Computer chip material inspired by the human brain could slash AI energy use

Summary

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. The results are reported in the journal Science Advances. 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,” said lead author Dr Babak Bakhit, from Cambridge’s Department of Materials Science and Metallurgy. “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.” 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 growi...