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.

中文翻译：

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印刷的基于突触晶体管的电子皮肤，供机器人感受和学习

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、信号编码和预处理功能。为此，必须有高质量、一致响应的电子设备分布在大面积区域，并能够提供具有长期和短期记忆的突触行为。在这里，我们提出了一种使用印刷在柔性基板上的 ZnO 纳米线来实现突触晶体管（12×14 阵列）的方法，该方法具有 100% 的良率和高均匀性。所呈现的设备在脉冲刺激下显示出突触行为，表现出兴奋性（抑制性）突触后电流、尖峰率依赖性可塑性以及短期到长期记忆的转变。实现的晶体管表现出出色的类生物突触行为，并显示出硬件学习的巨大潜力。这是通过一个原型计算电子皮肤来证明的，该原型包括事件驱动的传感器、突触晶体管和尖峰神经元，这些神经元赋予机器人手类似生物皮肤的触觉感觉。通过联想学习，所呈现的计算电子皮肤可以逐渐获得类似人体的疼痛反射。习得的行为可以通过练习得到加强。这种类似于周围神经系统的局部学习可以大大减少数据延迟并减少机器人平台上的认知负荷。所呈现的计算电子皮肤可以逐渐获得类似人体的疼痛反射。习得的行为可以通过练习得到加强。这种类似于周围神经系统的局部学习可以大大减少数据延迟并减少机器人平台上的认知负荷。所呈现的计算电子皮肤可以逐渐获得类似人体的疼痛反射。习得的行为可以通过练习得到加强。这种类似于周围神经系统的局部学习可以大大减少数据延迟并减少机器人平台上的认知负荷。