Harnessing the power of gallium nitride and machine learning

$2.5 million project will develop compact power conversion systems

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Credit: University of Houston

Military installations, especially on ships and aircraft, require robust power electronics systems to operate radar and other equipment, but there is limited space onboard. Researchers from the University of Houston will use a $2.5 million grant from the U.S. Department of Defense to develop compact electronic power systems to address the issue.

Harish Krishnamoorthy, assistant professor of electrical and computer engineering and principal investigator for the project, said he will focus on developing power converters using gallium nitride (GaN) devices, capable of quickly storing and discharging energy to operate the radar systems.

He is working with co-PI Kaushik Rajashekara, professor of electrical and computer engineering, and Tagore Technology, a semiconductor company based in Arlington Heights, Ill. The work has potential commercial applications, in addition to military use, he said.

Currently, radar systems require large capacitors, which store energy and provide bursts of power to operate the systems. The electrolytic capacitors also have relatively short lifespans, Krishnamoorthy said.

GaN technology offers the promise of more efficient and compact power conversion than silicon-based technology. That’s because they are wide bandgap semiconductors. GaN devices can be turned on and off far more quickly – over 10 times as quickly as silicon devices, Krishnamoorthy said. The resulting higher operating frequency allows passive components in the circuit – including capacitors and inductors – to be designed at much smaller dimensions.

But there are still drawbacks to GaN devices. Noise – electromagnetic interference, or EMI – can affect the precision of radar systems, since the devices work at such high speeds. Part of Krishnamoorthy’s project involves designing a system where converters can contain the noise, allowing the radar system to operate unimpeded.

He also will use machine learning to predict the lifespan of GaN devices, as well as of circuits employing these devices. The use of GaN technology in power applications is relatively new, and assessing how long they will continue to operate in a circuit remains a challenge.

“We don’t know how long these GaN devices will last in practical applications, because they’ve only been used for a few years,” Krishnamoorthy said. “That’s a concern for industry.”

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Original Source

https://uh.edu/news-events/stories/2020/april-2020/04272020krishnamoorthy-hiper.php

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