Rapid technological advancement in CMOS technologies has resulted in increased deployment of low-power Internet-of-Things (IoT) devices. As batteries, used to power-up these devices, suffer from limited lifespan, powering up numerous devices have become a major concern. Radio frequency (RF) is ubiquitous in the surroundings from which energy can be harvested and utilized to increase battery lifetime. Even for low-power sensors, RF energy harvesters can be utilized as primary power sources. However, power density of RF signals is very low and therefore building blocks of RF energy harvester need to be designed carefully to maximize efficiency to gain suitable output power. This research is focused on the design of an RF energy harvesting system in standard CMOS technology. The main goal of this research is to design an RF energy harvesting system with high power conversion efficiency (PCE) and adequate output voltage for low input power. The proposed dynamic voltage compensated cross-coupled fully differential rectifier is capable of providing very high PCE. The synchronous DC-DC boost converter provides stable DC output voltage. Rectifier and DC-DC converter of the system have been designed by using low-power transistors to ensure operation at very low input power. In order to maximize the power transfer through the system, matching network and maximum power point tracking (MPPT) controller has been implemented. In order to cope with rapid input power variation, a machine learning (ML) based MPPT controller has been designed and implemented into FPGA. The proposed ML based MPPT controller has demonstrated fast response time. To further enhance the performance of the RF energy harvesting system, a self-compensated rectifier integrated energy harvesting system is also presented. The energy extracted by using the proposed RF energy harvesting systems can easily be stored and utilized to fully power up low-power sensors used for IoT devices. Integration of RF energy harvester with these devices will significantly reduce the maintenance cost and result in energy-effluent IoT technologies.Includes bibliographical references
