Printed "Synaptic Transistors" Drive an Electronic Skin Capable of Sensing "Pain"

A team of engineers at the University of Glasgow has developed an electronic skin which is capable of feeling "pain" — using "synaptic transistors" that mimic the neural pathways of the human brain to learn how to respond to stimuli.

"We all learn early on in our lives to respond appropriately to unexpected stimuli like pain in order to prevent us from hurting ourselves again. Of course, the development of this new form of electronic skin didn’t really involve inflicting pain as we know it – it’s simply a shorthand way to explain the process of learning from external stimulus," explains Ravinder Dahiya, professor and leader of the Bendable Electronics and Sensing Technologies (BEST) group at the University.

"What we've been able to create through this process," Dahiya continues, "is an electronic skin capable of distributed learning at the hardware level, which doesn’t need to send messages back and forth to a central processor before taking action. Instead, it greatly accelerates the process of responding to touch by cutting down the amount of computation required. We believe that this is a real step forward in our work towards creating large-scale neuromorphic printed electronic skin capable of responding appropriately to stimuli."

The electronic skin is modeled on the human peripheral nervous system, using a grid of 168 "synaptic transistors" constructed from zinc-oxide nanowires printed onto a flexible plastic substrate to offer pressure sensing capabilities — and to allow a robotic hand to respond to an external stimulus without sending the data to a central processor first, creating something able to recoil in "pain" from a sharp jab to its palm.

"In the future, this research could be the basis for a more advanced electronic skin which enables robots capable of exploring and interacting with the world in new ways," co-author Fengyuan Liu claims, "or building prosthetic limbs which are capable of near-human levels of touch sensitivity."

The team's work has been published in the journal Science Robotics, though the DOI was not yet live at the time of writing.