The objective of this Master's thesis was to design and implement a low power Analog to Digital Converter (ADC) used for sensor measurements. In the complete measurement unit, in which the ADC is part of, different sensors will be measured. One set of these sensors are three strain gauges with weak output signals which are to be pre-amplified before being converted. The focus of the application for the ADC has been these sensors as they were considered a limiting factor. The report describes theory for the algorithmic and incremental converter as well as a hybrid converter utilizing both of the two converter structures. All converters are based on one operational amplifier and they operate in repetitive fashions to obtain power efficient designs on a small chip area although at low conversion rates. Two converters have been designed and implemented to different degrees of completeness. One is a 13 bit algorithmic (or cyclic) converter which uses a switching scheme to reduce the problem of capacitor mismatch. This converter was implemented at transistor level and evaluated separately and to some extent also with sub-components. The second converter is a hybrid converter using both the operation of the algorithmic and incremental converter to obtain 16 bits of resolution while still having a fairly high sample rate.

Energy efficiency is a key requirement for wireless sensor nodes, biomedical implants, and wearable devices. The energy consumption of the sensor node needs to be minimized to avoid battery replacement, or even better, to enable the device to survive on energy harvested from the ambient. Capacitive sensors do not consume static power; thus, they are attractive from an energy efficiency perspective. In addition, they can be employed in a wide range of sensing applications. However, the sensor readout circuit–i.e., the capacitance-to-digital converter (CDC)–can be the dominant source of energy consumption in the system. Thus, the development of energy-efficient CDCs is crucial to minimizing the energy consumption of capacitive sensor nodes. In the first part of this dissertation, we propose several energy-efficient CDC architectures for low-energy sensor nodes. First, we propose a digitally-controlled coarsefine multislope CDC that employs both current and frequency scaling to achieve significant improvement in energy efficiency. Second, we analyze the limitations of successive approximation (SAR) CDC, and we address these limitations by proposing a robust parasitic-insensitive opamp-based SAR CDC. Third, we propose an inverter-based SAR CDC that achieves an energy efficiency figure-of-merit (FoM) of 31fJ/Step, which is the best energy efficiency FoM reported to date. Fourth, we propose a differential SAR CDC with quasi-dynamic operation to maintain excellent energy efficiency for a scalable sample rate. In the second part of this dissertation, we study the matching properties of small integrated capacitors, which are an integral component of energy-efficient CDCs. Despite conventional wisdom, we experimentally illustrate that the mismatch of small capacitors can be directly measured, and we report mismatch measurements for subfemtofarad integrated capacitors. We also correct the common misconception that lateral capacitors match better than vertical capacitors, and we identify the conditions that make one implementation preferable. In the third and last part of this dissertation, we investigate the potential of novel metal-organic framework (MOF) thin films in capacitive gas sensing. We provide sensitivity-based optimization and simple fabrication flow for capacitive interdigitated electrodes. We use a custom flexible gas sensor test setup that is designed and built in-house to characterize MOF-based capacitive gas sensors.