Researchers have developed a new kind of nanoelectronic device that could dramatically cut the energy consumed by artificial intelligence hardware by mimicking the human brain.
The researchers, led by the University of Cambridge, developed a form of hafnium oxide that acts as a highly stable, low‑energy ‘memristor’ — a component designed to mimic the efficient way neurons are connected in the brain. Current AI systems rely on conventional computer chips that shuttle data back and forth between memory and processing units. This constant movement consumes large amounts of electricity, and global demand is exploding as AI adoption expands across industries.
Brain-inspired, or neuromorphic, computing is an alternative way to process information that could reduce energy use by as much as 70% by storing and processing information in the same place, and doing so with extremely low power. Such a system would also be far more adaptable, in the same way our own brains are able to learn and adapt.
“Energy consumption is one of the key challenges in current AI hardware. To address that, you need devices with extremely low currents, excellent stability, outstanding uniformity across switching cycles and devices, and the ability to switch between many distinct states,”Dr Babak Bakhit, Dept of Materials Science and Metallurgy, University of Cambridge
Most existing memristors rely on the formation of tiny conductive filaments inside metal oxide material. But these filaments behave unpredictably and typically require high forming and operating voltages, limiting their usefulness in large-scale data storage and computing systems.
The Cambridge team instead created a new type of hafnium-based thin film that switches states in a completely different way. By adding strontium and titanium and growing the film using a two‑step method, the researchers were able to form tiny electronic gates, or ‘p-n junctions’, inside the oxide where the layers meet. This allows the device to change its resistance smoothly by shifting the height of an energy barrier at the interface, rather than by growing or rupturing the filaments.
Using the hafnium-based devices, the researchers achieved switching currents about a million times lower than those of some conventional oxide-based devices. The memristors also produced hundreds of distinct, stable conductance levels, a key requirement for analogue ‘in-memory’ computing.
Bakhit, a materials physicist, said the breakthrough followed several years of unsuccessful experiments. The turning point came late last year when he tried a twist on the two‑stage deposition method, adding oxygen only after the first layer had been grown.
“I spent almost three years on this. There were a huge number of failures. But at the end of November, we saw the first really good results. It’s still early days of course, but if we can solve the temperature issue, this technology could be game-changing because the energy consumption is so much lower and at the same time, the device performance is highly promising,” Dr Babak Bakhit, Dept of Materials Science and Metallurgy, University of Cambridge
Babak Bakhit et al. ‘HfO2-based memristive synapses with asymmetrically extended p-n heterointerfaces for highly energy-efficient neuromorphic hardware.’ Science Advances (2026). DOI: 10.1126/sciadv.aec2324
University of Cambridge article
Credit: Dr Babak Bakhit