MIT engineers have designed a “brain-on-a-chip,” smaller than a piece of
confetti, that is made from tens of thousands of artificial brain synapses
known as memristors — silicon-based components that mimic the
information-transmitting synapses in the human brain.
The researchers
borrowed from principles of metallurgy to fabricate each memristor from alloys
of silver and copper, along with silicon. When they ran the chip through
several visual tasks, the chip was able to “remember” stored images and
reproduce them many times over, in versions that were crisper and cleaner
compared with existing memristor designs made with unalloyed elements.
Their results, published today in the journal Nature Nanotechnology, demonstrate
a promising new memristor design for neuromorphic devices — electronics that
are based on a new type of circuit that processes information in a way that
mimics the brain’s neural architecture. Such brain-inspired circuits could be
built into small, portable devices, and would carry out complex computational
tasks that only today’s supercomputers can handle.
“So far,
artificial synapse networks exist as software. We’re trying to build real
neural network hardware for portable artificial intelligence systems,” says
Jeehwan Kim, associate professor of mechanical engineering at MIT. “Imagine
connecting a neuromorphic device to a camera on your car, and having it
recognize lights and objects and make a decision immediately, without having to
connect to the internet. We hope to use energy-efficient memristors to do those
tasks on-site, in real-time.”
Wandering ions
Memristors, or
memory transistors, are an essential element in neuromorphic computing. In a
neuromorphic device, a memristor would serve as the transistor in a circuit,
though its workings would more closely resemble a brain synapse — the junction
between two neurons. The synapse receives signals from one neuron, in the form
of ions, and sends a corresponding signal to the next neuron.
A transistor in
a conventional circuit transmits information by switching between one of only
two values, 0 and 1, and doing so only when the signal it receives, in the form
of an electric current, is of a particular strength. In contrast, a memristor
would work along a gradient, much like a synapse in the brain. The signal it
produces would vary depending on the strength of the signal that it receives.
This would enable a single memristor to have many values, and therefore carry
out a far wider range of operations than binary transistors.
Like a brain
synapse, a memristor would also be able to “remember” the value associated with
a given current strength, and produce the exact same signal the next time it
receives a similar current. This could ensure that the answer to a complex
equation, or the visual classification of an object, is reliable — a feat that
normally involves multiple transistors and capacitors.
Ultimately,
scientists envision that memristors would require far less chip real estate
than conventional transistors, enabling powerful, portable computing devices
that do not rely on supercomputers, or even connections to the Internet.
Existing
memristor designs, however, are limited in their performance. A single
memristor is made of a positive and negative electrode, separated by a
“switching medium,” or space between the electrodes. When a voltage is applied
to one electrode, ions from that electrode flow through the medium, forming a
“conduction channel” to the other electrode. The received ions make up the
electrical signal that the memristor transmits through the circuit. The size of
the ion channel (and the signal that the memristor ultimately produces) should
be proportional to the strength of the stimulating voltage.
Kim says that
existing memristor designs work pretty well in cases where voltage stimulates a
large conduction channel, or a heavy flow of ions from one electrode to the
other. But these designs are less reliable when memristors need to generate
subtler signals, via thinner conduction channels.
The thinner a
conduction channel, and the lighter the flow of ions from one electrode to the
other, the harder it is for individual ions to stay together. Instead, they
tend to wander from the group, disbanding within the medium. As a result, it’s
difficult for the receiving electrode to reliably capture the same number of
ions, and therefore transmit the same signal, when stimulated with a certain
low range of current.
Borrowing from metallurgy
Kim and his
colleagues found a way around this limitation by borrowing a technique from
metallurgy, the science of melding metals into alloys and studying their
combined properties.
“Traditionally,
metallurgists try to add different atoms into a bulk matrix to strengthen
materials, and we thought, why not tweak the atomic interactions in our
memristor, and add some alloying element to control the movement of ions in our
medium,” Kim says.
Engineers
typically use silver as the material for a memristor’s positive electrode.
Kim’s team looked through the literature to find an element that they could
combine with silver to effectively hold silver ions together, while allowing
them to flow quickly through to the other electrode.
The team landed
on copper as the ideal alloying element, as it is able to bind both with
silver, and with silicon.
“It acts as a
sort of bridge, and stabilizes the silver-silicon interface,” Kim says.
To make
memristors using their new alloy, the group first fabricated a negative
electrode out of silicon, then made a positive electrode by depositing a slight
amount of copper, followed by a layer of silver. They sandwiched the two
electrodes around an amorphous silicon medium. In this way, they patterned a
millimeter-square silicon chip with tens of thousands of memristors.
As a first test
of the chip, they recreated a gray-scale image of the Captain America shield.
They equated each pixel in the image to a corresponding memristor in the chip.
They then modulated the conductance of each memristor that was relative in
strength to the color in the corresponding pixel.
The chip
produced the same crisp image of the shield, and was able to “remember” the
image and reproduce it many times, compared with chips made of other materials.
The team also
ran the chip through an image processing task, programming the memristors to
alter an image, in this case of MIT’s Killian Court, in several specific ways,
including sharpening and blurring the original image. Again, their design
produced the reprogrammed images more reliably than existing memristor designs.
“We’re using
artificial synapses to do real inference tests,” Kim says. “We would like to
develop this technology further to have larger-scale arrays to do image
recognition tasks. And some day, you might be able to carry around artificial
brains to do these kinds of tasks, without connecting to supercomputers, the
internet, or the cloud.”
Journal article: https://www.nature.com/articles/s41565-020-0694-5
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