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

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