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Abstract:
The world’s computing needs have grown exponentially in recent years due to the technology boom. To meet the needs of the next technological leap, the scientific community is working to enhance current processing capabilities and simultaneously develop entirely new computational methods.

Current and future computing is getting a boost from new research

Austin, Texas | Posted on July 21, 2023

Two new papers from the research group of Jean Anne Incorvia, a professor at the Chandra Family of Electrical and Computer Engineering Cockrell School of Engineering, aim to contribute to both of these scientific needs. Together, they offer improvements to today’s semiconductor technology as well as more agile building blocks for the next generation of computers that think like the human brain.

“We are on the precipice of a new class of computers, and recreating how our brains think is an extraordinary research,” said Incorvia. “At the same time, the computing techniques we use today are not going anywhere, so it is important to continue to improve and innovate on the devices that support our current technologies.”

Logic Problems

New research published in ACS Nano deals with transistors and circuits. Inside the chip are components called logic gates that interpret digital signals.

These logic gates are transistors that can generally conduct electrons or holes – which occurs when electrons move inside atoms – but not both. In this paper, the researchers connected logic gates together that can conduct electrons and holes.

They showed that this achievement reduced the number of transistors needed in a circuit. And that means more transistors can be squeezed into the same space, making them more efficient and powerful, or the space saved can be used to shrink devices. They also demonstrated a new circuit that specifically exploits transistor behavior.

Transistors are made of an ultra-thin two-dimensional material, which has a natural “ambi-polar” property that allows it to conduct holes and electrons. However, they don’t do very well on their own. Perfecting that capability is a key component of this paper, and through device engineering they demonstrated the essential XOR, NOR, and NAND circuitry without the need for any other devices than ambi-polar transistors. These circuits are the basic building blocks of larger circuits.

“When we think about the future of computing, if we can take advantage of this natural behavior of 2D materials and scale them, we can cut in half the number of transistors we need in our circuits,” said Incorvia.

The researchers demonstrated this capability at a fairly large device size. Next steps include shrinking the device and further reducing the power consumption required to make it a commercially viable chip component.

Noisy Neurons

These findings apply to current computing technology. A second paper published recently in Applied Physics Letters looks at the next generation of computers, which think more like the human brain.

These neuromorphic devices are better than traditional computers at AI tasks such as interpreting images and processing language. In this new paper, researchers created a new type of artificial neuron – the one in the human brain responsible for transmitting information between brain cells – using a magnetic material.

Artificial neurons represent a popular area of ​​computational neuromorphic research. What makes this set of neurons stand out is the chaotic nature of their reactions to electrical pulses.

They outperform other artificial neurons as part of a neural network in interpreting images, especially when the data to be interpreted is noisy. The device fared better than any other artificial neuron at identifying images of blurry shoes, and the gap widened as the image became blurrier.

“Because the device itself is stochastic in response to data input, it performs better at handling noisy datasets,” said Incorvia.

These neurons could impact “edge computing” deployments, where devices need to be smaller, use less power, and be further away from central computing resources such as cloud servers. They are also resistant to radiation.

One of the early applications of this technology could come in outer space, where silicon chips struggle to survive high levels of radiation. The ability to handle radiation as well as messy data could make these neurons ideal for future space-based technologies.

Incorvia worked with fellow electrical and computer engineering faculty members Deji Akinwande and Joseph Friedman, professor of electrical and computer engineering at The University of Texas at Dallas on the ACS Nano research, which was funded by a grant from the National Science Foundation and the US Air Force Research Laboratory. Other project team members include: Sungai Xintong Li and Ethan from the Chandra Family Department of Electrical and Computer Engineering; Peng Zhou and Xuan Hu from the UT Dallas Department of Electrical and Computer Engineering; and Kenji Watanable and Takashi Taniguchi of the Center for Research in Electronic and Optical Materials at the National Institute of Materials Science in Japan.

Applied Physics Letters research is funded by a grant from the National Science Foundation. Incorvia’s team on the project included Thomas Leonard, Samuel Liu, and Harrison Jin, all from electrical and computer engineering.

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Nat Retribution
University of Texas at Austin

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