Self-Powered Neuromorphic Systems Based on Tribotronics Synaptic Devices

Artificial synaptic devices based on field-effect transistors (FETs) are essential building blocks for neuromorphic computing systems that emulate the signal processing and learning capabilities of biological neural networks. However, most FET-based synaptic devices depend on external power sources, which increases energy consumption and limits their applications in self-powered systems. Triboelectric nanogenerators (TENGs) have recently emerged as promising candidates for self-powered neuromorphic systems that simultaneously harvest ambient mechanical energy and modulate synaptic signals, thereby enabling energy-efficient neuromorphic operation. This review summarizes recent advances in TENG-driven three-terminal artificial synaptic devices, focusing on their device architectures, operating principles, and synaptic functionalities. It discusses the unique electrical characteristics of TENG outputs, including high internal impedance and pulsed voltage signals, and their correlation with synaptic behaviors such as excitatory and inhibitory postsynaptic currents, nonvolatile weight retention, and frequency- and time-dependent plasticity. In addition, it explores the emerging applications of these devices in tactile sensing, human-machine interaction, auditory perception, and multimodal neuromorphic systems. Finally, the review highlights the current challenges, including signal stability, impedance matching, multisensory integration, and power consumption, and discusses the potential strategies to address these limitations, along with the future perspectives to advance the development of next-generation self-powered neuromorphic computing systems.