Film Multilayers in Electronic Packaging Systems

With the continued miniaturization and increasing performance of electronic devices, new technical challenges have arisen. One such issue is delamination occurring at critical interfaces inside the device. This major reliability issue can occur during the manufacturing process or during normal use of the device. Proper evaluation of the adhesion strength of critical interfaces early in the product development cycle can help reduce reliability issues and time-to-market of the product. However, conventional adhesion strength testing is inherently limited in the face of package miniaturization, which brings about further technical challenges to quantify design integrity and reliability. Although there are many different interfaces in today's advanced electronic packages, they can be generalized into two main categories: 1) rigid to rigid connections with a thin flexible polymeric layer in between, or 2) a thin film membrane on a rigid structure. Knowing that every technique has its own advantages and disadvantages, multiple testing methods must be enhanced and developed to be able to accommodate all the interfaces encountered for emerging electronic packaging technologies. For evaluating the adhesion strength of high adhesion strength interfaces in thin multilayer structures a novel adhesion test configuration called “single cantilever adhesion test (SCAT)” is proposed and implemented for an epoxy molding compound (EMC) and photo solder resist (PSR) interface. The test method is then shown to be capable of comparing and selecting the stronger of two potential EMC/PSR material sets. Additionally, a theoretical approach for establishing the applicable testing domain for a four-point bending test method was presented. For evaluating polymeric films on rigid substrates, major testing challenges are encountered for reducing testing scatter and for factoring in the potentially degrading effect of environmental conditioning on the material properties of the film. An advanced blister test with predefined area test method was developed that considers an elasto-plastic analytical solution and implemented for a conformal coating used to prevent tin whisker growth. The advanced blister testing with predefined area test method was then extended by employing a numerical method for evaluating the adhesion strength when the polymer’s film properties are unknown.

Tensor Completion for Multidimensional Inverse Problems with Applications to Magnetic Resonance Relaxometry

This thesis deals with tensor completion for the solution of multidimensional inverse problems. We study the problem of reconstructing an approximately low rank tensor from a small number of noisy linear measurements. New recovery guarantees, numerical algorithms, non-uniform sampling strategies, and parameter selection algorithms are developed. We derive a fixed point continuation algorithm for tensor completion and prove its convergence. A restricted isometry property (RIP) based tensor recovery guarantee is proved. Probabilistic recovery guarantees are obtained for sub-Gaussian measurement operators and for measurements obtained by non-uniform sampling from a Parseval tight frame. We show how tensor completion can be used to solve multidimensional inverse problems arising in NMR relaxometry. Algorithms are developed for regularization parameter selection, including accelerated k-fold cross-validation and generalized cross-validation. These methods are validated on experimental and simulated data. We also derive condition number estimates for nonnegative least squares problems. Tensor recovery promises to significantly accelerate N-dimensional NMR relaxometry and related experiments, enabling previously impractical experiments. Our methods could also be applied to other inverse problems arising in machine learning, image processing, signal processing, computer vision, and other fields.

VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies.