Researchers created a new design for computer memory that could improve performance while also lowering the energy demands of internet and communications technologies, which are expected to consume nearly a third of global electricity in the next ten years.
The study was published in the journal, ‘Science Advances.’
The University of Cambridge-led team created a device that processes data in the same way that synapses in the human brain do. The devices are made of hafnium oxide, a material that is already used in the semiconductor industry, and tiny self-assembled barriers that can be raised and lowered to allow electrons to pass through.
This method of altering the electrical resistance in computer memory devices and allowing information processing and memory to coexist could lead to the development of computer memory devices with significantly higher density, higher performance, and lower energy consumption. The findings were published in the journal Science Advances.
Our data-hungry world has led to a ballooning of energy demands, making it ever more difficult to reduce carbon emissions. Within the next few years, artificial intelligence, internet usage, algorithms and other data-driven technologies are expected to consume more than 30 per cent of global electricity.
“To a large extent, this explosion in energy demands is due to shortcomings of current computer memory technologies,” said first author Dr Markus Hellenbrand, from Cambridge’s Department of Materials Science and Metallurgy. “In conventional computing, there’s memory on one side and processing on the other, and data is shuffled back between the two, which takes both energy and time.”
One potential solution to the problem of inefficient computer memory is a new type of technology known as resistive switching memory. Conventional memory devices are capable of two states: one or zero. A functioning resistive switching memory device, however, would be capable of a continuous range of states – computer memory devices based on this principle would be capable of far greater density and speed.
“A typical USB stick based on the continuous range would be able to hold between ten and 100 times more information, for example,” said Hellenbrand.
Hellenbrand and his colleagues developed a prototype device based on hafnium oxide, an insulating material that is already used in the semiconductor industry. The issue with using this material for resistive switching memory applications is known as the uniformity problem. At the atomic level, hafnium oxide has no structure, with the hafnium and oxygen atoms randomly mixed, making it challenging to use for memory applications.
However, the researchers found that by adding barium to thin films of hafnium oxide, some unusual structures started to form, perpendicular to the hafnium oxide plane, in the composite material.
These vertical barium-rich ‘bridges’ are highly structured, and allow electrons to pass through, while the surrounding hafnium oxide remains unstructured. At the point where these bridges meet the device contacts, an energy barrier was created, which electrons can cross. The researchers were able to control the height of this barrier, which in turn changes the electrical resistance of the composite material.
“This allows multiple states to exist in the material, unlike conventional memory which has only two states,” said Hellenbrand.
Unlike other composite materials, which require expensive high-temperature manufacturing methods, these hafnium oxide composites self-assemble at low temperatures. The composite material showed high levels of performance and uniformity, making them highly promising for next-generation memory applications.
A patent on the technology has been filed by Cambridge Enterprise, the University’s commercialisation arm.
“What’s really exciting about these materials is they can work like a synapse in the brain: they can store and process information in the same place, like our brains can, making them highly promising for the rapidly growing AI and machine learning fields,” said Hellenbrand.
(with inputs from ANI)
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