Skin-Inspired Thermoreceptors-Based Electronic Skin for Biomimicking Thermal Pain Reflexes.

Electronic systems possessing skin-like morphology and functionalities (electronic skins [e-skins]) have attracted considerable attention in recent years to provide sensory or haptic feedback in growing areas such as robotics, prosthetics, and interactive systems. However, the main focus thus far has been on the distributed pressure or force sensors. Herein a thermoreceptive e-skin with biological systems like functionality is presented. The soft, distributed, and highly sensitive miniaturized (≈700 µm2 ) artificial thermoreceptors (ATRs) in the e-skin are developed using an innovative fabrication route that involves dielectrophoretic assembly of oriented vanadium pentoxide nanowires at defined locations and high-resolution electrohydrodynamic printing. Inspired from the skin morphology, the ATRs are embedded in a thermally insulating soft nanosilica/epoxy polymeric layer and yet they exhibit excellent thermal sensitivity (-1.1 ± 0.3% °C-1 ), fast response (≈1s), exceptional stability (negligible hysteresis for >5 h operation), and mechanical durability (up to 10 000 bending and twisting loading cycles). Finally, the developed e-skin is integrated on the fingertip of a robotic hand and a biological system like reflex is demonstrated in response to temperature stimuli via localized learning at the hardware level.

Printed synaptic transistor-based electronic skin for robots to feel and learn.

An electronic skin (e-skin) for the next generation of robots is expected to have biological skin-like multimodal sensing, signal encoding, and preprocessing. To this end, it is imperative to have high-quality, uniformly responding electronic devices distributed over large areas and capable of delivering synaptic behavior with long- and short-term memory. Here, we present an approach to realize synaptic transistors (12-by-14 array) using ZnO nanowires printed on flexible substrate with 100% yield and high uniformity. The presented devices show synaptic behavior under pulse stimuli, exhibiting excitatory (inhibitory) post-synaptic current, spiking rate-dependent plasticity, and short-term to long-term memory transition. The as-realized transistors demonstrate excellent bio-like synaptic behavior and show great potential for in-hardware learning. This is demonstrated through a prototype computational e-skin, comprising event-driven sensors, synaptic transistors, and spiking neurons that bestow biological skin-like haptic sensations to a robotic hand. With associative learning, the presented computational e-skin could gradually acquire a human body-like pain reflex. The learnt behavior could be strengthened through practice. Such a peripheral nervous system-like localized learning could substantially reduce the data latency and decrease the cognitive load on the robotic platform.