Floating-Gate Synaptic Transistors for Energy-Efficient Neuromorphic Computing.

Abstract

By integrating nonvolatile memory and processing, floating-gate synaptic transistors (FGSTs) have emerged as a pivotal platform for energy-efficient neuromorphic computing, overcoming limitations inherent in conventional Von Neumann architectures. These devices utilize a unique floating-gate layer to facilitate charge storage and manipulation. This review presents a comprehensive overview of recent advancements in FGST device design, focusing on innovative floating-gate structures, diverse floating-gate material systems, and advanced tunneling dielectric layers. These innovations have significantly enhanced synaptic performance, including near-linear conductance modulation, ultralow energy consumption, multilevel storage, extended retention times, and robust endurance characteristics. Consequently, FGSTs achieve remarkable pattern-recognition accuracy and effectively mimic complex biological plasticity rules. Moreover, their integration into neuromorphic sensory systems for vision, audition, touch, and neuronal behavior enables these devices to conduct high-fidelity real-time multimodal and reconfigurable processing. Despite these advancements, challenges persist in scaling synaptic energy to femtojoule levels, enhancing the mechanical flexibility of wearable electronics, improving operational stability, and developing large-scale synaptic devices array. This paper outlines strategic pathways in materials and architecture to steer the development of FGSTs toward highly efficient, brain-inspired neuromorphic hardware.