Bendable Electronics and Sensing Technologies (BEST) group, James Watt School of Engineering, University of Glasgow, G12 8QQ Glasgow, UK.
Sci Robot. 2022 Jun;7(67):eabl7286. doi: 10.1126/scirobotics.abl7286. Epub 2022 Jun 1.
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.
下一代机器人的电子皮肤（e-skin）有望具有类似生物皮肤的多模态传感、信号编码和预处理。为此，必须有高质量、均匀响应的电子设备分布在大面积上，并能够具有长短期记忆的突触行为。在这里，我们提出了一种使用在柔性基底上打印的 ZnO 纳米线实现突触晶体管（12×14 阵列）的方法，具有 100%的产率和高度的均匀性。所提出的器件在脉冲刺激下表现出突触行为，表现出兴奋性（抑制性）后突触电流、尖峰率依赖性可塑性以及短期到长期记忆转变。所实现的晶体管表现出优异的类生物突触行为，并显示出在硬件学习中的巨大潜力。这是通过一个由事件驱动传感器、突触晶体管和尖峰神经元组成的原型计算电子皮肤来证明的，它为机器手赋予了类似生物皮肤的触觉感知。通过联想学习，所提出的计算电子皮肤可以逐渐获得类似人体的疼痛反射。通过实践可以增强所学到的行为。这种类似周围神经系统的局部学习可以大大减少数据延迟，并降低机器人平台的认知负担。
