There is a growing need for interplanetary travel technology development. There are hence plans to build deep space human habitats, communication relays, and fuel depots. These can be classified as large space structures. To build large structures, it is essential that these are modular in nature. With modularization of structures, it becomes essential that interconnection of modules is developed. Docking systems enable interconnection of modules. The state-of-the-art technology in docking systems is the Power Data Grapple Fixture (PDGF), used on the International Space Station by the Canadarm2 robotic arm to grapple, latch onto and provide power to the object it has grappled. The PDGF is operated by highly skilled astronauts on the ISS and are prone to human errors. Therefore, there is a need for autonomous docking. Another issue with the PDGF is that it costs around 1 to 2 million US dollars to build the 26-inch diameter docking mechanism. Hence, there is a growing need to build a lower cost and scalable, smaller docking systems. Building scalable smaller docking systems will hence enable testing them on small satellites. With the increasing need for small, low cost, autonomous docking systems, this thesis has been proposed. This thesis focuses on modeling and autonomous control of an electromagnetic probe and cone docking mechanism. The electromagnetic docking system is known to be a highly nonlinear system. Hence, this work discusses various control strategies for this docking system using a levitation strategy.

Time-interleaved analog to digital converters (ADCs) have become critical components in high-speed communication systems. Consumers demands for smaller size, more bandwidth and more features from their communication systems have driven the market to use modern complementary metal-oxide-semiconductor (CMOS) technologies with shorter channel-length transistors and hence a more compact design. Downscaling the supply voltage which is required in submicron technologies benefits digital circuits in terms of power and area. Designing accurate analog circuits, however becomes more challenging due to the less headroom. One way to overcome this problem is to use calibration to compensate for the loss of accuracy in analog circuits.

Time-interleaving increases the effective data conversion rate in ADCs while keeping the circuit requirements the same. However, this technique needs special considerations as other design issues associated with using parallel identical channels emerge. The first and the most important is the practical issue of timing mismatch between channels, also called sample-time error, which can directly affect the performance of the ADC. Many techniques have been developed to tackle this issue both in analog and digital domains. Most of these techniques have high complexities especially when the number of channels exceeds 2 and some of them are only valid when input signal is a single tone sinusoidal which limits the application.

This dissertation proposes a sample-time error calibration technique which bests the previous techniques in terms of simplicity, and also could be used with arbitrary input signals. A 12-bit 650 MSPS pipeline ADC with 1.5 GHz analog bandwidth for digital beam forming systems is designed in IBM 8HP BiCMOS 130 nm technology. A front-end sample-and-hold amplifier (SHA) was also designed to compare with an SHA-less design in terms of performance, power and area. Simulation results show that the proposed technique is able to improve the SNDR by 20 dB for a mismatch of 50% of the sampling period and up to 29 dB at 37% of the Nyquist frequency. The designed ADC consumes 122 mW in each channel and the clock generation circuit consumes 142 mW. The ADC achieves 68.4 dB SNDR for an input of 61 MHz.

Alternative computation based on neural systems on a nanoscale device are of increasing interest because of the massive parallelism and scalability they provide. Neural based computation systems also offer defect finding and self healing capabilities. Traditional Von Neumann based architectures (which separate the memory and computation units) inherently suffer from the Von Neumann bottleneck whereby the processor is limited by the number of instructions it fetches. The clock driven based Von Neumann computer survived because of technology scaling. However as transistor scaling is slowly coming to an end with channel lengths becoming a few nanometers in length, processor speeds are beginning to saturate. This lead to the development of multi-core systems which process data in parallel, with each core being based on the Von Neumann architecture.

The human brain has always been a mystery to scientists. Modern day super computers are outperformed by the human brain in certain computations. The brain occupies far less space and consumes a fraction of the power a super computer does with certain processes such as pattern recognition. Neuromorphic computing aims to mimic biological neural systems on silicon to exploit the massive parallelism that neural systems offer. Neuromorphic systems are event driven systems rather than being clock driven. One of the issues faced by neuromorphic computing was the area occupied by these circuits. With recent developments in the field of nanotechnology, memristive devices on a nanoscale have been developed and show a promising solution. Memristor based synapses can be up to three times smaller than Complementary Metal Oxide Semiconductor (CMOS) based synapses.

In this thesis, the Programmable Metallization Cell (a memristive device) is used to prove a learning algorithm known as Spike Time Dependant Plasticity (STDP). This learning algorithm is an extension to Hebb’s learning rule in which the synapses weight can be altered by the relative timing of spikes across it. The synaptic weight with the memristor will be its conductance, and CMOS oscillator based circuits will be used to produce spikes that can modulate the memristor conductance by firing with different phases differences.