Nanoelectronic device performs real-time AI classification
Summary
With its tiny footprint, ultra-low power consumption and lack of lag time to receive analyses, the device is ideal for direct incorporation into wearable electronics (like smart watches and fitness trackers) for real-time data processing and near-instant diagnostics. “Today, most sensors collect data and then send it to the Cloud, where the analysis occurs on energy-hungry servers before the results are finally sent back to the user,” said Northwestern’s Mark C. Hersam, the study’s senior author. For current silicon-based technologies to categorise data from large sets like ECGs, it takes more than 100 transistors – each requiring its own energy to run. While traditional technologies use silicon, the researchers constructed the miniaturised transistors from two-dimensional molybdenum disulfide and one-dimensional carbon nanotubes. “The integration of two disparate materials into one device allows us to strongly modulate the current flow with applied voltages, enabling dynamic reconfigurability,” Hersam said.