A path-based framework for analyzing large markov models

Reliability and dependability modeling can be employed during many stages of analysis of a computing system to gain insights into its critical behaviors. To provide useful results, realistic models of systems are often necessarily large and complex. Numerical analysis of these models presents a formidable challenge because the sizes of their state-space descriptions grow exponentially in proportion to the sizes of the models. On the other hand, simulation of the models requires analysis of many trajectories in order to compute statistically correct solutions. This dissertation presents a novel framework for performing both numerical analysis and simulation. The new numerical approach computes bounds on the solutions of transient measures in large continuous-time Markov chains (CTMCs). It extends existing path-based and uniformization-based methods by identifying sets of paths that are equivalent with respect to a reward measure and related to one another via a simple structural relationship. This relationship makes it possible for the approach to explore multiple paths at the same time,· thus significantly increasing the number of paths that can be explored in a given amount of time. Furthermore, the use of a structured representation for the state space and the direct computation of the desired reward measure (without ever storing the solution vector) allow it to analyze very large models using a very small amount of storage. Often, path-based techniques must compute many paths to obtain tight bounds. In addition to presenting the basic path-based approach, we also present algorithms for computing more paths and tighter bounds quickly. One resulting approach is based on the concept of path composition whereby precomputed subpaths are composed to compute the whole paths efficiently. Another approach is based on selecting important paths (among a set of many paths) for evaluation. Many path-based techniques suffer from having to evaluate many (unimportant) paths. Evaluating the important ones helps to compute tight bounds efficiently and quickly.

Antimony-based type-II superlattice infrared detectors

Numerous applications within the mid- and long-wavelength infrared are driving the search for efficient and cost effective detection technologies in this regime. Theoretical calculations have predicted high performance for InAs/GaSb type-II superlattice structures, which rely on mature growth of III-V semiconductors and offer many levels of freedom in design due to band structure engineering. This work focuses on the fabrication and characterization of type-II superlattice infrared detectors. Standard UV-based photolithography was used combined with chemical wet or dry etching techniques in order to fabricate antinomy-based type-II superlattice infrared detectors. Subsequently, Fourier transform infrared spectroscopy and radiometric techniques were applied for optical characterization in order to obtain a detector's spectrum and response, as well as the overall detectivity in combination with electrical characterization. Temperature dependent electrical characterization was used to extract information about the limiting dark current processes. This work resulted in the first demonstration of an InAs/GaSb type-II superlattice infrared photodetector grown by metalorganic chemical vapor deposition. A peak detectivity of 1.6x10^9 Jones at 78 K was achieved for this device with a 11 micrometer zero cutoff wavelength. Furthermore the interband tunneling detector designed for the mid-wavelength infrared regime was studied. Similar results to those previously published were obtained.