Next generation high density three-dimensional magnetic recording systems

This work is the first work using patterned soft underlayers in multilevel three-dimensional vertical magnetic data storage systems. The motivation stems from an exponentially growing information stockpile, and a corresponding need for more efficient storage devices with higher density. The world information stockpile currently exceeds 150EB (ExaByte=1x1018Bytes); most of which is in analog form. Among the storage technologies (semiconductor, optical and magnetic), magnetic hard disk drives are posed to occupy a big role in personal, network as well as corporate storage. However; this mode suffers from a limit known as the Superparamagnetic limit; which limits achievable areal density due to fundamental quantum mechanical stability requirements. There are many viable techniques considered to defer superparamagnetism into the 100's of Gbit/in2 such as: patterned media, Heat-Assisted Magnetic Recording (HAMR), Self Organized Magnetic Arrays (SOMA), antiferromagnetically coupled structures (AFC), and perpendicular magnetic recording. Nonetheless, these techniques utilize a single magnetic layer; and can thusly be viewed as two-dimensional in nature. In this work a novel three-dimensional vertical magnetic recording approach is proposed. This approach utilizes the entire thickness of a magnetic multilayer structure to store information; with potential areal density well into the Tbit/in2 regime. ^ There are several possible implementations for 3D magnetic recording; each presenting its own set of requirements, merits and challenges. The issues and considerations pertaining to the development of such systems will be examined, and analyzed using empirical and numerical analysis techniques. Two novel key approaches are proposed and developed: (1) Patterned soft underlayer (SUL) which allows for enhanced recording of thicker media, (2) A combinatorial approach for 3D media development that facilitates concurrent investigation of various film parameters on a predefined performance metric. A case study is presented using combinatorial overcoats of Tantalum and Zirconium Oxides for corrosion protection in magnetic media. ^ Feasibility of 3D recording is demonstrated, and an emphasis on 3D media development is emphasized as a key prerequisite. Patterned SUL shows significant enhancement over conventional "un-patterned" SUL, and shows that geometry can be used as a design tool to achieve favorable field distribution where magnetic storage and magnetic phenomena are involved. ^

Focused ion beam manufacturing of magnetic nanostructures

A number of patterning methods including conventional photo-lithography and E-beam lithography have been employed to pattern devices with critical dimensions of submicrometer levels. The methods of device fabrication by lithography and multilevel processing are usually specific to the chemical and physical properties of the etchants and materials used, and require a number of processing steps. As an alternative, focused ion beam (FIB) lithography is a unique and straightforward tool to rapidly develop nanomagnetic prototyping devices. This feature of FIB is critical to conduct the basic study necessary to advance the state-of-the-art in magnetic recording. ^ The dissertation develops a specific design of nanodevices and demonstrates FIB-fabricated stable and reproducible magnetic nanostructures with a critical dimension of about 10 nm. The project included the fabrication of a patterned single and multilayer magnetic media with areal densities beyond 10 Terabit/in 2. Each block had perpendicular or longitudinal magnetic anisotropy and a single domain structure. The purpose was to demonstrate how the ability of FIB to directly etch nanoscale patterns allowed exploring (even in the academic environment) the true physics of various types of nanostructures. ^ Another goal of this study was the investigation of FIB patterned magnetic media with a set of characterization tools: e.g. Spinstand Guzik V2002, magnetic force microscopy, scanning electron microscopy with energy dispersive system and wavelength dispersive system. ^ In the course of this work, a unique prototype of a record high density patterned magnetic media device capable of 10 terabit/in 2 was built. The read/write testing was performed by a Guzik spinstand. The readback signals were recorded and analyzed by a digital oscilloscope. A number of different configurations for writing and reading information from a magnetic medium were explored. The prototype transducers for this work were fabricated via FIB trimming of different magnetic recording heads. ^

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Developing a high density platinum/alumina hermetic feedthrough

Typically, hermetic feedthroughs for implantable devices, such as pacemakers, use a alumina ceramic insulator brazed to a platinum wire pin. This combination of material has a long history in implantable devices and has been approved by the FDA for implantable hermetic feedthroughs. The growing demand for increased input/output (I/O) hermetic feedthroughs for implantable neural stimulator applications could be addressed by developing a new, cofired platinum/alumina multilayer ceramic technology in a configuration that supports 300 plus I/Os, which is not commercially available. Seven platinum powders with different particle sizes were used to develop different conductive cofire inks to control the densification mismatch between platinum and alumina. Firing profile (ramp rate, burn- out and holding times) and firing atmosphere and concentrations (hydrogen (wet/dry), air, neutral, vacuum) were also optimized. Platinum and alumina exhibit the alloy formation reaction in a reduced atmosphere. Formation of any compound can increase the bonding of the metal/ceramic interface, resulting in enhanced hermeticity. The feedthrough fabricated in a reduced atmosphere demonstrated significantly superior performance than that of other atmospheres. A composite structure of tungsten/platinum ratios graded thru the via structure (pure W, 50/50 W/Pt, 80/20 Pt/W and pure Pt) exhibited the best performance in comparison to the performance of other materials used for ink metallization. Studies on the high temperature reaction of platinum and alumina, previously unreported, showed that, at low temperatures in reduced atmosphere, Pt 3Al or Pt8Al21 with a tetragonal structure would be formed. Cubic Pt3Al is formed upon heating the sample to temperatures above 1350 °C. This cubic structure is the equilibrium state of Pt-Al alloy at high temperatures. The alumina dissolves into the platinum ink and is redeposited as a surface coating. This was observed on both cofired samples and pure platinum thin films coated on a 99.6 Wt% alumina and fired at 1550 °C. Different mechanisms are proposed to describe this behavior based on the size of the platinum particle

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.