Metaplastic and Energy-Efficient Biocompatible Graphene Artificial Synaptic Transistors for Enhanced Accuracy Neuromorphic Computing

TL;DR

This paper introduces biocompatible graphene-based artificial synaptic transistors with ultra-low energy consumption, mimicking biological synapses and demonstrating superior performance in neural network tasks, suitable for energy-efficient neuromorphic computing.

Contribution

The work presents a novel bilayer graphene-based synaptic device with metaplasticity and ultra-low energy switching, advancing biocompatible neuromorphic hardware.

Findings

Achieved ~50 aJ/m^2 switching energy efficiency.

Demonstrated metaplasticity in artificial synapses.

Outperformed linear synapses in image classification.

Abstract

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/m^2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.