Implementations of Low-power μProcessor System for Miniaturized IoT Applications

Miniaturized sensing devices enable the IoT for many practical applications. Due to the small form factor, these types of devices have limited battery size, and therefore efficient energy consumption is a key requirement. However, since they are used in a wide range of operating conditions that vary across time, it is challenging for them to achieve energy efficiency.

In this context, this dissertation focuses on achieving energy-efficiency in miniaturized sensors. The first part presents a dynamic power management technique for the μprocessor, which enables on-chip closed-loop minimum-energy-point tracking and hence guarantees energy-optimal operation at all times. The next part presents a µprocessor system designed for high temperature applications. It features a deep sleep-mode that allows the complete system to retain full 16-kB SRAM contents with 0.54µW at 125˚C, which is 26× lower than the baseline design. The last two parts present essential sub-blocks required for low-power μprocessor systems: a wide-range level converter and a switched-capacitor DC-DC converter. The proposed level converter offers robust operation across a wide range of low and high supply voltages as well as PVT variations. The switched-capacitor DC-DC converters are automatically generated from the given input specifications. Based on the theoretical analyses, the proposed DC-DC generation tool directly finds the optimal design parameters.

Chair: Professor Dennis Sylvester