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.

Rational Function Interpolation of Electromagnetic Transfer Functions of High-Speed Interconnect Systems from Discrete Time-Domain and Frequency-Domain Data

We present new methodologies to generate rational function approximations of broadband electromagnetic responses of linear and passive networks of high-speed interconnects, and to construct SPICE-compatible, equivalent circuit representations of the generated rational functions. These new methodologies are driven by the desire to improve the computational efficiency of the rational function fitting process, and to ensure enhanced accuracy of the generated rational function interpolation and its equivalent circuit representation. Toward this goal, we propose two new methodologies for rational function approximation of high-speed interconnect network responses. The first one relies on the use of both time-domain and frequency-domain data, obtained either through measurement or numerical simulation, to generate a rational function representation that extrapolates the input, early-time transient response data to late-time response while at the same time providing a means to both interpolate and extrapolate the used frequency-domain data. The aforementioned hybrid methodology can be considered as a generalization of the frequency-domain rational function fitting utilizing frequency-domain response data only, and the time-domain rational function fitting utilizing transient response data only. In this context, a guideline is proposed for estimating the order of the rational function approximation from transient data. The availability of such an estimate expedites the time-domain rational function fitting process. The second approach relies on the extraction of the delay associated with causal electromagnetic responses of interconnect systems to provide for a more stable rational function process utilizing a lower-order rational function interpolation. A distinctive feature of the proposed methodology is its utilization of scattering parameters. For both methodologies, the approach of fitting the electromagnetic network matrix one element at a time is applied. It is shown that, with regard to the computational cost of the rational function fitting process, such an element-by-element rational function fitting is more advantageous than full matrix fitting for systems with a large number of ports. Despite the disadvantage that different sets of poles are used in the rational function of different elements in the network matrix, such an approach provides for improved accuracy in the fitting of network matrices of systems characterized by both strongly coupled and weakly coupled ports. Finally, in order to provide a means for enforcing passivity in the adopted element-by-element rational function fitting approach, the methodology for passivity enforcement via quadratic programming is modified appropriately for this purpose and demonstrated in the context of element-by-element rational function fitting of the admittance matrix of an electromagnetic multiport.

Dynamic-Data Driven Real-Time Identification for Electric Power Systems

Power system engineers face a double challenge: to operate electric power systems within narrow stability and security margins, and to maintain high reliability. There is an acute need to better understand the dynamic nature of power systems in order to be prepared for critical situations as they arise. Innovative measurement tools, such as phasor measurement units, can capture not only the slow variation of the voltages and currents but also the underlying oscillations in a power system. Such dynamic data accessibility provides us a strong motivation and a useful tool to explore dynamic-data driven applications in power systems. To fulfill this goal, this dissertation focuses on the following three areas: Developing accurate dynamic load models and updating variable parameters based on the measurement data, applying advanced nonlinear filtering concepts and technologies to real-time identification of power system models, and addressing computational issues by implementing the balanced truncation method. By obtaining more realistic system models, together with timely updated parameters and stochastic influence consideration, we can have an accurate portrait of the ongoing phenomena in an electrical power system. Hence we can further improve state estimation, stability analysis and real-time operation.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.