Self-Powered Neuromorphic Touch Sensors Based on Triboelectric Devices: Current Approaches and Open Challenges

Advanced neuromorphic systems mimicking the human sensory and nervous system will enable artificial perception for intelligent robotics and human machine interfaces. Among sensing modalities, tactile perception is crucial for replicating human somatosensory and motor functions, with significant potential to restore impaired tactile capabilities. Artificial neuromorphic sensors can directly sense, store and process various stimuli information and implement computation functions such as perception, learning, and memory. However, computational energy efficiency must be achieved with novel neuromorphic systems capable of environmental energy harvesting enabling self-powered sensing, and real-time edge data processing. Here, we focus on the integration of tactile self-powered sensors based on triboelectric nanogenerators (TENGs) with neuromorphic devices. We systematically discuss current approaches for coupling TENGs with artificial synapses and neurons, covering the main integration architectures (ex situ, discrete circuit, direct gating, monolithic), the primary operational modes (displacement-driven, pulse-driven), and neuromorphic functions as short- and long-term plasticity, memory, and logic-in-memory computing. We also highlight the mechanisms of signal generation and transduction, and the strategies used to enhance performance and energy efficiency. The review concludes with a discussion on key challenges and future directions for developing sustainable, low-power, and multifunctional neuromorphic tactile systems, paving the way toward fully integrated self-powered artificial somatosensory platforms.