Category: Electrical Engineering

Reza Farsad Asadi: Miniaturized Vacuum Tubes

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Co-authors: Tao Zheng, Jaime da Silva

https://youtu.be/tqZZzpv1__M

A Nanoscale Vacuum-Channel Transistor (NVCT) is a transistor based on field emission that the electron channel is a vacuum. These transistors are nanoscale versions of vacuum tubes. Because the channel is a vacuum, they can potentially be faster than traditional solid state transistors. Also, they can be expected to be less susceptible to harsh environments compare to the solid state transistors. However, they have shown instability over time and they require high vacuum to operate. This project on investigating the lifetime and instability of field emitters over time.

Reza Farsad Asadi
Program: PhD in Electrical Engineering
Faculty mentor: Bruce Gnade

Han Gao: Study of Radiation Tolerance of GaN-based Devices

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https://youtu.be/YAHU2-4jnmA

There is increasing interest in the radiation resistance of wide-bandgap semiconductor devices, both for power transistors and RF applications. Ga-based devices are especially interesting because they have shown high resistance to many different types of radiation and to many different doses. In general, the magnitude of the damage, as evidenced by the drain-to-source leakage depends on the linear energy transfer (LET) and drain-source voltage (𝑉_𝐷𝑆). In this project we are studying the effects of radiation on GaN-based devices, both experimentally and theoretically, to improve the understanding of the operation of such devices in different radiation environments.

Han Gao
Program: PhD in Electrical Engineering
Faculty mentor: Bruce Gnade

Jiapeng Liu: Solve inverse problem with physics assisted Deep Learning

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https://youtu.be/xDllNa3txfg

Deep learning has been widely used for solving ill-posed inverse problem in the past few years. With sufficient training data, data-driven deep neural networks can serve as good approximation of mapping between input and solution. However, training set can be expensive to obtain, and consequentially, the network may not generalize well. Starting with the Deep Image Prior by Ulyanov et al, convolutional neural networks are proven to be strong prior that can solve inverse problems without training data. Recent works have explored the notion of using dataset to supervise the training process, physics priors can be added to the network architecture for even stronger constraints, and the input will be self-supervising the network. By iteratively updating the weights of the network the output generated by the network is forced to satisfy the physical model and that converges to the correct solution.

Jiapeng Liu
Program: PhD in Electrical Engineering
Faculty mentor: Prasanna Rangarajan

Cameron Matson: Benefits of MIMO in 3D UAV-to-UAV topologies

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Winner: Electrical Engineering (Graduate)

Co-authors: Syed Muhammad Hashir, Sicheng Song

https://youtu.be/Pf7At2Bz_AU

Unmanned Aerial Vehicles (UAVs) often lack the size, weight, and power to support large antenna arrays or a large number of radio chains. Despite such limitations, emerging applications that require the use of swarms, where UAVs form a pattern and coordinate towards a common goal, must have the capability to transmit in any direction in 3D space from moment to moment. In this work, we design a measurement study to evaluate the role of antenna polarization diversity on single-antenna and multi-antenna UAV systems communicating in arbitrary 3D space. To do so, we construct flight patterns where one transmitting UAV is hovering at a high altitude (80 m) to focus on the impact of heterogeneous drone-based antenna polarization without multipath effects. Then, a receiving UAV hovers at 114 different positions that span an ellipsoid surrounding the transmitting UAV with a radius of approximately 20 m along equally-spaced elevation and azimuth angles. To understand the role of diverse antenna polarizations and multiple antennas, both UAVs have a dedicated radio chain to a horizontally-mounted antenna and a dedicated radio chain to a vertically-mounted antenna, creating four wireless channels. With this measurement campaign, we seek to understand how single-antenna systems could optimally select an antenna orientation or multi-antenna systems could have the greatest gains.

Cameron Matson
Program: Master’s in Electrical Engineering
Faculty mentor: Joe Camp

Menglin Wang: Advanced Capacitors for Future Power Conversion Systems

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https://youtu.be/11AOmLE9dpo

Capacitors are critical for voltage source converter functionality. DC-link capacitors are known to have reliability issues. Therefore, the overall goal of this program is to improve the understanding of high voltage breakdown in high dielectric constant inorganic ceramic capacitors. This year the focus has been on improving the understanding of resistance degradation in BaTiO3 at elevated temperature, using single crystal BaTiO3 as a model system to understand the impact and control of oxygen vacancy migration to maximize the long-term reliability of high voltage inorganic multi-layer ceramic capacitors.

Menglin Wang
Program: PhD in Electrical Engineering
Faculty mentor: Bruce Gnade