Artificial Synapse with Tunable Dynamic Range for Neuromorphic Computing with Ion Intercalated Bilayer Graphene

In neuromorphic computing, a tunable dynamic range in artificial synapses is crucial, as it allows devices to emulate the human brain's efficiency in processing complex information with analog programmable states. Here, we introduce an electrochemical random-access memory (ECRAM) based on bilayer graphene. Our device achieves a large and programmable dynamic range through lithium-ion (Li+) intercalation, pulse modulation, and geometric engineering. We systematically investigated how pulse parameters, including amplitude, duty cycle, frequency, and signal type, affect conductance and dynamic range. Our results demonstrate that higher pulse amplitudes and/or longer duty cycles enhance Li+ intercalation efficiency; while lower frequencies pulse trains facilitate ion intercalation, significantly influencing conductance and dynamic range. Additionally, we explored how geometric factors such as channel length in bilayer and multi-layer graphene and the introduction of hole structures may affect intercalation kinetics and subsequently device performance. First-principles density functional theory (DFT) calculations were also performed to support that the interlayer space in bilayer graphene is energetically favorable for Li transport, and that hole structures promote efficient Li intercalation by providing barrierless pathways through the exposed edges of the bilayer. These findings confirm bilayer graphene as a promising material for developing high-performance artificial synapses with tunable characteristics.