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Magnetic Resonance Imaging (MRI) is a non-invasive procedure used in the medical community as a powerful way of creating images of the human anatomy. MRI is preferred over other examination techniques such as X-ray computed tomography (CT) because of its excellent soft tissue discrimination as well as the absence of ionizing radiation. Currently most clinical MRI systems use the single radio frequency coil imaging. However over past several years research has increasingly focused on the concept of using arrays of mutually decoupled surface coils. These surface coil arrays can simultaneously acquire multiple images, resulting in an increase in the field of view. This thesis pursues the design and construction of a high impedance preamplifier with the goal of investigating the decoupling of a surface coil array in a 4.7T magnetic resonance system.

The profound digitization of modern microelectronic modules made Analog-to- Digital converters (ADC) key components in many systems. With resolutions up to 14bits and sampling rates in the 100s of MHz, the pipeline ADC is a prime candidate for a wide range of applications such as instrumentation, communications and consumer electronics. However, while past work focused on enhancing the performance of the pipeline ADC from an architectural standpoint, little has been done to individually address its fundamental building blocks. This work aims to achieve the latter by proposing design techniques to improve the performance of these blocks with minimal power consumption in low voltage environments, such that collectively high performance is achieved in the pipeline ADC. Towards this goal, a Recycling Folded Cascode (RFC) amplifier is proposed as an enhancement to the general performance of the conventional folded cascode. Tested in Taiwan Semiconductor Manufacturing Company (TSMC) 0.18?m Complementary Metal Oxide Semiconductor (CMOS) technology, the RFC provides twice the bandwidth, 8-10dB additional gain, more than twice the slew rate and improved noise performance over the conventional folded cascode-all at no additional power or silicon area. The direct auto-zeroing offset cancellation scheme is optimized for low voltage environments using a dual level common mode feedback (CMFB) circuit, and amplifier differential offsets up to 50mV are effectively cancelled. Together with the RFC, the dual level CMFB was used to implement a sample and hold amplifier driving a singleended load of 1.4pF and using only 2.6mA; at 200MS/s better than 9bit linearity is achieved. Finally a power conscious technique is proposed to reduce the kickback noise of dynamic comparators without resorting to the use of pre-amplifiers. When all techniques are collectively used to implement a 1Vpp 10bit 160MS/s pipeline ADC in Semiconductor Manufacturing International Corporation (SMIC) 0.18[mu]m CMOS, 9.2 effective number of bits (ENOB) is achieved with a near Nyquist-rate full scale signal. The ADC uses an area of 1.1mm2 and consumes 42mW in its analog core. Compared to recent state-of-the-art implementations in the 100-200MS/s range, the presented pipeline ADC uses the least power per conversion rated at 0.45pJ/conversion-step.

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The design of power amplifiers within a circuit simulator requires a good non-linear model that accurately predicts the electormagnetic behaviour of the power transistor. In recent years, a certain class of large signal frequency-dependent black-box behavioural modeling techniques known as Poly-Harmonic Distortion (PHD) models has been devised to mimic the non-linear unmatched RF transistor. These models promise a good prediction of the device behaviour under multi-harmonic periodic continuous wave inputs. This thesis describes the capabilities of the PHD modeling framework and the theoretical type of behaviour that it is capable of predicting. Specifically, the PHD framework cannot necessarily predict the response of a broadband aperiodic signal. This analysis will be performed by deriving the PHD modeling framework as a simplification of the Volterra series kernel functions under the assumption that the power transistor is operating under continuous periodic multi-harmonic voltage and current signals in a stable circuit. A PHD model will be seen as a set of describing functions that predict the response of the Device Under Test (DUT) for any given non-linear periodic continuous-wave inputs that have a specific fundamental frequency. Two popular implementations of PHD models that can be found in the literature are the X-parameter and Cardiff models. Each model formulates the describing functions of the general PHD model differently. The mathematical formulation of the X-parameter and Cardiff models will be discussed in order to provide a theoretical ground for comparing their robustness. The X-parameter model will be seen as the first-order Taylor series approximation of the PHD model describing functions around a Large Signal Operating Point (LSOP) of the device under test. The Cardiff large-signal model uses Fourier series coefficient functions that vary with the magnitude of the large signal(s) as the PHD model describing functions. This thesis will provide a breakdown of the measurement procedure required for the extraction of these models, the challenges involved in the measurement, as well as the mathematical extraction of the model coe cients from measurement data. As each of these models contain have extended versions that enhance the predictive capability of the model under stronger nonlinear modes of operation, a comparison is used to represent the cost of increasing model accuracy as a function of the increasing model complexity for each model. The order of complexity of each model can manifest itself in terms of the mathematical formulation, the number of parameters required and the measurement time that is required to extract each model for a given DUT. This comparison will fairly assess the relative strengths and weaknesses of each model.

The design of power amplifiers within a circuit simulator requires a good non-linear model that accurately predicts the electormagnetic behaviour of the power transistor. In recent years, a certain class of large signal frequency-dependent black-box behavioural modeling techniques known as Poly-Harmonic Distortion (PHD) models has been devised to mimic the non-linear unmatched RF transistor. These models promise a good prediction of the device behaviour under multi-harmonic periodic continuous wave inputs. This thesis describes the capabilities of the PHD modeling framework and the theoretical type of behaviour that it is capable of predicting. Specifically, the PHD framework cannot necessarily predict the response of a broadband aperiodic signal. This analysis will be performed by deriving the PHD modeling framework as a simplification of the Volterra series kernel functions under the assumption that the power transistor is operating under continuous periodic multi-harmonic voltage and current signals in a stable circuit. A PHD model will be seen as a set of describing functions that predict the response of the Device Under Test (DUT) for any given non-linear periodic continuous-wave inputs that have a specific fundamental frequency. Two popular implementations of PHD models that can be found in the literature are the X-parameter and Cardiff models. Each model formulates the describing functions of the general PHD model differently. The mathematical formulation of the X-parameter and Cardiff models will be discussed in order to provide a theoretical ground for comparing their robustness. The X-parameter model will be seen as the first-order Taylor series approximation of the PHD model describing functions around a Large Signal Operating Point (LSOP) of the device under test. The Cardiff large-signal model uses Fourier series coefficient functions that vary with the magnitude of the large signal(s) as the PHD model describing functions. This thesis will provide a breakdown of the measurement procedure required for the extraction of these models, the challenges involved in the measurement, as well as the mathematical extraction of the model coe cients from measurement data. As each of these models contain have extended versions that enhance the predictive capability of the model under stronger nonlinear modes of operation, a comparison is used to represent the cost of increasing model accuracy as a function of the increasing model complexity for each model. The order of complexity of each model can manifest itself in terms of the mathematical formulation, the number of parameters required and the measurement time that is required to extract each model for a given DUT. This comparison will fairly assess the relative strengths and weaknesses of each model.

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