Human brain-inspired computer memory design set to increase energy efficiency and performance

University of Cambridge researcher's innovative 'resistive switching memory' has the potential to reshape the landscape of computing.
Abdul-Rahman Oladimeji Bello


Our increasing energy demands have become a pressing concern in a world driven by data. Predictions suggest that internet and communications technologies could consume nearly a third of global electricity within the next decade.

However, researchers at the University of Cambridge have recently made a discovery that could revolutionize computer memory, addressing energy efficiency and performance.

Their innovative design, inspired by the human brain's synapses, has the potential to reshape the landscape of computing.

Dr. Markus Hellenbrand, the first author of the study conducted at Cambridge's Department of Materials Science and Metallurgy, explained the limitations of current computer memory technologies.

He pointed out the energy and time wasted in shuttling data back and forth between separate memory and processing units. This traditional setup has fueled the explosive growth in energy demands, hindering our efforts to reduce carbon emissions

To overcome this challenge, the researchers turned to a promising new technology called resistive switching memory. Unlike conventional memory devices, which can only represent two states (one or zero), resistive switching memory devices have the potential to represent a continuous range of states. Imagine the possibilities: computer memory with greater density, higher performance, and significantly lower energy consumption.

Dr. Hellenbrand further elaborated, using a typical USB stick as an example. He explained that a resistive switching memory-based USB stick could hold between ten and 100 times more information than current technology. The potential benefits are immense, given our data-hungry society and the exponential growth of artificial intelligence, algorithms, and internet usage.

The researchers focused on hafnium oxide, an insulating material commonly used in the semiconductor industry. However, there was one significant obstacle to overcome: hafnium oxide lacks structure at the atomic level, making it unsuitable for memory applications. But the team found an ingenious solution by introducing barium into thin films of hafnium oxide, resulting in the formation of unique structures within the composite material.

These novel structures, known as vertical barium-rich "bridges," allowed electrons to pass through while the surrounding hafnium oxide remained unstructured. At the points where these bridges met the device contacts, an adjustable energy barrier was created. This barrier influenced the electrical resistance of the composite material and enabled multiple states to exist within it.

Breakthrough of Hafnium oxide

Human brain-inspired computer memory design set to increase energy efficiency and performance
Engineer with super computer

One remarkable aspect of this breakthrough is that the hafnium oxide composites are self-assembled at low temperatures, unlike other composite materials that require expensive high-temperature manufacturing methods.

Not only did the composite material exhibit exceptional performance and uniformity, but it also demonstrated the potential to emulate the functioning of synapses in the human brain.

Dr. Hellenbrand expressed his excitement, stating that these materials could store and process information in the same place, just like our brains do. This feature makes them incredibly promising for the rapidly growing fields of artificial intelligence and machine learning.

The research team has filed a patent on this technology through Cambridge Enterprise, the university's commercialization arm. They are now collaborating with industry partners to conduct larger feasibility studies and gain a deeper understanding of how these high-performance structures form. Since hafnium oxide is already used in the semiconductor industry, integrating it into existing manufacturing processes should not pose significant challenges.

The implications of this research are far-reaching, offering a glimmer of hope in our quest for more energy-efficient and high-performance computing. This breakthrough could help curb the ever-increasing energy demands of our data-driven world by addressing the shortcomings of current computer memory technologies.

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