A team of engineers from the University of Glasgow led by Professor Ravinder Dahiya developed an artificial skin with a new type of processing system based on “synaptic transistors,” which mimics the brain's neural pathways to learn how to react to external stimuli.

Tech Briefs: What got you started on this research?

Professor Ravinder Dahiya: Touch sensing is unique in that it uses sensors distributed all over our skin. Using skin-type sensing on a robot is therefore very challenging. Current technology is not yet ready for such complex systems. At present, electronics is mainly realized on planar substrates, which are not soft and flexible.

Tech Briefs: You refer in your work to “synaptic transistors.” Can you explain what that means?

Dahiya: In our bodies, a synapse receives inputs from various sensors and the synapse performs a summation of these inputs. The synapse is the point where, in the case of nerves, our body learns over a period of time. For example, if I touch a rough surface very frequently, my body learns to differentiate between a rough and a smooth surface. We have tried to bring similar functions to our transistors. We call them synaptic transistors because they're able to learn touch-based stimuli.

Tech Briefs: Could you briefly explain how the touch sensing works?

Dahiya: We printed a grid of 168 synaptic transistors made from zinc-oxide nanowires directly onto a flexible plastic surface. Then, we connected the synaptic transistor with the skin sensor present over the palm of a fully articulated, human-shaped robot hand.

When the sensor is touched, it registers a change in its electrical resistance — a small change corresponds to a light touch, and a large change in resistance corresponds to a harder touch. The change in resistance of the touch sensor is followed by a circuit that converts the change in resistance into voltage spikes whose frequency varies according to the amplitude of the resistance change. The spikes are applied to the gate of the synaptic transistor. If the frequency of the pulses changes, the transistor's conductance changes, which is how the transistor learns.

Tech Briefs: Did you start out purposely using the body as an inspiration for how to do this?

Dahiya: Yes, the human skin was an inspiration. It's well proven and well tested — it works.

Tech Briefs: Do you have any thoughts about other applications?

Dahiya: Mobile health is one application, where each person could have a smart wearable device that can report health parameters to one central unit. If the device worn by each person does some local computation so it sends more information, but less unprocessed data to the central unit, it will be more efficient.

An edited version of this interview appeared in the August 2022 issue of Tech Briefs.