Synaptic transistor ‘learns’ while it computes
1 min read
Taking inspiration from the human brain, materials scientists at the Harvard School of Engineering and Applied Sciences (SEAS) have created a new type of transistor which self optimises its properties for the functions it has carried out in the past.
The researchers believe the device could mark the beginning of a new kind of artificial intelligence, and even take parallel computing into a 'new era'.
"The transistor we've demonstrated is really an analogue to the synapse in our brains," said Jian Shi, a postdoctoral fellow at SEAS. "Each time a neuron initiates an action and another neuron reacts, the synapse between them increases the strength of its connection. And the faster the neurons spike each time, the stronger the synaptic connection. Essentially, it memorises the action between the neurons."
Structurally, the synaptic transistor consists of a nickelate semiconductor sandwiched between two platinum electrodes.
Adjacent to this is a small pocket of ionic liquid. An external circuit multiplexer converts the time delay into a magnitude of voltage which it applies to the ionic liquid, creating an electric field that either drives ions into the nickelate or removes them.
The entire device, just a few hundred microns long, is embedded in a silicon chip.
The researchers claim the synaptic transistor offers several advantages over traditional silicon transistors.
Firstly, it is not restricted to the binary system of ones and zeros. "This system changes its conductance in an analogue way, continuously, as the composition of the material changes," explained Shi. "It would be rather challenging to use cmos to imitate a synapse, because real biological synapses have a practically unlimited number of possible states - not just 'on' or 'off.'"
The device is also extremely energy efficient. The nickelate belongs to an unusual class of materials, called correlated electron systems, which can undergo an insulator-metal transition.
At a certain temperature - or, in this case, when exposed to an external field - the conductance of the material suddenly changes.
"A very small excitation allows you to get a large signal, so the input energy required to drive this switching is potentially very small," noted principal investigator Shriram Ramanathan. "That could translate into a large boost for energy efficiency."
The team's findings have been published in the journal Nature Communications.
