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System Bits: Feb. 26

Firefly LEDs; Rice PUFs; machine spying.

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Firefly microstructures in LED light bulbs
Pennsylvania State University researchers wanted to improve the energy efficiency of commercial light-emitting diode light bulbs to save even more energy. They found the answer in the lantern surface of fireflies.

“LED lightbulbs play a key role in clean energy,” said Stuart (Shizhuo) Yin, professor of electrical engineering at Penn State. “Overall commercial LED efficiency is currently only about 50%. One of the major concerns is how to improve the so-called light extraction efficiency of the LEDs. Our research focuses on how to get light out of the LED.”

One way to get more light out of LEDs without using more power is to create symmetrical microstructures on the surface of the LED. The research team looked at fireflies and how they maximize their release of light. The microstructures on their lanterns are asymmetrical, even lopsided, the team noted.

“Later I noticed not only do fireflies have these asymmetric microstructures on their lanterns, but a kind of glowing cockroach was also reported to have similar structures on their glowing spots,” said Chang-Jiang Chen, doctoral student in electrical engineering and lead author in the study. “This is where I tried to go a little deeper into the study of light extraction efficiency using asymmetric structures.”

The team crafted asymmetrical pyramids for their microstructured LED surfaces, which improved the light extraction efficiency to around 90%.

PUF goes IoT device security
Physically unclonable function technology isn’t new – it’s implemented by Intrinsic ID and other companies – but there are different ways to use PUF for greater device security, especially in the Internet of Things.

Two Rice University researchers last week presented a paper on their PUF technology at the International Solid-State Circuits Conference in San Francisco. Kaiyuan Yang and Dai Li of Rice’s VLSI Lab described their “zero-overhead” method for generating two unique fingerprints for each device with PUF tech. Their process is said to use the same PUF components to make both keys and doesn’t require additional area and latency. Their PUF tech is claimed to be about 15 times more energy-efficient than previously published versions.

“Basically, each PUF unit can work in two modes,” says Yang, assistant professor of electrical and computer engineering. “In the first mode, it creates one fingerprint, and in the other mode it gives a second fingerprint. Each one is a unique identifier, and dual keys are much better for reliability. On the off chance the device fails in the first mode, it can use the second key. The probability that it will fail in both modes is extremely small.”

PUF fingerprints, he notes, are as useful in authentication as human fingerprints.


Dai Li (left) and Kaiyuan Yang of Rice University’s VLSI Lab presented their new security technology at the 2019 International Solid-State Circuits Conference (ISSCC). (Photo credit: Jeff Fitlow, Rice University)

“First, they are unique,” Yang says. “You don’t have to worry about two people having the same fingerprint. Second, they are bonded to the individual. You cannot change your fingerprint or copy it to someone else’s finger. And finally, a fingerprint is unclonable. There’s no way to create a new person who has the same fingerprint as someone else.”

Here’s where PUF is useful for connected lightbulbs and other IoT devices.

“The general concept for IoT is to connect physical objects to the internet in order to integrate the physical and cyber worlds,” Yang says. “In most consumer IoT today, the concept isn’t fully realized because many of the devices are powered and almost all use existing IC feature sets that were developed for the mobile market.”

Ghosts in the machine?
The sounds that a laboratory instrument makes can provide a strong clue to what the instrument was doing by recording such sounds. At the University of California, Riverside, they are keenly aware of such snooping.

“Any active machine emits a trace of some form: physical residue, electromagnetic radiation, acoustic noise, etc. The amount of information in these traces is immense, and we have only hit the tip of the iceberg in terms of what we can learn and reverse engineer about the machine that generated them,” says Philip Brisk, a UC Riverside associate professor of computer science who worked on the project.

The research team recorded sounds from a DNA synthesizer as it was going through its manufacturing routine. The machine contains components that open and close to release chemicals as it manufactures the DNA bases. The tubes and chambers of the DNA synthesizer produce distinctive sounds in the process. It was difficult, however, for the scientists to hear the differences in those sounds.

“But through a careful feature engineering and bespoke machine-learning algorithm written in our lab, we were able to pinpoint those differences,” says Sina Faezi, a doctoral student.

“A few years ago, we published a study on a similar method for stealing plans of objects being fabricated in 3D printers, but this DNA synthesizer attack is potentially much more serious,” says Mohammad Abdullah Al Faruque, an electrical and computer engineering professor at the University of California, Irvine.

“The take-home message for bioengineers is that we have to worry about these security issues when we’re designing instruments,” William Grover, a bioengineering professor at UC Riverside says.



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