The HfO2-based memristors measure 40nm2 and are said to exhibit properties similar to biological synapses. Using newly developed technology, the memristors were integrated in matrices: in the future, the group says this technology may be used to design computers that function similarly to biological neural networks. Also, as HfO2 is used in the production of modern processors, the technology should be easy to integrate into industrial processes.
"In a simpler version, memristors are promising binary non-volatile memory cells, in which information is written by switching the electric resistance - from high to low and back again,” explained Yury Matveyev, senior researcher of MIPT's Laboratory of Functional Materials and Devices for Nanoelectronics. “What we are trying to demonstrate are much more complex functions of memristors - that they behave similar to biological synapses."
A synapse is a point of connection between neurons, the main function of which is to transmit a signal from one neuron to another. Each neuron may have thousands of synapses, to connect with a number of other neurons. This means that information can be processed in parallel, rather than sequentially and is the reason why ‘living’ neural networks are so effective both in terms of speed and energy consumption.
There are a number of physical effects that can be exploited to design memristors. Most often, these devices use only two different states encoding logic zero and one. However, in order to simulate biological synapses, a continuous spectrum of conductivities had to be used in the devices.
The researchers also demonstrated a more complex mechanism - spike-timing-dependent plasticity, or the dependence of the value of the connection between neurons on the relative time taken for them to be ‘triggered’. It had previously been shown that this mechanism is responsible for associative learning.
To demonstrate this function in their memristor devices, the researchers used an electric signal which reproduced, as far as possible, the signals in living neurons, and they obtained a dependency similar to those observed in living synapses.
Andrey Zenkevich, head of MIPT's Laboratory of Functional Materials and Devices for Nanoelectronics, said: "Thanks to this research, we are now one step closer to building an artificial neural network. It may only be the very simplest of networks, but it is nevertheless a hardware prototype which could be used as a basis for the hardware implementation of artificial neural networks."
