Autonomous vehicles are a potential application for the technology, which could improve security and redundancy.
Engineers at MIT in Massachusetts developed the ‘brain’, 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 reportedly crisper and cleaner compared with existing memristor designs made with unalloyed elements.
A research announcement said the work demonstrated 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 AI systems,” said Jeehwan Kim, associate professor of mechanical engineering at MIT. “Imagine connecting a neuromorphic device to a camera on your car, and having it recognise 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.”
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.
The signal a memristor 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, the team envisions memristors requiring 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.
Kim and his colleagues tackled reliability issues with subtle signals 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,” said Kim.
The team landed on copper as the ideal alloying element, as it is able to bind with both silver and silicon. “It acts as a sort of bridge, and stabilises the silver-silicon interface,” he said.
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 small 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 1mm2 silicon chip with tens of thousands of memristors.
The design reportedly reproduced and even altered images more reliably than existing memristor designs.
“We're using artificial synapses to do real inference tests,” said Kim. “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.”
The research was published in Nature Nanotechnology.
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