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- Creators: Arizona State University
Description
Power supply management is important for MEMS (Micro-Electro-Mechanical-Systems) bio-sensing and chemical sensing applications. The dissertation focuses on discussion of accessibility to different power sources and supply tuning in sensing applications. First, the dissertation presents a high efficiency DC-DC converter for a miniaturized Microbial Fuel Cell (MFC). The miniaturized MFC produces up to approximately 10µW with an output voltage of 0.4-0.7V. Such a low voltage, which is also load dependent, prevents the MFC to directly drive low power electronics. A PFM (Pulse Frequency Modulation) type DC-DC converter in DCM (Discontinuous Conduction Mode) is developed to address the challenges and provides a load independent output voltage with high conversion efficiency. The DC-DC converter, implemented in UMC 0.18µm technology, has been thoroughly characterized, coupled with the MFC. At 0.9V output, the converter has a peak efficiency of 85% with 9µW load, highest efficiency over prior publication. Energy could be harvested wirelessly and often has profound impacts on system performance. The dissertation reports a side-by-side comparison of two wireless and passive sensing systems: inductive and electromagnetic (EM) couplings for an application of in-situ and real-time monitoring of wafer cleanliness in semiconductor facilities. The wireless system, containing the MEMS sensor works with battery-free operations. Two wireless systems based on inductive and EM couplings have been implemented. The working distance of the inductive coupling system is limited by signal-to-noise-ratio (SNR) while that of the EM coupling is limited by the coupled power. The implemented on-wafer transponders achieve a working distance of 6 cm and 25 cm with a concentration resolution of less than 2% (4 ppb for a 200 ppb solution) for inductive and EM couplings, respectively. Finally, the supply tuning is presented in bio-sensing application to mitigate temperature sensitivity. The FBAR (film bulk acoustic resonator) based oscillator is an attractive method in label-free sensing application. Molecular interactions on FBAR surface induce mass change, which results in resonant frequency shift of FBAR. While FBAR has a high-Q to be sensitive to the molecular interactions, FBAR has finite temperature sensitivity. A temperature compensation technique is presented that improves the temperature coefficient of a 1.625 GHz FBAR-based oscillator from -118 ppm/K to less than 1 ppm/K by tuning the supply voltage of the oscillator. The tuning technique adds no additional component and has a large frequency tunability of -4305 ppm/V.
ContributorsZhang, Xu (Author) / Chae, Junseok (Thesis advisor) / Kiaei, Sayfe (Committee member) / Bakkaloglu, Bertan (Committee member) / Kozicki, Michael (Committee member) / Phillips, Stephen (Committee member) / Arizona State University (Publisher)
Created2012
Description
Millimeter-wave (mmWave) and sub-terahertz (sub-THz) systems aim to utilize the large bandwidth available at these frequencies. This has the potential to enable several future applications that require high data rates, such as autonomous vehicles and digital twins. These systems, however, have several challenges that need to be addressed to realize their gains in practice. First, they need to deploy large antenna arrays and use narrow beams to guarantee sufficient receive power. Adjusting the narrow beams of the large antenna arrays incurs massive beam training overhead. Second, the sensitivity to blockages is a key challenge for mmWave and THz networks. Since these networks mainly rely on line-of-sight (LOS) links, sudden link blockages highly threaten the reliability of the networks. Further, when the LOS link is blocked, the network typically needs to hand off the user to another LOS basestation, which may incur critical time latency, especially if a search over a large codebook of narrow beams is needed. A promising way to tackle both these challenges lies in leveraging additional side information such as visual, LiDAR, radar, and position data. These sensors provide rich information about the wireless environment, which can be utilized for fast beam and blockage prediction. This dissertation presents a machine-learning framework for sensing-aided beam and blockage prediction. In particular, for beam prediction, this work proposes to utilize visual and positional data to predict the optimal beam indices. For the first time, this work investigates the sensing-aided beam prediction task in a real-world vehicle-to-infrastructure and drone communication scenario. Similarly, for blockage prediction, this dissertation proposes a multi-modal wireless communication solution that utilizes bimodal machine learning to perform proactive blockage prediction and user hand-off. Evaluations on both real-world and synthetic datasets illustrate the promising performance of the proposed solutions and highlight their potential for next-generation communication and sensing systems.
ContributorsCharan, Gouranga (Author) / Alkhateeb, Ahmed (Thesis advisor) / Chakrabarti, Chaitali (Committee member) / Turaga, Pavan (Committee member) / Michelusi, Nicolò (Committee member) / Arizona State University (Publisher)
Created2024