Bio-inspired artificial mechanoreceptors with built-in synaptic functions for intelligent tactile skin

Abstract

Tactile perception involves the preprocessing of signals from slowly adapting and fast-adapting afferent neurons, which exhibit synapse-like interactions between mechanoreceptors and their dendrites or terminals, transmitting signals to the brain. Emulating these adaptation and sensory memory functions is crucial for artificial tactile sensing systems. Here, inspired by human tactile afferent systems, we present an array of artificial synaptic mechanoreceptors with built-in synaptic functions by vertically integrating synaptic transistors with a reduced graphene oxide channel, an ionogel gate dielectric and an elastomeric fingerprint-like receptive layer in an all-in-one platform. Triboelectric-capacitive gating between the receptive layer and gate dielectric in response to tactile stimulation governs excitatory post-synaptic current patterns, enabling slowly adapting and fast-adapting characteristics for signal preprocessing. The artificial synaptic mechanoreceptor array demonstrated handwriting style, surface pattern and texture discrimination via machine learning using fused slowly adapting and fast-adapting post-synaptic values, offering high data efficiency and potential for intelligent skin.