Controlled Ionic Tunneling in Lithium Nanoionic Synaptic Transistor through Atomically Thin Graphene Layer for Neuromorphic Computing

Abstract Lithium nanoionic transistors have recently emerged as promising artificial synaptic devices for neuromorphic hardware systems. However, mimicking the essential synaptic functionalities including nonvolatile conductance modulation with a near‐linear analog weight update has been a crucial milestone in those synaptic devices and has a direct impact on pattern recognition accuracy. The volatile channel conductance change due to the instability of the solid electrolyte interface and lithium‐ion nucleation at electrolyte‐channel interface are two key phenomena responsible for the nonlinear switching in lithium nanoionics transistor. Graphene is proposed as an atomically thin ionic tunneling layer to establish nonvolatile analog multilevel conduction in lithium nanoionic transistor. The combined effects of controlled ionic tunneling through graphene and stable solid electrolyte interface result in the device exhibiting nearly linear conductance switching with distinct gate‐controllable nonvolatile multilevel conduction states and a smallest asymmetric ratio of 0.26 and highest on/off ratio of 28. A neural network simulation result obtained from the graphene layer device indicates high handwritten digit recognition accuracy. These results demonstrate the potential application of atomically thin two‐dimensional (2D) materials as an ionic tunneling layer in nanoionics synaptic transistors and may facilitate the development of a neuromorphic computing system with high performance.