Science: A Transistor while it computes, learns, developed by Harvard.
WorldWide Tech & Science. Francisco De Jesùs.
Left to right: Shriram Ramanathan, associate professor of materials
science, with Jian Shi and Sieu D. Ha, postdoctoral fellows at Harvard
SEAS. (Photo by Eliza Grinnell, SEAS Communications.)
Schematic of the ionic liquid-gated SmNiO3 synaptic transistor (Photo: Harvard Univ.)
Diagram of the key features of neurons and synapses in the brain (Image: Mariana Ruiz Villarreal)
Comparison of
the structures of a field effect transistor (left) and Harvard's
synaptic transistor (right) (Image: B. Dodson)
It doesn't take a Watson to realize that even the world's best supercomputers are staggeringly inefficient and energy-intensive machines.
Our brains have upwards of
86 billion neurons, connected by synapses that not only complete myriad logic
circuits; they continuously adapt to stimuli, strengthening some connections
while weakening others. We call that process learning, and it enables the kind
of rapid, highly efficient computational processes that put Siri and Blue Gene
to shame.
Materials scientists at the
Harvard School of Engineering and Applied Sciences (SEAS) have now created a new type
of transistor that mimics the behavior of a synapse(In the nervous system, a synapse is a structure that permits a neuron (or nerve cell) to pass an electrical or chemical signal to another cell (neural or otherwise) The novel device
simultaneously modulates the flow of information in a circuit and physically
adapts to changing signals.
Exploiting unusual
properties in modern materials, the synaptic transistor could mark the
beginning of a new kind of artificial intelligence: one embedded not in smart
algorithms but in the very architecture of a computer.
The findings appear in Nature Communications.
“There’s extraordinary
interest in building energy-efficient electronics these days,” says principal
investigator Shriram Ramanathan, associate professor of materials
science at Harvard SEAS. “Historically, people have been focused on speed, but
with speed comes the penalty of power dissipation. With electronics becoming
more and more powerful and ubiquitous, you could have a huge impact by cutting
down the amount of energy they consume.”
The human mind, for all its
phenomenal computing power, runs on roughly 20 Watts of energy (less than a
household light bulb), so it offers a natural model for engineers.
“The transistor we’ve
demonstrated is really an analog to the synapse in our brains,” says co-lead author
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 memorizes the action between
the neurons.”
In principle, a system
integrating millions of tiny synaptic transistors and neuron terminals could
take parallel computing into a new era of ultra-efficient high performance.
Several
prototypes of the synaptic transistor are visible on this silicon chip. (Photo
by Eliza Grinnell, SEAS Communications.)
While calcium ions and
receptors effect a change in a biological synapse, the artificial version
achieves the same plasticity with oxygen ions. When a voltage is applied, these
ions slip in and out of the crystal lattice of a very thin (80-nanometer) film
of samarium nickelate, which acts as the synapse channel between two platinum
"axon" and "dendrite" terminals. The varying concentration
of ions in the nickelate raises or lowers its conductance—that is, its ability
to carry information on an electrical current—and, just as in a natural
synapse, the strength of the connection depends on the time delay in the
electrical signal.
Structurally, the device
consists of the nickelate semiconductor sandwiched between two platinum
electrodes and adjacent to 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 synaptic transistor
offers several immediate advantages over traditional silicon transistors. For a
start, it is not restricted to the binary system of ones and zeros.
“This system changes its
conductance in an analog way, continuously, as the composition of the material
changes,” explains Shi. “It would be rather challenging to use CMOS, the
traditional circuit technology, to imitate a synapse, because real biological
synapses have a practically unlimited number of possible states—not just ‘on’
or ‘off.’”
The synaptic transistor
offers another advantage: non-volatile memory, which means even when power is
interrupted, the device remembers its state.
Additionally, the new
transistor is inherently energy efficient. The nickelate belongs to an unusual
class of materials, called correlated electron systems, that 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.
“We exploit the extreme
sensitivity of this material,” says Ramanathan. “A very small excitation allows
you to get a large signal, so the input energy required to drive this switching
is potentially very small. That could translate into a large boost for energy
efficiency.”
The nickelate system is
also well positioned for seamless integration into existing silicon-based
systems.
“In this paper, we
demonstrate high-temperature operation, but the beauty of this type of a device
is that the 'learning' behavior is more or less temperature insensitive, and
that’s a big advantage,” says Ramanathan. “We can operate this anywhere from
about room temperature up to at least 160 degrees Celsius.”
For now, the limitations
relate to the challenges of synthesizing a relatively unexplored material
system, and to the size of the device, which affects its speed.
“In our proof-of-concept
device, the time constant is really set by our experimental geometry,” says
Ramanathan. “In other words, to really make a super-fast device, all you’d have
to do is confine the liquid and position the gate electrode closer to it.”
In fact, Ramanathan and his
research team are already planning, with microfluidics experts at SEAS, to
investigate the possibilities and limits for this “ultimate fluidic
transistor.”
He also has a seed grant
from the National Academy of Sciences to explore the integration of synaptic
transistors into bioinspired circuits, with L. Mahadevan, Lola England de Valpine Professor of Applied
Mathematics, professor of organismic and evolutionary biology, and professor of
physics.
“In the SEAS setting it’s
very exciting; we’re able to collaborate easily with people from very diverse
interests,” Ramanathan says.
For the materials
scientist, as much curiosity derives from exploring the capabilities of
correlated oxides (like the nickelate used in this study) as from the possible
applications.
“You have to build new
instrumentation to be able to synthesize these new materials, but once you’re
able to do that, you really have a completely new material system whose
properties are virtually unexplored,” Ramanathan says.
“It’s very exciting to
have such materials to work with, where very little is known about them and you
have an opportunity to build knowledge from scratch.”
“This kind of
proof-of-concept demonstration carries that work into the ‘applied’ world,” he
adds, “where you can really translate these exotic electronic properties into
compelling, state-of-the-art devices.”
This research was supported
by the National Science Foundation (NSF), the Army Research Office’s
Multidisciplinary University Research Initiative, and the Air Force Office of
Scientific Research. The team also benefited from the facilities at the Harvard
Center for Nanoscale Systems, a member of the NSF-supported National
Nanotechnology Infrastructure Network. Sieu D. Ha, a postdoctoral fellow at
SEAS, was the co-lead author; additional coauthors included graduate student
You Zhou and Frank Schoofs, a former postdoctoral fellow.
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