At present, enhancing the performance of microelectronics is usually achieved by reducing component size, especially of the individual transistors on silicon chips.
According to Larysa Baraban, one of the three primary authors of the international study, which has involved a total of six institutes, "This can't go on indefinitely – so we need new approaches."
To that end the study has looked at an approach based on the brain, combining data processing with data storage in an artificial neuron.
“Our group has extensive experience with biological and chemical electronic sensors,” Baraban said. “So, we have simulated the properties of neurons using the principles of biosensors and modified a classical field-effect transistor to create an artificial neurotransistor.”
The advantage of such an architecture, according to Baraban, lies in the simultaneous storage and processing of information in a single component. In conventional transistor technology, they are separated, which slows processing time and hence ultimately also limits performance.
Modelling computers on the human brain is not new and scientists have made numerous attempts to hook up nerve cells to electronics in Petri dishes.
“A wet computer chip that has to be fed all the time is of no use to anybody,” says Gianaurelio Cuniberti from TU Dresden. The Professor for Materials Science and Nanotechnology is one of the three scientists behind the neurotransistor alongside Ronald Tetzlaff, Professor of Fundamentals of Electrical Engineering in Dresden, and Leon Chua from the University of California at Berkeley.
Now, Cuniberti, Baraban and their team have been able to implement it:
“We apply a viscous substance – called solgel – to a conventional silicon wafer with circuits. This polymer hardens and becomes a porous ceramic,” Cuniberti explains. “Ions move between the holes. They are heavier than electrons and slower to return to their position after excitation. This delay, called hysteresis, is what causes the storage effect.” According to Cuniberti, this is a decisive factor in the functioning of the transistor. “The more an individual transistor is excited, the sooner it will open and let the current flow. This strengthens the connection. The system is learning.”
According to Cuniberti, “Computers based on our chip would be less precise and tend to estimate mathematical computations rather than calculating them down to the last decimal. But they would be more intelligent. For example, a robot with such processors would learn to walk or grasp; it would possess an optical system and learn to recognise connections. And all this without having to develop any software.”
But these are not the only advantages of neuromorphic computers. Thanks to their plasticity, which is similar to that of the human brain, they can adapt to changing tasks during operation and are able to solve problems for which they were not originally programmed.
