Triboelectric-memristive coupling for self-powered neuromorphic computing: mechanisms, devices, and systems

Coupling triboelectric nanogenerators (TENGs) with memristors offers a direct route to merge energy harvesting and adaptive learning within the same physical substrate, enabling self-powered neuromorphic systems driven by ubiquitous mechanical stimuli. Unlike conventional electronics that rely on external power rails, triboelectric-memristive hybrids transduce mechanical excitations into programmable resistive states, thereby supporting synaptic functions such as short-term plasticity (STP), long-term potentiation (LTP), and spike-timing-dependent plasticity (STDP). This review consolidates the physical mechanisms of triboelectric-memristive coupling and clarifies how charge transfer, interfacial electron-ion processes, and device-level state dynamics jointly realize energy-to-information transduction for signal processing and learning. Beyond prior surveys that focus on TENGs or memristors in isolation, we establish a unified transduction map that links mechanical stimulus statistics to TENG waveform characteristics and further to memristive state-variable updates, and we use this map as the organizing framework throughout the paper. We then provide (i) a mechanism-guided taxonomy of representative device architectures and their achievable plasticity modes, and (ii) a system-level perspective on integrating self-powered sensing, in-memory learning, and multimodal fusion. Finally, we summarize key challenges - including charge stability, humidity tolerance, device variability, and scalable integration - and discuss emerging directions such as large-area triboelectric materials for array uniformity, multiphysics co-learning for richer in-sensor intelligence, and physics-informed compact models to enable device–circuit–algorithm co-design under stochastic energy inputs.