An extremely simple macroscale electronic skin realized by deep machine learning

TLDR

Macroscale electronic skins are typically built with complex multi‑layer patterned structures and elaborate circuit designs. The study demonstrates a macroscale e‑skin constructed from a single‑layer piezoresistive MWCNT‑PDMS film without any patterning. A deep neural network interprets resistance changes in the composite film, enabling real‑time pressure level and position estimation. The approach enables macroscale pressure‑distribution sensing, opening possibilities for e‑skin, touch panels, flexible keyboards, sign‑language globes, infrastructure safety diagnostics, and gastrointestinal motility assessment.

Abstract

Abstract 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.