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A machine learning processing engine to build flexible devices with odor recognition capabilities

In recent years, there has been an increase in the development of flexible electronics: electronic components that can be stretched and thus enable the development of smart watches, fitness trackers, or other wearable smart devices. Flexible electronics are typically made by applying electronic circuits on flexible material substrates, such as plastic or paper.

Flexible chips can be used to fabricate devices that are low-cost, as well as extremely thin, bendable, and comfortable to wear. While their characteristics could make them more suitable than conventional electronics fabricated on silicon wafers for certain applications, so far not all these chips have achieved desirable performances.

Researchers at Arm and PragmatIC have recently used low-cost flexible chips to fabricate a machine learning (ML) processing engine, which could be used to build a wide range of smart devices with advanced data processing capabilities. Their paper, published in Nature Electronics, specifically demonstrates the use of their engine for applications that involve recognizing smells or odors.

"Arm Research has a close R&D collaboration with PragmatIC, which has low-cost flexible IC fabrication technology based on metal-oxide thin-film transistors (TFTs)," Emre Ozer, one of the researchers who carried out the study, told TechXplore. "This technology has great potential for the the fabrication of processing engines on low-cost flexible substrates, which could enable billions of objects to become 'smarter' while costing in the range of cents rather than dollars."

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