Faculty of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul, 143-747, Republic of Korea.
Laboratory of Big-data applications for public sector, Chung-Ang University, 221, Heukseok-dong, Dongjak-gu, Seoul, 156-756, Republic of Korea.
Sci Rep. 2017 Sep 11;7(1):11061. doi: 10.1038/s41598-017-11663-6.
Complicated structures consisting of multi-layers with a multi-modal array of device components, i.e., so-called patterned multi-layers, and their corresponding circuit designs for signal readout and addressing are used to achieve a macroscale electronic skin (e-skin). In contrast to this common approach, we realized an extremely simple macroscale e-skin only by employing a single-layered piezoresistive MWCNT-PDMS composite film with neither nano-, micro-, nor macro-patterns. It is the deep machine learning that made it possible to let such a simple bulky material play the role of a smart sensory device. A deep neural network (DNN) enabled us to process electrical resistance change induced by applied pressure and thereby to instantaneously evaluate the pressure level and the exact position under pressure. The great potential of this revolutionary concept for the attainment of pressure-distribution sensing on a macroscale area could expand its use to not only e-skin applications but to other high-end applications such as touch panels, portable flexible keyboard, sign language interpreting globes, safety diagnosis of social infrastructures, and the diagnosis of motility and peristalsis disorders in the gastrointestinal tract.
采用由多层组成的复杂结构，并具有多模式的器件组件阵列，即所谓的图案化多层结构，以及相应的信号读出和寻址电路设计，来实现宏观电子皮肤 (e-skin)。与这种常见方法不同，我们仅使用单层压阻式 MWCNT-PDMS 复合薄膜，既没有纳米、微或宏观图案，就实现了极其简单的宏观 e-skin。正是深度学习使得这种简单的大块材料能够发挥智能传感设备的作用。深度神经网络 (DNN) 使我们能够处理由施加压力引起的电阻变化，从而即时评估压力水平和受压下的精确位置。这一革命性概念在宏观区域实现压力分布感应的巨大潜力，可以将其应用不仅扩展到电子皮肤应用，还扩展到其他高端应用，如触摸面板、便携式柔性键盘、手语翻译球、社会基础设施安全诊断以及胃肠道运动和蠕动障碍的诊断。
