Plasma diagnostics and ITO film deposition by RF-assisted closed-field dual magnetron system

Deposition of indium tin oxide (ITO) among various transparent conductive materials on flexible organic substrates has been intensively investigated among academics and industrials for a whole new array of imaginative optoelectronic products. One critical challenge coming with the organic materials is their poor thermal endurances, considering that the process currently used to produce industry-standard ITO usually involves relatively high substrate temperature in excess of 200°C and post-annealing. A lower processing temperature is thus demanded, among other desires of high deposition rate, large substrate area, good uniformity, and high quality of the deposited materials. For this purpose, we developed an RF-assisted closed-field dual magnetron sputtering system. The “prototype” system consists of a 3-inch unbalanced dual magnetron operated at a closed-field configuration. An RF coil was fabricated and placed between the two magnetron cathodes to initiate a secondary plasma. The concept is to increase the ionization faction with the RF enhancement and utilize the ion energy instead of thermal energy to facilitate the ITO film growth. The closed-field unbalanced magnetrons create a plasma in the intervening region rather than confine it near the target, thus achieving a large-area processing capability. An RF-compensated Langmuir probe was used to characterize and compare the plasmas in mirrored balanced and closed-field unbalanced magnetron configurations. The spatial distributions of the electron density ne and electron temperature Te were measured. The density profiles reflect the shapes of the plasma. Rather than intensively concentrated to the targets/cathodes in the balanced magnetrons, the plasma is more dispersive in the closed-field mode with a twice higher electron density in the substrate region. The RF assistance significantly enhances ne by one or two orders of magnitude higher. The effect of various other parameters, such as pressure, on the plasma was also studied. The ionization fractions of the sputtered atoms were measured using a gridded energy analyzer (GEA) combined with a quartz crystal microbalance (QCM). The presence of the RF plasma effectively increases the ITO ionization fraction to around 80% in both the balanced and closed-field unbalanced configurations. The ionization fraction also varies with pressure, maximizing at 5-10 mTorr. The study of the ionization not only facilitates understanding the plasma behaviors in the RF-assisted magnetron sputtering, but also provides a criterion for optimizing the film deposition process. ITO films were deposited on both glass and plastic (PET) substrates in the 3-inch RF-assisted closed-field magnetrons. The electrical resistivity and optical transmission transparency of the ITO films were measured. Appropriate RF assistance was shown to dramatically reduce the electrical resistivity. An ITO film with a resistivity of 1.2×10-3 Ω-cm and a visible light transmittance of 91% was obtained with a 225 W RF enhancement, while the substrate temperature was monitored as below 110°C. X-ray photoelectron spectroscopy (XPS) was employed to confirm the ITO film stoichiometry. The surface morphology of the ITO films and its effect on the film properties were studied using atomic force microscopy (AFM). The prototype of RF-assisted closed-field magnetron was further extended to a larger rectangular shaped dual magnetron in a flat panel display manufacturing system. Similar improvement of the ITO film conductivities by the auxiliary RF was observed on the large-area PET substrates. Meanwhile, significant deposition rates of 25-42 nm/min were achieved.

An experimental investigation of air flow and convective heat transfer in enclosures having large ventilative flow rates

The prediction of convective heat transfer in enclosures under high ventilative flow rates is primarily of interest for building design and simulation purposes. Current models are based on experiments performed forty years ago with flat plates under natural convection conditions.

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.