Photovoltaic Energy Harvesting for Millimeter-Scale Systems

The Internet of Things (IoT) based on mm-scale sensors is a transformational technology that opens up new capabilities for biomedical devices, surveillance, micro-robots and industrial monitoring. Energy harvesting approaches to power IoT have traditionally included thermal, vibration and radio frequency. However, the achievement of efficient energy scavenging for IoT at the mm-scale or sub mm-scale has been elusive. In this work, I show that photovoltaic (PV) cells at the mm-scale can be an alternative means of wireless power transfer to mm-scale sensors for IoT, utilizing ambient indoor lighting or intentional irradiation of near-infrared (NIR) LED sources through biological tissue. Single silicon and GaAs photovoltaic cells at the mm-scale can achieve a power conversion efficiency of more than 17 % for silicon and 30 % for GaAs under low-flux NIR irradiation at 850 nm through the optimized device structure and sidewall/surface passivation studies, which guarantees perpetual operation of mm-scale sensors. Furthermore, monolithic single-junction GaAs photovoltaic modules offer a means for series-interconnected cells to provide sufficient voltage (> 5 V) for direct battery charging, and bypassing needs for voltage up-conversion circuitry. However, there is a continuing challenge to miniaturize such PV systems down to the sub mm-scale with minimal optical losses from device isolation and metal interconnects and efficient voltage up-conversion. Vertically stacked dual-junction PV cells and modules are demonstrated to increase the output voltage per cell and minimize area losses for direct powering of miniature devices for IoT and bio-implantable applications with low-irradiance narrowband spectral illumination. Dual-junction PV cells at small dimensions (150 µm x 150 µm) demonstrate power conversion efficiency greater than 22 % with more than 1.2 V output voltage under low-flux 850 nm NIR LED illumination, which is sufficient for batteryless operation of miniaturized CMOS IC chips. The output voltage of dual-junction PV modules with eight series-connected single cells is greater than 10 V while maintaining an efficiency of more than 18 %. Finally, I demonstrate monolithic PV/LED modules at the µm-scale for brain-machine interfaces, enabling two-way optical power and data transfer in a through-tissue configuration. The wafer-level assembly plan for the 3D vertical integration of three different systems including GaAs LED/PV modules, CMOS silicon chips, and neural probes is proposed.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163261/1/esmoon_1.pd

Energy-Efficient Circuit Designs for Miniaturized Internet of Things and Wireless Neural Recording

Internet of Things (IoT) have become omnipresent over various territories including healthcare, smart building, agriculture, and environmental and industrial monitoring. Today, IoT are getting miniaturized, but at the same time, they are becoming more intelligent along with the explosive growth of machine learning. Not only do IoT sense and collect data and communicate, but they also edge-compute and extract useful information within the small form factor. A main challenge of such miniaturized and intelligent IoT is to operate continuously for long lifetime within its low battery capacity. Energy efficiency of circuits and systems is key to addressing this challenge. This dissertation presents two different energy-efficient circuit designs: a 224pW 260ppm/°C gate-leakage-based timer for wireless sensor nodes (WSNs) for the IoT and an energy-efficient all analog machine learning accelerator with 1.2 µJ/inference of energy consumption for the CIFAR-10 and SVHN datasets. Wireless neural interface is another area that demands miniaturized and energy-efficient circuits and systems for safe long-term monitoring of brain activity. Historically, implantable systems have used wires for data communication and power, increasing risks of tissue damage. Therefore, it has been a long-standing goal to distribute sub-mm-scale true floating and wireless implants throughout the brain and to record single-neuron-level activities. This dissertation presents a 0.19×0.17mm2 0.74µW wireless neural recording IC with near-infrared (NIR) power and data telemetry and a 0.19×0.28mm2 0.57µW light tolerant wireless neural recording IC.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169712/1/jongyup_1.pd