Formal Multilevel Hierarchical Verification of Synchronous MOS Circuits

I have designed and implemented a system for the multilevel verification of synchronous MOS VLSI circuits. The system, called Silica Pithecus, accepts the schematic of an MOS circuit and a specification of the circuit's intended digital behavior. Silica Pithecus determines if the circuit meets its specification. If the circuit fails to meet its specification Silica Pithecus returns to the designer the reason for the failure. Unlike earlier verifiers which modelled primitives (e.g., transistors) as unidirectional digital devices, Silica Pithecus models primitives more realistically. Transistors are modelled as bidirectional devices of varying resistances, and nodes are modelled as capacitors. Silica Pithecus operates hierarchically, interactively, and incrementally. Major contributions of this research include a formal understanding of the relationship between different behavioral descriptions (e.g., signal, boolean, and arithmetic descriptions) of the same device, and a formalization of the relationship between the structure, behavior, and context of device. Given these formal structures my methods find sufficient conditions on the inputs of circuits which guarantee the correct operation of the circuit in the desired descriptive domain. These methods are algorithmic and complete. They also handle complex phenomena such as races and charge sharing. Informal notions such as races and hazards are shown to be derivable from the correctness conditions used by my methods.

Taming Chaotic Circuits

Control algorithms that exploit chaotic behavior can vastly improve the performance of many practical and useful systems. The program Perfect Moment is built around a collection of such techniques. It autonomously explores a dynamical system's behavior, using rules embodying theorems and definitions from nonlinear dynamics to zero in on interesting and useful parameter ranges and state-space regions. It then constructs a reference trajectory based on that information and causes the system to follow it. This program and its results are illustrated with several examples, among them the phase-locked loop, where sections of chaotic attractors are used to increase the capture range of the circuit.

Segmentation of Brain Tissue from Magnetic Resonance Images

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. Brain tissue is a particularly complex structure, and its segmentation is an important step for studies in temporal change detection of morphology, as well as for 3D visualization in surgical planning. In this paper, we present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the Computer Vision literature: EM segmentation, binary morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation in a way that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on IBM's supercomputer Power Visualization System for a database of 20 brain scans each with 256x256x124 voxels and validate those against segmentations generated by neuroanatomy experts.

Biologically Plausible Neural Circuits for Realization of Maximum Operations

Object recognition in the visual cortex is based on a hierarchical architecture, in which specialized brain regions along the ventral pathway extract object features of increasing levels of complexity, accompanied by greater invariance in stimulus size, position, and orientation. Recent theoretical studies postulate a non-linear pooling function, such as the maximum (MAX) operation could be fundamental in achieving such invariance. In this paper, we are concerned with neurally plausible mechanisms that may be involved in realizing the MAX operation. Four canonical circuits are proposed, each based on neural mechanisms that have been previously discussed in the context of cortical processing. Through simulations and mathematical analysis, we examine the relative performance and robustness of these mechanisms. We derive experimentally verifiable predictions for each circuit and discuss their respective physiological considerations.

Preliminary Characterisation of Low-Temperature Bonded Copper Interconnects for 3-D Integrated Circuits

Three dimensional (3-D) integrated circuits can be fabricated by bonding previously processed device layers using metal-metal bonds that also serve as layer-to-layer interconnects. Bonded copper interconnects test structures were created by thermocompression bonding and the bond toughness was measured using the four-point test. The effects of bonding temperature, physical bonding and failure mechanisms were investigated. The surface effects on copper surface due to pre-bond clean (with glacial acetic acid) were also looked into. A maximum average bond toughness of approximately 35 J/m² was obtained bonding temperature 300 C.

Surface Functionalization of Monodisperse Magnetic Nanoparticles

We present a systematic methodology to functionalize magnetic nanoparticles through surface-initiated atom-transfer radical polymerization (ATRP). The magnetite nanoparticles are prepared according to the method proposed by Sun et al. (2004), which leads to a monodisperse population of ~ 6 nm particles stabilized by oleic acid. The functionalization of the nanoparticles has been performed by transforming particles into macro-initiators for the ATRP, and to achieve this two different routes have been explored. The first one is the ligand-exchange method, which consists of replacing some oleic acid molecules adsorbed on the particle surface with molecules that act as an initiator for ATRP. The second method consists in using the addition reaction of bromine to the oleic acid double bond, which turns the oleic acid itself into an initiator for the ATRP. We have then grown polymer brushes of a variety of acrylic polymers on the particles, including polyisopropylacrylamide and polyacrylic acid. The nanoparticles so functionalized are water soluble and show responsive behavior: either temperature responsive behavior when polyisopropylacrylamide is grown from the surface or PH responsive in the case of polyacrylic acid. This methodology has potential applications in the control of clustering of magnetic nanoparticles.

Synthesis and Characterization of Magnetic Nanoparticles with High Magnetization and Good Oxidation Resistibility

Magnetic nanoparticles attract increasing attention because of their current and potential biomedical applications, such as, magnetically targeted and controlled drug delivery, magnetic hyperthermia and magnetic extraction. Increased magnetization can lead to improved performance in targeting and retention in drug delivery and a higher efficiency in biomaterials extraction. We reported an approach to synthesize iron contained magnetic nanoparticles with high magnetization and good oxidation resistibility by pyrolysis of iron pentacarbonyl (Fe(CO)[subscript 5]) in methane (CH[subscript 4]). Using the high reactivity of Fe nanoparticles, decomposition of CH[subscript 4] on the Fe nanoparticles leads to the formation of nanocrystalline iron carbides at a temperature below 260°C. Structural investigation indicated that the as-synthesized nanoparticles contained crystalline bcc Fe, iron carbides and spinel iron oxide. The Mössbauer and DSC results testified that the as-synthesized nanoparticle contained three crystalline iron carbide phases, which converted to Fe[subscript 3]C after a heat treatment. Surface analysis suggested that the as-synthesized and subsequently heated iron-iron carbide particles were coated by iron oxide, which originated from oxidization of surface Fe atoms. The heat-treated nanoparticles exhibited a magnetization of 160 emu/g, which is two times of that of currently used spinel iron oxide nanoparticles. After heating in an acidic solution with a pH value of 5 at 60°C for 20 h, the nanoparticles retained 90 percentage of the magnetization.