Communications of the ACM
Researchers Boost the Building Blocks of Computing
"If we can harness the natural behavior of . . . 2D materials and scale them, we could cut the number of transistors we need in our circuits in half," says Assistant Professor Jean Anne Incorvia.
Credit: University of Texas at Austin
Published research from the group of Jean Anne Incorvia, assistant professor at the University of Texas at Austin, describes improvements on current semiconductor technology and a nimbler building block for neural networks suitable for neuromorphic computing on the edge.
"We are on the precipice of a new class of computers, and recreating how our brains think is a tremendous research undertaking," says Incorvia. "At the same time, computing techniques we use today aren't going anywhere, so it's important to continue to improve and innovate on the devices that power our current technology."
Research published in ACS Nano describes the fabrication of high-performance ambipolar dual-gate transistors based on tungsten diselenide. The researchers linked logic gates together that could conduct both electrons and holes. They show that this achievement reduces the number of transistors needed in a circuit.
In a second paper, published in Applied Physics Letters, researchers describe their creation of an artificial neuron using magnetic materials. The neuron devices outperformed other artificial neurons as part of neural networks in interpreting images, specifically when the data to be interpreted was noisy. The neurons could be impactful for "edge computing" uses.
From University of Texas at Austin
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