IGZO-based floating-gate synaptic transistors for neuromorphic computing

Abstract

Neuromorphic computing, that is expected to be implemented with emerging synaptic devices, may overcome the inherent efficiency bottleneck in conventional digital computers. In this article, low-temperature processed indium-gallium-zinc-oxide (FGST) based floating-gate synaptic transistors with Al2O3/ITO/Al2O3 gate dielectric stacks are proposed for neuromorphic computing. The synaptic weight is stored as the channel conductance (G) of the floating-gate synaptic transistor (FGST). Negative/positive electrical pulse stimuli are applied on the bottom gate electrode of the synaptic transistor to facilitate/depress the synaptic weight, respectively. The operation mechanism of such neuromorphic devices is discussed based on the electron direct tunneling. Furthermore, the retention of the synaptic weight and the device endurance characteristics of the synaptic transistors are also investigated. In addition, in simulations with the measured device properties, an artificial neural network consisting of such floating-gate synaptic transistors shows a high classification accuracy of 95.7% for small digits dataset. These results may provide insight into potential applications of the floating-gate oxide-based synaptic transistors for artificial neural networks.