High aspect ratio microchannels using high thermal conductivity materials such as silicon carbide (SiC) have recently been explored to locally cool micro-scale power electronics that are prone to on-chip hot spot generation. Analytical and finite element modeling shows that SiC-based microchannels used for localized cooling should have high aspect ratio features (above 8:1) to obtain heat transfer coefficients (300 to 600 kW/m2·K) required to obtain gallium nitride (GaN) device channel temperatures below 100°C. This work presents experimental results of microfabricating high aspect ratio microchannels in a 4H-SiC substrate using inductively coupled plasma (ICP) etching. Depths of 90 μm and 80 μm were achieved with a 5:1 and 12:1 aspect ratio, respectively. This microfabrication process will enable the integration of microchannels (backside features) with high-power density devices such as GaN-on-SiC based electronics, as well as other SiC-based microfluidic applications.

Radiation-hardened electronics used in space, nuclear energy and radiation medicine applications require robust dielectric materials to be used as passivation layers and gate insulators. Thus, there is a need to understand the response of these materials under radiation exposure (e.g., gamma, neutron and proton) to develop radiation-tolerant and reliable electronic systems. In addition, as the size of transistors continues to scale down there is a need to have physically thicker dielectric layers with similar capacitance values to ultra-thin SiO2. High permittivity (high-k) dielectrics lend themselves well to this task as they have capacitance values similar to ultra-thin SiO2 while not facing issues of high leakage current and power dissipation as ultra-thin SiO2. Atomic layer deposition (ALD) of thin films has gained interest in the development of radiation-hardened electronics as this process results in high quality (continuous and pinhole-free) and conformal gate dielectric thin films with precise thickness control to the angstrom level. Here, we examine the impact of gammairradiation on plasma-enhanced ALD dielectric layers using metal-oxide semiconductor (MOS) capacitors. In this work, three ALD gate dielectric films: Al2O3, HfO2 and SiO2 (between 22 and 24 nm thick) are utilized. The capacitance-voltage (C-V) response of plasma-enhanced ALD-based MOS capacitors upon gamma irradiation (Co-60) up to 533 krad without any shielding is observed. It is shown that ALD grown HfO2 films are resistant to gamma irradiation based on the negligible shift in flat band voltage and hysteresis characteristics. Additionally, ALD grown Al2O3 films exhibited minimal generation of mobile traps but generation of trapped charges was observed. Furthermore, the flat band and hysteresis of ALD grown SiO2 films showed development of both trapped and mobile charges which may suggest that this material lends itself to radiation dosimetry applications. These initial findings support the use of plasma-enhanced ALD grown films in the development of radiation-hardened electronics and sensors.

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The development of radiation-hardened, temperature-tolerant materials, sensors and electronics will enable lightweight space sub-systems (reduced packaging requirements) with increased operation lifetimes in extreme harsh environments such as those encountered during space exploration. Gallium nitride (GaN) is a ceramic, semiconductor material stable within high-radiation, high-temperature and chemically corrosive environments due to its wide bandgap (3.4 eV). These material properties can be leveraged for ultraviolet (UV) wavelength photodetection. In this paper, current results of GaN metal-semiconductor-metal (MSM) UV photodetectors behavior after irradiation up to 50 krad and temperatures of 15°C to 150°C is presented. These initial results indicate that GaN-based sensors can provide robust operation within extreme harsh environments. Future directions for GaN-based photodetector technology for down-hole, automotive and space exploration applications are also discussed.

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Autonomous driving is a challenging problem where there is currently an intense focus on research and development. Human drivers are forced to make thousands of complex decisions in a short amount of time,quickly processing their surroundings and moving factors. One of these aspects, recognizing regions on the road that are driveable is vital to the success of any autonomous system. This problem can be addressed with deep learning framed as a region proposal problem. Utilizing a Mask R-CNN trained on the Berkeley Deep Drive (BDD100k) dataset, we aim to see if recognizing driveable areas, while also differentiating between the car's direct (current) lane and alternative lanes is feasible.