Neuromorphic computing system for real-time signal monitoring and classification with ultra-low-power 2D devices

The detection and classification of electrophysiological signals (EPSs), such as electroencephalography (EEG) and electromyography (EMG) recordings, are the gold standard in neuroscience, enabling the identification of digital biomarkers capable of health monitoring, personalised medicine and advanced brain-computer interfaces (BCIs).

The state-of-the-art technology in this field, however, still relies on bulky, inefficient microelectronic systems which relies on artificial intelligence (AI) in the cloud.

The energy efficiency and classification accuracy can be largely improved by neuromorphic computing with emerging materials and devices capable of mimicking the neural mechanisms in our brain.

This project aims at developing a novel class of neuromorphic systems based on reservoir computing (RC) in charge trap memory (CTM) based on 2D semiconductors. 2D-CTM devices are able to extracted features from EPSs at extremely low power and high accuracy of classification, thus providing efficient biomarkers for medical diagnosis and BCIs.

The project will develop the RC system based on the 2D-CTM technology for a broad application space, with the goal of establishing a novel technology platform for scalable, lowpower implantable/wearable chips for real-time EPS monitoring and classification.