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    Applications of Additive Manufacturing Technologies to Ambient Energy Harvesters for Microwave and Millimeter-Wave Autonomous Wireless Sensing Networks and 3D Packaging Integration

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    LIN-DISSERTATION-2020.pdf (29.00Mb)
    Date
    2020-12-02
    Author
    Lin, Tong-Hong
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    Abstract
    The objectives of my researches are developing new RF and mm-wave energy harvester topologies and realizing them with new additive manufacturing fabrication processes. The proposed energy harvester topologies are utilized to achieve energy-autonomous wireless sensing networks for 5G communication and IoT solutions. The developed additive manufacturing fabrication process is adopted to realize not only energy harvesters but also mm-wave IC packaging process. Ambient energy harvesting techniques collect ambient energy such as solar, RF, heat, and vibration and convert them into DC power sources to support the energy requirement of electronics. Since the energy is provided autonomously and constantly, maintenance or replacement for the batteries inside wireless electronics is not necessary resulting in enormous cost reduction. The researches of energy harvester focus on three categories, new topologies to enhance the performances, increased harvested power levels, and applied energy harvester to find new killer applications. This work proposes new designs and improvements in all three categories. Various proof-of-concept backscattered sensing systems with integrated RF energy harvesters for 5G and IoT applications are demonstrated. In this research, high-efficiency and broadband rectifiers are proposed to support high-performance rectifications as well as increase harvested energy. New topologies to utilize both DC and harmonics are demonstrated to increase the reading range of on-body wireless sensing networks. Furthermore, energy-autonomous microfluidic sensing systems are demonstrated to unleash the potential of microfluidic applications. 5G energy harvester is proposed and integrated inside the multi-layered additive manufacturing IC packages to achieve fully-functional SiP modules. While determining the fabrication methods, low-cost, fast-prototyping, and scalable methods with great material and structural flexibilities are preferable, and thus, additive manufacturing technologies including inkjet printing, 3D printing, and glass semi-additive patterning process are adopted. This research utilizes inkjet-printed masks, substrates, and metal traces to simplify the conventional fabrication process. The new low-loss inkjet-printable ink is developed to push the additive manufacturing technologies to mm-wave ranges. The flexible 3D-printed materials are characterized and used for wearable sensor designs, microfluidic channels, and flexible packaging topologies. The 3D features are included inside the IC packages to achieve high-performance multi-layer packaging structures with shorter lengths, lower loss, and smaller parasitics. The high-precision glass semi-additive patterning process is used to realized AiP and SiP designs with great performances. Furthermore, through combining inkjet and 3D printing, this work proposes a fast, cost-effective, scalable, and environmentally-friendly fabrication process for various high-performance and compact antenna designs, microwave/mm-wave components, microfluidic channels, RF energy harvesters, and SiP designs. In summary, this work utilizes additive manufacturing processes to realize various innovative topologies of energy harvesters to harvest more power and achieve higher rectification efficiency with smaller sizes. Additive manufacturing processes and energy harvesting techniques are also used to demonstrate new applications including the first on-body long-range sensing network, the first energy-autonomous long-range microfluidic sensing system, and the first fully-functional energy-autonomous 5G SiP module design. The proposed topologies are suitable for smart cities, smart skin, and IoT applications.
    URI
    http://hdl.handle.net/1853/66015
    Collections
    • Georgia Tech Theses and Dissertations [23877]
    • School of Electrical and Computer Engineering Theses and Dissertations [3381]

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