Artificial 'neurotransistor' created
Imitating the functioning of neurons using semiconductor materials

Date:
July 14, 2020
Source:
Helmholtz-Zentrum Dresden-Rossendorf
Summary:
While the optimization of conventional microelectronics is slowly
reaching its physical limits, nature offers us a blueprint how
information can be processed and stored efficiently: our own brain.

Scientists have now successfully imitated the functioning of
neurons using semiconductor materials.



FULL STORY ========================================================================== Especially activities in the field of artificial intelligence,
like teaching robots to walk or precise automatic image recognition,
demand ever more powerful, yet at the same time more economical computer
chips. While the optimization of conventional microelectronics is slowly reaching its physical limits, nature offers us a blueprint how information
can be processed and stored quickly and efficiently: our own brain. For
the very first time, scientists at TU Dresden and the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) have now successfully imitated the functioning
of brain neurons using semiconductor materials. They have published
their research results in the journal Nature Electronics.


========================================================================== Today, enhancing the performance of microelectronics is usually achieved
by reducing component size, especially of the individual transistors
on the silicon computer chips. "But that can't go on indefinitely --
we need new approaches," Larysa Baraban asserts. The physicist, who
has been working at HZDR since the beginning of the year, is one of the
three primary authors of the international study, which involved a total
of six institutes. One approach is 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 continues. "So, we 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 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.

Silicon wafer + polymer = chip capable of learning Modeling computers
on the human brain is no new idea. Scientists made attempts to hook
up nerve cells to electronics in Petri dishes decades ago. "But 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 brains behind the neurotransistor alongside Ronald Tetzlaff, Professor of Fundamentals of Electrical Engineering in Dresden, and Leon Chua from the University of California at Berkeley, who had already postulated similar components
in the early 1970s.

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," the materials science professor 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." As
Cuniberti explains, 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." Cuniberti and his team are not
focused on conventional issues, though.

"Computers based on our chip would be less precise and tend to estimate mathematical computations rather than calculating them down to the last decimal," the scientist explains. "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 recognize 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, thus, solve problems for which they were
not originally programmed.


========================================================================== Story Source: Materials provided by
Helmholtz-Zentrum_Dresden-Rossendorf. Note: Content may be edited for
style and length.


========================================================================== Journal Reference:
1. Eunhye Baek, Nikhil Ranjan Das, Carlo Vittorio Cannistraci,
Taiuk Rim,
Gilbert Santiago Can~o'n Bermu'dez, Khrystyna Nych, Hyeonsu Cho,
Kihyun Kim, Chang-Ki Baek, Denys Makarov, Ronald Tetzlaff, Leon
Chua, Larysa Baraban, Gianaurelio Cuniberti. Intrinsic plasticity
of silicon nanowire neurotransistors for dynamic memory and learning
functions. Nature Electronics, 2020; DOI: 10.1038/s41928-020-0412-1 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/07/200714101230.htm

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