The researchers published their findings in the journal Nature Communications, in a paper titled “Protonic solid-state electrochemical synapse for physical neural networks”.
Neural networks attempt to simulate the way learning takes place in the brain, which is based on the gradual strengthening or weakening of the connections between neurons, known as synapses. The core component of this physical neural network is the resistive switch, whose electronic conductance can be controlled electrically. This control, or modulation, emulates the strengthening and weakening of synapses in the brain. In neural networks using conventional silicon microchip technology, the simulation of these synapses is a very energy-intensive process. Most candidate analog resistive devices so far for such simulated synapses have either been very inefficient, in terms of energy use, or performed inconsistently from one device to another or one cycle to the next.
The new system, the researchers say, overcomes both of these challenges.
“We’re addressing not only the energy challenge, but also the repeatability-related challenge that is pervasive in some of the existing concepts out there,” says Bilge Yildiz, a professor of nuclear science and engineering and of materials science and engineering.
The resistive switch in this work is an electrochemical device, made of tungsten trioxide (WO3) and works in a way similar to the charging and discharging of batteries. Ions, in this case protons, can migrate into or out of the crystalline lattice of the material, explains Yildiz, depending on the polarity and strength of an applied voltage. These changes remain in place until altered by a reverse applied voltage — just as the strengthening or weakening of synapses does.