Testing microelectronic biofluidic systems

According to the 2005 International Technology Roadmap for Semiconductors, the integration of emerging nondigital CMOS technologies will require radically different test methods, posing a major challenge for designers and test engineers. One such technology is microelectronic fluidic (MEF) arrays, which have rapidly gained importance in many biological, pharmaceutical, and industrial applications. The advantages of these systems, such as operation speed, use of very small amounts of liquid, on-board droplet detection, signal conditioning, and vast digital signal processing, make them very promising. However, testable design of these devices in a mass-production environment is still in its infancy, hampering their low-cost introduction to the market. This article describes analog and digital MEF design and testing method

Graph-based simulated annealing: a hybrid approach to stochastic modeling of complex microstructures

A stochastic model is proposed for the efficient simulation of complex three-dimensional microstructures consisting of two different phases. The model is based on a hybrid approach, where in a first step a graph model is developed using ideas from stochastic geometry. Subsequently, the microstructure model is built by applying simulated annealing to the graph model. As an example of application, the model is fitted to a tomographic image describing the microstructure of electrodes in Li-ion batteries. The goodness of model fit is validated by comparing morphological characteristics of experimental and simulated data

Unconventional machine learning of genome-wide human cancer data

Recent advances in high-throughput genomic technologies coupled with exponential increases in computer processing and memory have allowed us to interrogate the complex aberrant molecular underpinnings of human disease from a genome-wide perspective. While the deluge of genomic information is expected to increase, a bottleneck in conventional high-performance computing is rapidly approaching. Inspired in part by recent advances in physical quantum processors, we evaluated several unconventional machine learning (ML) strategies on actual human tumor data. Here we show for the first time the efficacy of multiple annealing-based ML algorithms for classification of high-dimensional, multi-omics human cancer data from the Cancer Genome Atlas. To assess algorithm performance, we compared these classifiers to a variety of standard ML methods. Our results indicate the feasibility of using annealing-based ML to provide competitive classification of human cancer types and associated molecular subtypes and superior performance with smaller training datasets, thus providing compelling empirical evidence for the potential future application of unconventional computing architectures in the biomedical sciences

Edge Detection by Cost Minimization

Edge detection is cast as a problem in cost minimization. This is achieved by the formulation of two cost functions which evaluate the quality of edge configurations. The first is a comparative cost function (CCF), which is a linear sum of weighted cost factors. It is heuristic in nature and can be applied only to pairs of similar edge configurations. It measures the relative quality between the configurations. The detection of edges is accomplished by a heuristic iterative search algorithm which uses the CCF to evaluate edge quality. The second cost function is the absolute cost function (ACF), which is also a linear sum of weighted cost factors. The cost factors capture desirable characteristics of edges such as accuracy in localization, thinness, and continuity. Edges are detected by finding the edge configurations that minimize the ACF. We have analyzed the function in terms of the characteristics of the edges in minimum cost configurations. These characteristics are directly dependent of the associated weight of each cost factor. Through the analysis of the ACF, we provide guidelines on the choice of weights to achieve certain characteristics of the detected edges. Minimizing the ACF is accomplished by the use of Simulated Annealing. We have developed a set of strategies for generating next states for the annealing process. The method of generating next states allows the annealing process to be executed largely in parallel. Experimental results are shown which verify the usefulness of the CCF and ACF techniques for edge detection. In comparison, the ACF technique produces better edges than the CCF or other current detection techniques

Ultrathin polymer film mechanics: the role of entanglements and morphology

Polymer thin films are used in many applications including packaging, electronics, and membranes where they can be freestanding or serve as coatings within a multilayer system. In an effort to reduce plastic waste and conserve energy, minimizing the thickness of these applied polymer films is necessary but requires an understanding of the mechanical properties and how they change as film thickness decreases. Polymer chains exhibit changes in mobility and entanglements when confined in thin film geometries. Utilizing custom-built instrumentation that can measure the complete stress-strain response of polymer films below 100 nm in thickness, this dissertation explores the physical changes in polymer molecules, specifically related to entanglements and morphology, in ultrathin geometries and relates them to the observed mechanical response. To systematically manipulate entanglements, polystyrene of varying chain lengths is blended in different ratios and the complete uniaxial stress-strain response is measured for 100 nm films on a liquid surface (Chapter 2). The strength of these macroscopic films is quantitatively compared to uniaxial extension in molecular dynamics simulations of similar blended glassy films. Based on these results a mean-field model relating the mechanical response to the number of load-bearing entanglements within the systems is developed. Moving on to a more complex, phase-separated system, the effect of morphology on poly(styrene-b-2-vinylpyridine) films is measured in a freestanding state. While maintaining a constant volume fraction in the block copolymer, the morphology is altered through solvent vapor annealing in chloroform. Through uniaxial extension, a higher maximum stress is measured in the lamellar morphology compared to the cylindrical morphology and a similar elastic modulus is measured for the two morphologies. Values for these two mechanical properties in both morphologies are higher than for polystyrene and poly(2-vinylpyridine) homopolymers. These enhanced properties are related to the chain conformations within the two morphologies and residual stresses. However, softening of P2VP is observed in the presence of water. To explore this softening, the two morphologies of poly(styrene-b-2-vinylpyridine) are measured in uniaxial extension on water’s surface. Elastic moduli and maximum stresses are reported that are below what is measured for the homopolymer components. The cylindrical morphology is also stronger of the two phase-separated morphologies. Both morphologies exhibit increases in failure strain of 10x. A reduced complex shear modulus and glass transition temperature are measured for poly(2-vinylpyridine) in the presence of water. These electrostatic interactions between water and the poly(2-vinylpyridine) are responsible for the extreme ductility and weakened mechanical strength observed. Through this dissertation, the number of load-bearing entanglements within polystyrene blends is quantified and the mechanical response of a phase-separated block copolymer is measured in two environments examining the effects of morphology and expanding the knowledge of ultrathin film mechanics