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

Introduction

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

Current Challenges

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

Proposed Solution

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.

Project Objectives

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.

Key Features of 2D-CTM Devices

1. Extract features from EPSs at extremely low power.
2. Achieve high accuracy of classification.
3. Provide efficient biomarkers for medical diagnosis and BCIs.

Application and Goals

The project will develop the RC system based on the 2D-CTM technology for a broad application space. The goal is to establish a novel technology platform for scalable, low-power implantable/wearable chips for real-time EPS monitoring and classification.