Scientists have taken inspiration from this remarkable organ to create chips that could cut conventional energy use by 70%.

Researchers at the University of Cambridge have developed a new brain-inspired nanoscale device that they say could dramatically reduce the enormous energy demands of artificial intelligence hardware. The team created an ultra-low-power "memristor": a device that can both store and process information in the same location, much like synapses in the human brain.

In conventional computing architectures, memory and processing units are physically separated, requiring data to shuttle back and forth between these units for every task. This seemingly simple process consumes enormous amounts of electricity and is a significant contributor to AI's exploding power demands.

Researchers have increasingly looked toward neuromorphic computing as a possible solution. Instead of mimicking the architecture of traditional computers, neuromorphic systems aim to emulate how biological brains operate. In the human brain, neurons and synapses simultaneously store and process information through dense networks of electrical and chemical signaling. This architecture is extraordinarily energy efficient.

At the center of many neuromorphic computing concepts is a component known as a memristor. Unlike conventional transistors, memristors can retain memory states even when power is removed. They also behave somewhat like artificial synapses whose connection strengths can change over time.

However, existing memristors come with major limitations. Most oxide-based memristors operate by forming and rupturing tiny conductive filaments within the material. These microscopic conductive pathways form somewhat randomly, making the devices unpredictable from one switching cycle to another. They also typically require relatively high voltages and consume more power than researchers would like for truly energy-efficient AI hardware.

In their study, published in the journal Science Advances, the Cambridge team took a completely different approach.

Instead of relying on conductive filaments, the researchers engineered a hafnium-oxide-based material that switches states through controlled changes at an internal electronic interface. By adding strontium and titanium into hafnium oxide and fabricating the material using a two-stage growth process, the team created what are effectively microscopic p-n junctions inside the device. These are the same kinds of electronic junctions used throughout conventional semiconductor electronics.

Rather than forming and destroying conductive pathways, the device changes its electrical resistance by modifying the height of an energy barrier at this internal junction. This allows for much smoother and more controllable switching behavior. According to the researchers, this solves one of the biggest problems in memristor technology: variability.

"Filamentary devices suffer from random behavior," says lead author Dr. Babak Bakhit. "But because our devices switch at the interface, they show outstanding uniformity from cycle to cycle and from device to device."